In our first blog of our ecosystem orchestration series, we explored why fragmented platforms may be holding your organisation back and how to navigate them through ecosystem orchestration. In this blog, we uncover the signs of website sprawl arising, how it may be affecting your organisation, the hidden costs of these signs and how consolidation can help mitigate them. 

Website estates rarely sprawl and fragment overnight. The gradual accumulation of websites is often the result of growth through business acquisitions or new sites being launched for different departments, products, campaigns or geographical regions. Each new site introduces its own hosting, security requirements, content workflows and maintenance demands.  

Over time, what may once have seemed like manageable expansion becomes a complex web of disconnected platforms, duplicated content, siloed data and rising operational overheads. The actual cost of fragmentation becomes more than technical, negatively affecting your team’s productivity and resulting in disjointed user journeys and a poor overall customer experience with limited ability to personalise and capitalise on AI.  

The impact on both B2B and B2C businesses is profound, with 20-30% of annual revenue lost due to inefficiencies caused by siloed systems and over 25% of customers defecting after just one bad experience. 

When digital expansion happens without a clear long-term governance strategy, a plethora of disconnected sites and technologies that are both difficult and expensive to maintain arise alongside fragmented user journeys and an inconsistent user experience. 

In most organisations, the website estate sits at the centre of the customer journey. When it becomes fragmented, the knock-on effects show up across the wider ecosystem, from how content is managed and how performance is measured to how CRM and customer data are used to enable personalisation. 

So, what are the warning signs that organisations should be on the lookout for when it comes to website sprawl and how might consolidation be the solution to this?

Fragmentation warning signs 

If these signs sound familiar, website sprawl may be taking effect:  

Inconsistent brand experience 

Users expect a seamless journey regardless of where and when they engage with your organisation. When different sites across your estate have different look-and-feels, inconsistent messaging or tone and navigation discrepancies, a lack of trust may arise and lead to reduced engagement.  

Duplicated content and publishing effort 

With every increase in the number of your websites, there is an increased likelihood of content duplication and discrepancies. This ultimately becomes harder to manage and makes the job of updating content across your sites a time-consuming minefield. Without strong governance or systems in place to manage this amount of content debt, conflicting and inaccurate information will continue to snowball and leave both your internal teams and users frustrated. 

Greater risk of security and compliance breaches

The more fragmented the estate, the more security vulnerabilities and increased likelihood of a malicious cyber-attack that devastates your business. This is especially true when it comes to older or forgotten websites that may not be fully patched. Similarly, as regulations tighten on key experience requirements like accessibility and data protection, the risk multiplies. Unless you have the operational bandwidth to monitor and maintain all your websites, you are opening yourself up for sanctions and fines. 

Rising maintenance costs

Each website introduces its own infrastructure requirements, costs and challenges. Managing the maintenance, hosting and support of multiple platforms is time consuming and leads to duplicated efforts.  

Hard-to-govern CMS landscape

If websites are built on different technology platforms, the operational burden grows substantially. Overhead increases when it comes to maintaining and building those sites. Integrations become more difficult and content and design changes require your team to learn multiple tools, workflows and processes.  

Poor data visibility 

Not only does a fragmented estate complicate gaining a unified customer view, but it obfuscates your websites’ analytics performance. Potential earnings are at stake because of the inability to provide users with personalised experiences and your team the ability to identify trends or insights to optimise experiences. 

These signs often indicate that your organisation needs a refreshed ecosystem orchestration and governance strategy to ensure that you can continue to scale and meet the ever-demanding needs of your users. 

The hidden costs for your organisation

The hidden costs of a website sprawl creep up in various places within an organisation. The operational drag of publishing and maintenance overhead can be felt by teams, while users grapple with inconsistent journeys that impact conversion and trust. Governance risks from compliance failures to accessibility issues and security exposure can arise and data fragmentation across platforms leads to measurement inconsistency.  

This cumulatively blocks personalisation, as relevant experiences cannot be scaled without a consistent foundation. 

What “good” consolidation looks like

Consolidation is about more than just reducing the number of websites in your ecosystem. It is about creating a coherent, manageable and scalable environment for your business to thrive digitally. When executed correctly, consolidation will unite each part of a digital estate under one governance model, ensuring consistency with content and design management. Its reusable components and shared design system, supported by a clear website and brand architecture, amplify this union.  

A composable headless CMS is central to this. It can create a single source of truth and eliminate one of the biggest causes of website sprawl: duplicate content across multiple systems. By centralising content and enabling its reuse across multiple websites, organisations can reduce reliance on fragmented legacy platforms. Separating content from presentation allows organisations to manage multiple sites from a single platform while delivering consistent user experiences across channels. This modular approach also enables legacy systems to be migrated gradually, which improves governance and reduces duplication.  

A shared measurement framework with analytics and tagging offers team comparable data and a single source of truth to work from. With accessibility built in by default, digital experiences can be enhanced and scaled confidently.  

Why consolidation is the entry point to orchestration

Website consolidation is often where fragmentation becomes most visible, but it is rarely just a website problem. True value comes when consolidation is approached as part of a wider ecosystem direction.  

Consolidation matters beyond websites because it: 

• Reduces digital sprawl and the “surface area of complexity” 
• Improves operational efficiency across teams and workflows 
• Streamlines the connection between journeys, data, CRM and personalisation 
• Creates a stronger foundation for consistent experiences, connected data and future orchestration 
• Sets up a scalable foundation for the future of orchestration and AI-driven experiences

How CACI can help with your website & CMS consolidation

CACI’s approach to website sprawl and consolidation is grounded in practical experience, helping organisations regain control and build a foundation for sustainable innovation.  

We start by understanding your current environment, mapping out where sprawl and hidden costs are lurking. We then work with you to design governance frameworks, implement visibility tools and optimise your workloads. You gain ongoing support, regular reviews and continuous optimisation to retain your focus on what matters most: delivering meaningful experiences and fostering innovation. 

Speak to our specialists today to assess where sprawl is creating the greatest operational drag and where consolidation can help you unlock the most value. 

Download our ecosystem orchestration infographic to find out whether your platform still supports how you need to operate today. 

Next in our series, we will explore another common blocker to orchestration: how disconnected CRM and digital platforms limit personalisation, create inconsistency and what organisations can do to overcome them.

Many organisations believe complexity is a technology problem. They invest in new platforms, modern architecture and advanced analytics to simplify systems, processes and decision-making. Instead, complexity rarely decreases; it shifts shape. 

The true challenge is structural. 

Enterprises evolve through layers of decisions: new systems, new processes and new organisational models. Over time, these layers accumulate without a shared understanding of how they connect. 

The result is familiar to every technology leader: 

• Change initiatives collide with unseen dependencies 
• Teams optimise locally, which can cause global friction
• Transformation slows despite the use of better tools.

Technology alone does not solve this problem. What organisations often lack is a clear, shared understanding of how they work: what their core capabilities are, how systems and processes depend on each other, and where change will have knock-on effects. 

When structure becomes explicit and living, complexity becomes navigable. 

Why “more data” is no longer the answer 

For years, digital strategy focused on data accumulation: data lakes grew, analytics platforms multiplied and dashboards became central to decision-making. 

Yet many CTOs and CIOs now experience a paradox: more data does not always produce clearer decisions. 

This is because insight without context creates ambiguity. Data shows patterns, but it does not explain what they mean for how the organisation works or what should change next. 

Meaning requires structure; the relationships between systems, processes, risks and strategic objectives. 

The next phase of enterprise intelligence will not be driven by more data, but by connecting data to organisational context. 

The question shifts from: “What does the data say” to “What does this mean for how our organisation works and what should we change?” 

The next evolution of enterprise platforms is model-driven 

Enterprise platforms have evolved in a clear progression: 

• Documentation tools captured structure 
• Analytics tools captured performance
• Low-code tools accelerate execution.

Each solved a problem, although none solved alignment. 

A new class of platforms is emerging: ones that begin with a shared organisational model and are a digital representation of how capabilities, processes and technologies connect. 

When applications and workflows are generated from this model, organisations gain something new: change becomes intentional rather than reactive. 

Model-driven platforms do not replace existing tools, rather, they provide the connective tissue that allows them to work together coherently. 

The future: Model-driven platforms, with low-code at scale 

Low-code platforms have transformed how organisations build software by reducing friction, empowering business users and accelerating innovation. 

But speed alone does not solve complexity, and as low-code scales, organisations may discover a new challenge: solutions can be built faster than organisations can understand their impact. 

Applications multiply, dependencies become opaque and governance becomes reactive. 

The limitation is not in low-code itself, but the absence of a shared model of the enterprise from which applications are built. 

The next generation of platforms will shift from building apps to generating them from an organisational understanding. 

Instead of designing every application independently, organisations will define how their enterprise works and allow systems to emerge from that foundation. 

This is not a rejection of low-code. On the contrary, organisations cannot do without it. But it needs to operate within a more strategic, model-driven framework that aligns applications to shared enterprise goals. 

Why this matters for CTOs and CIOS

As organisations grow in complexity, the challenge for CTOs and CIOs is no longer just delivering systems quickly, but doing so in a way that remains understandable, governed and aligned over time. 

For CTOs and CIOs, this means: 

• Understanding the impact of change before it is implemented 
• Maintaining governance without slowing delivery
• Keeping strategy, architecture and execution aligned over time
• Scaling low and no-code safely without architectural drift

If the constraints of traditional low-code platforms, overstretched IT teams or the risks of poorly governed business-led development are limiting your organisation’s progress, there is a more robust path forward. 

CACI’s model-driven enterprise platform, Mood, creates a living, digital representation of your organisation, connecting strategy, operations, systems, data and governance into a single, contextual enterprise model. This model becomes the foundation for application development, not an afterthought. 

Rather than building disconnected apps on fragmented data, you build directly from enterprise truth. 

By modelling how your business actually works, you can visualise dependencies, simulate change before implementation and generate operational applications directly from the enterprise model itself. Strategy and execution remain aligned because they share the same semantic core. 

The result is controlled agility: 

1. Transformation delivered at pace 
2. Governance built in by design
3. Full traceability from boardroom objective to system change
4. Sustainable low/no-code development without architectural compromise

This is not just application development. It is enterprise orchestration. 

If your ambition is to move beyond patchwork automation toward a truly model-driven enterprise, CACI can help you build it. 

Reach out to us for a free consultation on how a digital twin may help your organisation become more agile to change. For more on what a model-driven framework looks like in enterprises, get in touch here.

In our first blog of this service design series, we assessed the impact of service design on end-to-end performance and why it is critical for leaders to understand its intricacies. This blog looks at the key role that discovery plays in service design.
 
Many organisations have already invested in service design: running a discovery, mapping journeys and building personas, uncovering pain points and presenting the findings. Yet despite the effort, very little remains unchanged in the day‑to‑day reality of how a service works. If that sounds uncomfortably familiar, you are not alone. Many leaders find themselves in the same position: plenty of insight, but not enough impact.
 
While service discovery is invaluable, it does not fix broken services, reduce operational costs or improve customer experience on its own. Insight is only powerful when it leads to action. This is the moment where most organisations stall and where the real work of service design begins.

What discovery will help your organisation achieve

Discovery surfaces the truth about how your service performs today, exposing friction, inconsistencies and unnecessary complexity. It reveals gaps between what users expect and what your organisation delivers through tried‑and‑tested methods:

• Identifying pain points and experience failures.
• Journey mapping to highlight where user effort is wasted or where support breaks down.
• Service blueprinting to show the operational, policy and system-level issues creating that friction.

While these methods create clarity, clarity alone does not deliver change. It must be translated into decisions, prioritisation and delivery execution. Insight becomes valuable only when it moves beyond documentation and into operational improvement.

The most common point of failure in service design and transformation is not generating insight, but implementing it. This implementation gap is well recognised across large‑scale public service and organisational change, where strong discovery, policy or design intent often fails to embed into day‑to‑day delivery.

Why organisations struggle to move forward

• No clear ownership of delivery, leaving recommendations without accountable leaders to drive them
• Insights disconnected from a funded roadmap, so promising ideas never become prioritised work
• Lack of governance or performance mechanisms to sustain improvements once they move into live operations
• Misaligned teams (digital, ops, policy, technology) working on different goals, timelines and incentives
• Operational complexity and legacy constraints that make changes difficult to implement at scale
• Technology limitations that block even simple service improvements.

None of this is a failure of service design, but a failure of translation, from insight into action, from concept into delivery, and from isolated improvements into sustained, measurable performance gains.

What successful service transformation looks like

The organisations that unlock real value from service design treat discovery as the start, not the end. To convert insight into measurable operational improvement, they establish:

• Clear prioritisation
• A defined delivery roadmap
• Alignment between digital, operational and customer teams
• Governance and ownership
• Measurement frameworks

How CACI helps turn discovery insights into operational changes

When it comes to service design, many organisations see the fastest wins by starting small. CACI’s quick‑start service design sprints are intentionally lightweight, low‑risk and designed to show value within weeks, not months. These are focused, time‑boxed engagements that target a single service, customer journey or operational hotspot, giving you immediate clarity on where improvements will deliver the highest return.

Because each sprint blends user insight, operational analysis and pragmatic delivery planning, you get tangible outputs fast: a prioritised set of improvements, clear owners and actions your team can implement straight away to maximise impact.

Whether you need a Rapid Service Assessment, a Blueprint Sprint or an AI‑Readiness Review, these agile engagements allow you to test the value of service design, prove ROI early and build momentum without heavy internal lift or long procurement cycles.

It is the fastest, safest way to turn insight into operational improvement with CACI supporting you every step of the way.

Discovery is essential, but value is only realised when insight leads to action and when service design is connected to delivery, governance and operational realities. For organisations that have already invested in discovery but now need to turn recommendations into measurable outcomes, this is the moment to bridge the gap.

CACI can help your organisation move from insight to implementation and from implementation to impact, translating discovery into decisions, decisions into action and action into service performance.

Contact CACI’s Service Design team to get started.

The quick service restaurant (QSR) sector is entering 2026 at a pivotal moment. Consumer demand remains resilient, but the operating environment is more complex than at any point in the last decade. Inflationary pressures, labour shortages, evolving customer expectations and rapid technological change are forcing QSR leaders to make sharper, more evidence-based decisions.

While much has been written about emerging QSR trends, many articles stop short of answering the most important question: which trends will genuinely deliver sustainable growth, and which risk becoming costly distractions? 

In 2026, success will depend less on adopting every new innovation and more on prioritising the right initiatives, in the right locations, for the right customers, underpinned by strong data foundations. 

Why QSRs can’t afford to ignore these trends

Globally, the QSR market continues to grow, but that growth is increasingly uneven. According to market analysis of the UK foodservice sector, total market value is forecast to exceed £85bn by 2026, with growth driven largely by QSR and delivery-led formats. 

However, this growth masks significant pressure beneath the surface:

• Labour and food inflation have increased operating costs by 20–30% since 2019, according to hospitality cost analysis and UK inflation data. 
• More than 50% of UK consumers report actively cutting back on discretionary spending, including eating out. 
• Digital interactions now influence the majority of QSR transactions, from mobile ordering and kiosks to loyalty and payment.

Against this backdrop, trends are not abstract ideas — they directly influence network planning, pricing strategy, menu development and customer experience. Brands that understand how these trends play out locally are far better positioned to protect margins and unlock sustainable growth.

Top 7 quick service restaurant trends for 2026 

1. AI as a strategic engine, not just a technology layer 

Artificial intelligence has moved well beyond experimentation in QSR. In 2026, AI is increasingly embedded across forecasting, pricing, labour scheduling and customer engagement. 

Academic and industry research shows that machine-learning-based demand forecasting can reduce forecast error by up to 52%, directly lowering waste and improving operational efficiency. 

Additional industry analysis highlights that AI-enabled forecasting can reduce food waste by up to 25%, improving both sustainability and margins. 

However, the biggest gains come when AI is treated as a strategic capability, not a bolt-on. Without high-quality customer data, location insight and behavioural context, AI risks reinforcing inefficiencies rather than resolving them.

What leading QSRs are doing differently: 

Rather than deploying AI in isolation, leading QSRs are focusing on strengthening the data foundations that sit behind it. This includes improving customer data quality, linking transactional and behavioural signals, and incorporating location-based context into forecasting models. As a result, AI is increasingly used to anticipate demand, optimise decision-making and reduce operational risk, rather than simply automate existing processes.

2. Drive-thru reinvention: speed, accuracy and experience 

Despite the growth of delivery and mobile ordering, the drive-thru remains the backbone of the QSR model. Industry analysis consistently shows that drive-thru accounts for nearly 75% of QSR sales in mature markets.

Key developments shaping 2026 include:

• Voice AI reducing average order time by 20–30 seconds per vehicle 
• Increased use of queue analytics to manage peak-time congestion

Crucially, hospitality research shows that order accuracy and perceived friendliness have a greater impact on repeat visits than speed alone, reinforcing the need for balanced optimisation. 

What leading QSRs are doing differently:

Top-performing QSRs are moving away from uniform drive-thru solutions and instead optimising performance at a local level. By analysing demand patterns by site, time of day and customer mix, they are better able to balance speed, accuracy and service quality. This approach helps direct investment towards the locations and peak periods where improvements deliver the greatest return.

3. Omnichannel ordering and digital transformation (with loyalty at the core)

By 2026, omnichannel is no longer a differentiator — it is an expectation. Customers move seamlessly between apps, kiosks, drive-thru and delivery platforms. 

Industry data highlights that:

• Customers engaging across three or more channels are 2.5x more valuable than single-channel customers. 
• Loyalty members account for over 70% of digital QSR revenue at leading brands.

The challenge lies in orchestration. Fragmented systems and disconnected data undermine both margin and experience. Leading QSRs are investing in a single customer view, unifying transaction, behavioural and location data to understand which channels genuinely drive incremental value.

What leading QSRs are doing differently: 

Rather than treating channels independently, leading QSRs are building a more integrated view of the customer journey. By connecting data across mobile, in-store, drive-thru and delivery platforms, they gain clearer visibility of true customer value and channel interaction. This enables more consistent experiences, better-targeted loyalty strategies and improved understanding of which channels drive incremental growth.

4. Value-driven strategies in a cost-conscious market

Value has re-emerged as one of the defining QSR trends of 2026. According to UK consumer research, more than half of consumers actively compare prices before choosing where to eat. 

Additional findings show that:

• Bundled meals increase average order value by 8–12% 
• Limited-time offers drive trial without permanently eroding price perception

The most effective value strategies are location-specific, using data to tailor pricing and promotions to local demographics, competition and demand patterns. 

What leading QSRs are doing differently

Instead of relying on national price promotions, leading brands are taking a more nuanced approach to value. By analysing local demographics, competitive intensity and purchasing behaviour, they are tailoring offers and bundles to specific markets. This allows them to respond to price sensitivity where it exists, while avoiding unnecessary margin erosion in locations where demand is more resilient.

5. Sustainability and packaging innovation

Sustainability is now a baseline expectation rather than a differentiator. Research indicates that over 75% of consumers expect QSR packaging to be recyclable or compostable. 

Industry data also shows:

• Packaging redesigns can deliver 10–15% material cost savings 
• Food waste contributes 8–10% of global greenhouse gas emissions, increasing pressure on operators to reduce waste 

What leading QSRs are doing differently: 

Leading QSRs are embedding sustainability into operational decision-making rather than treating it as a standalone initiative. By monitoring waste, packaging usage and customer response at a granular level, they are able to test changes, measure outcomes and scale successful approaches. This data-led approach helps balance environmental goals with operational efficiency and cost control.

6. Health, wellness and radical transparency

Health-led eating continues to influence QSR menus. Consumer studies show that over 40% of UK consumers actively seek healthier options when eating out.

Protein-forward and plant-based items continue to outperform category averages, while demand for clear nutritional and allergen information grows. 

What leading QSRs are doing differently: 

Rather than expanding menus uniformly, leading operators are using customer insight to understand how demand for healthier options varies by location and occasion. This allows them to introduce targeted menu changes, refine portion sizes and improve transparency without adding unnecessary complexity. The result is a more relevant offer that reflects local preferences while maintaining operational simplicity.

7. Ghost kitchens and virtual brands: a more disciplined model

Ghost kitchens remain relevant, but success depends on precision. Market analysis shows that location selection and demand modelling are the biggest determinants of virtual brand success. 

Virtual brands are increasingly used to:

• Extend trade area coverage 
• Test new concepts with lower capital risk 
• Optimise delivery economics

What leading QSRs are doing differently:

Successful operators are taking a more analytical approach to virtual brands and ghost kitchens. By combining demand forecasting, delivery radius analysis and competitive mapping, they are identifying opportunities that complement existing estates rather than cannibalise them. This disciplined use of data reduces risk and improves the likelihood of sustainable performance.

How QSR leaders can act on 2026 trends today

Understanding trends is only half the challenge. The real differentiator is execution. 

To translate 2026 trends into commercial advantage, QSR leaders should focus on five practical steps: 

1. Prioritise trends by impact, not hype 

Not every trend will matter equally to every brand. Use data to assess which initiatives will:

• Drive incremental demand 
• Improve operational efficiency 
• Strengthen customer loyalty 

2. Ground innovation in customer insight 

Customer expectations vary significantly by location, demographic and occasion. Advanced segmentation and behavioural analysis help ensure investment aligns with real demand. 

3. Use location intelligence to guide decisions 

From drive-thru optimisation to ghost kitchens, place matters. Understanding trade areas, cannibalisation risk and local competition reduces costly mistakes. 

4. Test, learn and scale 

Pilot new formats, offers and technologies in controlled environments. Measure results rigorously before national rollout. 

5. Build a strong data foundation 

Unified, high-quality data underpins every successful trend — from AI to personalisation to sustainability.

Future outlook: what comes next?

Looking beyond 2026, the QSR sector will continue to converge with retail and digital commerce. Automation will increase, but human service will remain critical. Data will become more central — not just for optimisation, but for resilience. 

The brands that outperform will be those that:

• Invest in insight, not just infrastructure 
• Optimise locally, not just nationally 
• Align innovation with measurable commercial outcomes

In a volatile environment, clarity beats complexity — and data-led decision-making is the most reliable route to sustainable growth. 

Frequently asked questions about QSR trends for 2026

What are the top quick service restaurant trends for 2026? 

The top quick service restaurant trends for 2026 include AI-driven operations, drive-thru optimisation, omnichannel ordering, value-led pricing strategies, sustainability-focused packaging and data-driven personalisation. These trends reflect rising cost pressures, digital adoption and changing consumer expectations across the QSR sector. 

How is AI being used in quick service restaurants? 

AI is used in quick service restaurants to improve demand forecasting, labour scheduling, order accuracy and personalised marketing. By 2026, many QSRs use AI to reduce food waste, optimise staffing and deliver more relevant customer offers in real time. 

Why is value such an important trend for QSRs in 2026? 

Value is a key QSR trend in 2026 because consumers are more price-conscious due to ongoing cost-of-living pressures. Quick service restaurants are responding with targeted value meals, bundles and promotions that balance affordability with profitability. 

Are ghost kitchens still relevant in 2026? 

Yes, ghost kitchens are still relevant in 2026, but they are used more selectively. QSR brands now rely on demand modelling, delivery radius analysis and location intelligence to ensure ghost kitchens are commercially viable. 

What role does data play in QSR trends for 2026? 

Data plays a central role in QSR trends for 2026 by enabling better decision-making across pricing, site selection, customer engagement and operations. Brands that integrate customer, transaction and location data are better positioned to adapt to market changes. 

How can quick service restaurants prepare for the future beyond 2026? 

Quick service restaurants can prepare for the future by investing in strong data foundations, customer insight and flexible operating models. This allows QSRs to test new concepts, optimise locations and respond quickly to evolving consumer behaviour.

Why Share of Wallet matters now 

Customer acquisition costs continue to rise, and the dynamic is even more pronounced in financial services, where competition for deposits, primary current accounts, and long-term savings has intensified. Research from the Harvard Business Review shows that it can cost up to five times more to acquire a new customer than to retain an existing one. Meanwhile, customer expectations have increased, switching barriers have fallen, and digital competitors are often just a click away. Within financial services, Open Banking has further accelerated switching and multi-banking behaviour, giving customers more freedom to distribute their balances across multiple institutions 

Organisations that focus only on acquisition risk spending heavily without ever realising sustainable growth. This is particularly true in financial services, where the cost of onboarding, KYC, AML checks, and compliance activities makes new customer acquisition especially expensive. As noted by Deloitte Insights, financial institutions that prioritise deepening existing customer relationships outperform those that rely heavily on acquisition-led strategies. 

Through Share of Wallet, financial institutions can also: 

• Identify the value of balances customers hold elsewhere, giving institutions insight into hidden opportunities for deposit and investment growth. 
• Understand which demographics, products and regions are outperforming the base, simplifying the identification of priority growth segments. 
• Access aggregated SOW metrics and periodic reporting, enabling customer-level and portfolio-level performance tracking. 
• Track KPIs linked to long-term strategic initiatives, connecting balance growth with broader business outcomes. 
• Use granular data to inform personalised communications, targeting customers based on wealth indicators, behaviours and potential. 

This guide explains what share of wallet means in a financial-services context, how to calculate it using balances and asset concentration, why it matters strategically, and the practical, analytics-driven methods institutions use to increase it. Drawing on use cases across banking, savings, credit, and wealth management — including work CACI delivers — this guide shows why leading FS organisations now treat balance-based SOW as a cornerstone of sustainable growth. 

What is Share of Wallet? 

Share of Wallet (SOW) in financial services refers to the proportion of a customer’s total account balances or savings “wallet” that they hold with your institution across products such as current accounts, savings, ISAs, investments, mortgages or personal loans. 

For example, if a customer has total liquid savings of £40,000 and holds £10,000 of those balances with your bank, your SOW is 25%. 

This measurement applies across the sector: the percentage of a customer’s investable assets held with a wealth manager, the proportion of deposits concentrated with a building society, or the share of credit balances placed with one provider. 

SOW provides a more complete understanding of customer value by: 

• Revealing the total wealth picture, rather than only internal balances. 
• Highlighting how much money customers hold elsewhere, enabling accurate opportunity sizing. 
• Filling gaps in financial understanding that internal data alone cannot provide. 

Share of Wallet vs Market Share

The two metrics assess very different dynamics:

• • Market share measures your institution’s total balances or products across the market. 
  • Share of wallet measures the proportion of each individual customer’s financial life that you hold. 

A bank may have high market share yet a low share of wallet per customer — signalling weak relationship depth. Conversely, a smaller provider might have very high wallet share among a loyal customer base. 

SOW also supports strategic decision-making by enabling: 

• Tracking of balance growth KPIs across segments and product lines. 
• Monitoring long-term performance such as deposit acquisition, wealth onboarding and cross-product engagement. 
• Identifying “headroom” — the additional balances customers are likely to hold elsewhere that could be captured. 

How to calculate share of wallet 

The Basic Formula 

SOW (%) = (Balances held with your institution ÷ Customer’s total balances) × 100

Example: 
• Total savings: £60,000 
• Balances with your bank: £15,000 
• SOW = 25% 

Data Sources for Calculation

• Internal account and balance data 
• Open Banking and aggregation tools 
• Customer research panels 
• Predictive modelling and machine-learning estimation of held-away balances 

A strong SOW calculation enables institutions to: 

• Combine customer-level balance estimates with postcode-level and product-level data for a 360° view of financial behaviour. 
• Use CACI Retail Finance Benchmarking to understand typical wallet sizes, competitor penetration and localised patterns. 
• Integrate wealth estimates into modelling, segmentation and pricing cohorts. 

Common Challenges

• Hidden balances not visible to individual providers 
• Volatile liquidity movements 
• Categorisation differences across product types 
• Life-stage and macroeconomic factors influencing wallet size

Why Share of Wallet Matters

Cost-Efficient Growth 

Deepening customer relationships by capturing more of their financial life is significantly more cost-effective than acquiring new customers. Increasing balance concentration boosts revenue per customer while lowering cost-to-serve. 

Customer Retention and Loyalty 

Customers who place a higher proportion of their savings or investment assets with one institution demonstrate far stronger loyalty and lower churn. 

Lifetime Value 

As wallet share increases, so does Customer Lifetime Value (CLV). Customers with deeper financial relationships are more likely to take mortgages, lending products, savings accounts and wealth services. 

Strategies to increase Share of Wallet

Segment Customers by Potential 

Not all customers have the same growth potential. SOW helps identify:

• High potential, low share customers with substantial held-away balances 
• High value customers to defend and deepen 
• Lower potential segments requiring reduced investment 

CACI helps institutions uncover these opportunities using demographic, geographic and behavioural insight. 

Cross-Selling and Upselling 

Examples include: 

• Encouraging current-account-only customers to open savings products 
• Moving savers from low-yield accounts to higher-value fixed-term or investment products 
• Introducing ISA or wealth solutions to customers showing investment readiness 

Next best product models identify optimal timing. 

Loyalty, Rewards and Relationship Pricing 

Mechanisms include: 

• Preferential rates for customers consolidating savings 
• Bundles linking savings, current accounts and credit 
• Incentives for salary mandates or account funding 

Bundling and Value Propositions 

Product bundles and integrated financial management tools increase stickiness by offering convenience, clarity and control. 

Customer Experience 

Ease, trust and service quality materially influence wallet share. Positive digital and branch experiences translate directly into balance consolidation. 

Financial Services use case: Share of Wallet in banking 

Customer-Level Coding 

Banks assess the percentage of customer balances they hold to identify:

• Customers with significant held-away funds 
• Investment assets managed by competitors 
• Opportunities to deepen primary relationships 

Savings Behaviour and Headroom 

Balance-based analysis distinguishes between:

• Fixed savings 
• Variable savings 
• Investment holdings 

Customers with large variable balances but low SOW offer clear growth potential. 

Segmentation by Demographics 

Older customers often consolidate more; younger customers diversify more widely. 
CACI’s Fresco segmentation adds further behavioural and life-stage context. 

Monitoring and Tracking 

Modern analytics track: 

• Balance concentration shifts 
• Flow of funds in and out of held-away accounts 
• Changes in product mix and adoption patterns 

How Institutions Use SOW

• Identify and quantify customer-level opportunities 
• Use CACI Retail Finance Benchmarking and location intelligence to find geographic hotspots 
• Target segments with low share but high growth capacity 
• Avoid unnecessary rate rises for customers already showing high SOW Provide frontline teams with estimated SOW indicators for personalised engagement 

Sector perspectives beyond Financial Services

 Retail and E-commerce  

Supermarkets compete to become the primary shopper destination. Loyalty cards, personalised coupons, and basket-building promotions all increase wallet share. E-commerce platforms use recommendation engines and premium memberships to keep customers buying within their ecosystem.  

Telecoms and Media 

Quad-play packages dramatically increase wallet share by consolidating multiple services into one bill. Customers who bundle are less likely to switch because of the perceived inconvenience of managing multiple providers.  

B2B and Professional Services  

For B2B firms, wallet share often means expanding into adjacent service areas. A consultancy may start with strategy and then cross-sell into analytics, technology, or managed services. Increasing wallet share in B2B builds long-term, multi-service relationships that are resistant to competitor approaches. 

Share of Wallet pitfalls and limitations 

Financial services face additional challenges: 

• Over-marketing: too many rate-driven offers can reduce trust. 
• Cannibalisation: shifting balances between products may not increase total value. 
• Balance volatility: savings can move rapidly in response to macro-economic signals. 
• Privacy and regulation: strict rules govern the use of customer financial data. 

Institutions should balance ambition with transparency and ethical standards. 

Advanced Share of Wallet analytics: The CACI approach 

Real differentiation comes from analytics: 

• Predictive modelling: estimating total wallet and held-away balances. 
• Uplift modelling: identifying which customers are likely to consolidate more funds. 
• Controlled experimentation: validating rate changes or marketing interventions. 
• Dashboards: tracking SOW in real time across segments and product lines. 

CACI’s data science services help banks turn SOW from a descriptive measure into a predictive, prescriptive engine for long-term balance growth. 

Share of Wallet implementation roadmap 

• Assess: measure baseline balance concentration. 
• Prioritise: identify customers with high potential and low current share. 
• Design: develop targeted financial strategies — pricing, product prompts, digital journeys. 
• Execute: deploy at the right moment with meaningful personalisation. 
• Measure: track responses, adjust propositions, and optimise. 

Evolving Dynamics of Wallet Share 

Wallet share in FS is evolving through: 

• AI-powered personal finance tools influencing balance allocation. 
• Open Banking transparency enabling better competitor comparison. 
• Cross-category mapping (e.g., savings vs investments). 
• ESG-driven decision-making shaping where customers place their assets. 

Conclusion 

Share of Wallet is more than a KPI — it is a growth framework grounded in balance concentration and trusted financial relationships. By accurately measuring and acting on SOW, institutions can increase profitability, reduce churn, and deepen their role in customers’ financial lives. 

CACI’s expertise in data science, segmentation, and customer insight helps banks move from generic cross-sell to intelligent, targeted strategies that materially increase the proportion of savings, balances, and financial value customers hold with them. 

Share of Wallet FAQs 

1. What is share of wallet in banking? 

Share of wallet in banking refers to the proportion of a customer’s total account balances or savings that they hold with a specific financial institution. 

2. How do banks calculate share of wallet? 

Banks calculate share of wallet by dividing the balances a customer holds with them by the customer’s estimated total savings or assets, including held-away funds. 

3. Why is share of wallet important for financial institutions? 

A higher share of wallet increases customer lifetime value, improves retention, and strengthens the institution’s role as the customer’s primary financial relationship. 

4. What is a good share of wallet percentage for banks? 

A strong share of wallet typically means holding the customer’s primary current account and a significant portion (often 50% or more) of their liquid savings. 

5. How can banks increase share of wallet? 

Banks increase share of wallet by offering competitive savings rates, personalised product recommendations, relationship-based incentives, and frictionless digital experiences that encourage customers to consolidate balances. 

6. What are held-away balances in financial services? 

Held-away balances are savings or investment funds that a customer holds with other institutions, which represent potential share of wallet growth opportunities. 

7. What affects a customer’s share of wallet? 

Factors include trust, interest rates, digital experience, financial goals, risk appetite, and the convenience of managing multiple financial products in one place. 

8. How does share of wallet relate to customer loyalty? 

Customers who allocate more of their balances to one institution typically show higher loyalty, lower churn, and longer relationship tenure. 

9. What tools do banks use to measure share of wallet? 

Banks use predictive modelling, Open Banking data, demographic profiling, and internal balance analytics to estimate total wallet size and identify held-away funds. 

10. What is a share of wallet strategy in financial services? 

A share of wallet strategy focuses on increasing the proportion of a customer’s total balances, deposits, or investable assets held with the institution through targeted engagement and personalised offers. 

Why Share of Wallet analysis matters

Most financial institutions recognise that retaining customers is more cost-effective than acquiring new ones. Yet few have a reliable method for understanding how much of a customer’s total savings, deposits or investment balances they actually hold, or how much value sits hidden in other institutions. This is where Share of Wallet Analysis becomes indispensable. 

In financial services, Share of Wallet (SOW) reflects the proportion of a customer’s total financial holdings—savings, current account balances, fixed-term deposits, investments or unsecured lending—held with your institution. Share of wallet analysis refers to the methods, data and models used to measure, estimate and interpret a customer’s total balance wallet, including held-away funds. Done well, it uncovers hidden balance headroom, identifies consolidation opportunities, highlights attrition risk, and provides a roadmap for profitable balance growth. 

In this article, we explore what share of wallet analysis means within financial services, how it is conducted, common analytical methods, and how advanced modelling transforms SOW from a static metric into a powerful engine for deposit growth, cross-sell, retention and customer value expansion. 

👉 If you’re new to the concept of wallet share itself, start with our Definitive Guide to Share of Wallet for Financial Services and then return here for the measurement and analysis deep dive. 

What is Share of Wallet analysis? 

Share of Wallet Analysis in financial services is the process of calculating and interpreting the proportion of a customer’s total account balances held with your institution versus competitors. It goes beyond the raw SOW percentage to understand why customers distribute balances the way they do, what balance growth potential exists, where consolidation opportunities lie, and which customers present the strongest long-term value. 

In practice, SOW analysis involves: 

• Measuring balances held with your institution 
• Estimating total customer wallet size, including held-away savings and investments 
• Identifying patterns across customer, product and demographic segments 
• Using predictive analytics to model future balance consolidation and risk 

Methods of Share of Wallet analysis 

1. Survey-Based Approaches 

Historically, banks and building societies often relied on surveys asking customers where else they held savings or investments. 

Strengths: 

• Useful for capturing attitudinal data (trust, preference, propensity to consolidate) 
• Can identify perceived gaps in relationships 

Weaknesses: 

• Self-reported balances are often inaccurate 
• Customers underreport or forget held-away accounts 
• Hard to scale reliably 

📖 Research published in the Journal of Marketing Research shows that self-reported financial behaviour often underestimates total balances. 

2. Internal Transactional and Balance Data 

Banks, building societies and wealth managers hold accurate information about the customer’s primary account balances—current accounts, savings, term deposits, ISAs, loans and investments. 

Strengths: 

• Highly accurate, real-time data 
• Enables granular behaviour analysis (flows in/out, volatility, deposit stability) 
• Supports segmentation and life-stage profiling 

Weaknesses: 

• Limited to balances held with your organisation 
• Does not show the size of competitors’ holdings 

This is the foundation for customer-level SOW coding but requires external data or modelling to understand the full wallet.

3. Third-Party Panels and Benchmark Data 

Industry benchmarks—such as regulatory publications, anonymised credit bureau data or aggregate financial panels—help institutions estimate likely total wallet sizes across segments. 

Strengths: 

• Offers a market-level perspective 
• Useful for comparing your penetration against competitors 

Weaknesses:

• Panels may not align perfectly with your customer mix 
• Insights are directional, not customer-specific

A Deloitte report on financial services highlights that panel data supports competitive context but must be calibrated to segment differences. 

4. Predictive Modelling 

This is the most advanced and reliable approach for FS. Predictive models estimate total customer wallet size, including balances you cannot see, using behavioural indicators, demographics, product mix, income signals and external datasets. 

Techniques include: 

• Regression models linking known balances to inferred total wealth 
• Machine learning models using hundreds of variables to predict wallet size 
• Uplift modelling to assess which actions drive incremental consolidation 
• Propensity-to-save and propensity-to-move models 

At CACI, we combine internal balance data, segmentation, geography and market-level insight to produce a highly accurate picture of held-away balances, wallet potential and consolidation opportunity. 

The process of Share of Wallet analysis

Step 1: Define the Financial Category 

Define what counts as the “wallet”: 

• Liquid savings 
• Fixed-term deposits 
• Current account balances 
• Investment assets 
• Unsecured lending exposure 
• The category definition shapes both measurement and modelling. 

Step 2: Collect and Integrate Data 

Bring together: 

• Internal balance data 
• Product holdings 
• Customer demographics 
• External panels and benchmarks 
• Predictive model outputs 

This is where CACI’s expertise in customer data integration and Retail Finance Benchmarking becomes essential.

Step 3: Calculate Current Wallet Share 

Apply the adapted FS formula: 

SOW (%) = (Balances held with you ÷ Estimated total customer wallet) × 100 

Step 4: Segment and Prioritise

Segment customers into actionable groups: 

•  High wallet, low share (big consolidation opportunity) 
• High wallet, high share (protect and retain) 
• Low wallet, high share (profitable but low headroom) 
• Low wallet, low share (limited upside)

Step 5: Apply Predictive Analytics 

Model: 

• Total wallet value 
• Likely held-away balances 
• Customer headroom 
• Propensity to consolidate 
• Product-specific opportunities (savings, ISAs, term deposits, investments) 

Step 6: Translate Insight into Action 

Actions include: 

• Targeted savings growth campaigns 
• Relationship pricing for consolidation 
• Fixed-term renewal strategies 
• Investment readiness triggers 
• Personalised engagement sequences 

Why advanced analytics makes the difference 

Basic wallet share tells you the percentage you currently hold. Advanced analytics tell you how much you could hold, how to win it, and where the risks are. 

Predictive Power 

Models forecast wallet potential for each customer, identifying those most likely to consolidate balances. 

Uplift Measurement 

Uplift modelling isolates the true incremental effect of actions—ensuring incentives are only offered where they change behaviour. 

Dashboards and Visualisation 

Dynamic dashboards allow product, marketing and risk teams to track: 

•  Wallet share 
• Flows in and out 
• Consolidation patterns 
• Segment-level performance 

Forrester research highlights that organisations adopting advanced analytics see significant improvements in customer experience outcomes. 

Sector examples of Share of Wallet analysis

Banking and Financial Services 

Banks use SOW analysis to identify: 

• Customers with large savings held externally 
• Deposit consolidation opportunities 
• ISA or investment readiness 
• Mortgage customers without savings or wealth relationships 

For example, a customer with high income and low internal savings may hold significant deposits elsewhere—representing high wallet headroom.

Retail and E-commerce (Contextual Comparison Only) 

Retailers use similar principles, but FS analysis focuses on balances, not spend. 

Telecoms and Media (Conceptual Parallel) 

Bundling logic informs FS strategies such as linking current accounts, savings and credit. 

B2B Services

Professional services firms use wallet analysis to expand into adjacent advisory domains. 

Pitfalls in Share of Wallet analysis

• Over-reliance on surveys
• Poor data governance or misuse of Open Banking data
• Treating all customers as having equal wallet potential
• Short-term incentives that erode long-term margin
• Misinterpreting volatility in savings (seasonality, life events)

Future of Share of Wallet analysis 

The next decade will further accelerate SOW capability through: 

• AI-driven next-best-action models 
• Real-time balance monitoring through connected data ecosystems 
• Cross-category household finance modelling 
• ESG-aligned financial behaviour analysis 

Organisations using AI-led wallet prediction will outperform those relying on historical balances alone. 

Conclusion

Share of Wallet Analysis turns a simple metric into a strategic growth engine. In financial services, it reveals how much of a customer’s total savings, deposits and investments you truly hold, where your hidden opportunities lie, and what actions will maximise customer lifetime value. 

By combining advanced analytics, data integration, segmentation and customer insight, financial institutions can unlock held-away balances, increase consolidation and strengthen their role in customers’ financial lives. 

At CACI, we help institutions turn SOW analysis into measurable growth—building models, integrating data and designing targeted interventions that drive long-term, profitable balance expansion. 

Low-code promises speed and greater autonomy for delivery teams. Done well, it can reduce bottlenecks and help organisations build and iterate quickly. But organisations that adopted low-code early are now finding that speed without shared structure can simply get you to the wrong place faster. The challenge is rarely the low-code tooling itself, but how it is used, governed and connected to the wider enterprise. 

So, why does low-code hit a ceiling? What does that ceiling look like within organisations, and how can a meta-model remove it?

The unintended costs of using low-code tools

Low-code platforms are great for fast application development through drag-and-drop techniques, followed by adding the logic. This app-first approach can be fast and accessible, particularly for smaller teams and well-bounded use cases. However, at scale, the approach can come at a cost if there is no shared model to keep applications aligned and consistent. 

Organisations may end up with a portfolio of disconnected, inconsistent and error-prone applications. Issues may go unnoticed, such as an increase in operational silos, technical debt and divergence from policy, but show up in: 

Governance: “How many apps do we have?” and “What data do we hold?” 
Scalability: “We cannot reuse anything without breaking something.” 
Strategic: “We have automated today’s mess, not tomorrow’s organisation.” 

The lack of structure around low-code is what causes these issues. Therefore, the aim should not be to automate fast, but to understand and evolve the organisation to deliver on its strategic and operational objectives coherently. 

Low-code: Great for building applications, weak for structuring them

Low-code enables the speedy assembly of applications. However, as an organisation grows, complications arise. Without a clear structure in place, projects risk becoming scattered and hard to manage, and teams can struggle to reuse, govern and scale what they have built. 

Before building any new application, assessing the organisation’s current situation and required changes is essential. Creating a meta-model that accurately reflects the organisation will offer a solid base for building applications, integrating new work, and maintaining consistency as delivery scales. 

By beginning with the enterprise model, which defines organisational purpose, then mapping out semantic relationships for context, business logic becomes transparent. This approach enables genuine, evidence-based decision intelligence. 

What is a meta-model? 

A meta-model is a master blueprint of an enterprise. It captures the things that matter most about how the organisation works, and how those elements relate to one another, so that applications and workflows can be built with shared context rather than in isolation. 

Using the analogy of a large housing development: although individual homes may vary in appearance and layout, they share common foundations, materials and construction processes to maintain consistent quality. 

A meta-model does this for applications. It guides the creation of specific applications tailored to each use case by defining the structure and context, while upholding overarching standards. 

It is the difference between a collection of diagrams and workflows and a living, navigable model of your enterprise that aligns to strategy. 

Instead of thinking about building apps in isolation, the question becomes: “What organisational change are we enabling and how does it connect to everything else?” 

Get the agility of low-code with the rigour of enterprise modelling 

When low-code is underpinned by meta-modelling, everything changes: 

• Reusable, consistent and governed logical structure
• Build interfaces that enable you to simulate and test changes safely 
• Align technical design with business strategy from day one

When enterprise structure becomes the foundation, speed and coherence stop being competing goals. They become complementary. 

Platforms like Mood, CACI’s digital twin platform for actionable organisational transformation, combine no-code and low-code tooling with a powerful, flexible meta-model capability at its core. This means teams can keep the speed benefits of low-code, while gaining the shared context needed to scale safely and consistently. 

What role do dashboards play? 

Most organisations are rich in analytics. Dashboards track performance, visualise trends and surface insights faster than ever. Business intelligence has transformed how leaders see their organisations. 

Yet many decision-makers experience familiar frustration: they can see the problem, but not the path forward. 

Analytic platforms excel at answering: 

• What happened? 
• Where are trends emerging?
• Which metrics changed?

But they rarely answer: 

• Which capability caused this? 
• What dependencies will be affected if we intervene?
• How will change ripple through the organisation?

Understanding these questions requires more than data. It requires structure. 

Enterprises are not just datasets. They are systems of interconnected capabilities, processes, technologies, risks and strategies. When this structural understanding is captured as a living model, analytics gains context. Instead of simply observing change, organisations can simulate it. 

The future of enterprise decision-making lies not in more dashboards, but in connecting insight to organisational meaning and executing successful transformation. 

The missing layer in digital transformation: Enterprise context 

Many transformation initiatives struggle not because of lack of tools or investment, but because of fragmentation. 

Different teams use different platforms: 

• Analytics tools for insight 
• Low-code tools for apps
• Architecture tools for modelling
• Project tools for execution

Each solves a piece of the puzzle, few connect them. 

What is missing is a shared context, a way to understand how decisions in one domain affect another. Without this, organisations experience: 

• Duplicated solutions 
• Misaligned initiatives
• Hidden dependencies

A model-driven approach introduces a new layer: a semantic representation of the enterprise. 

This is not documentation for its own sake, but a living structure that connects strategy, operations, technology and execution. When applications, workflows and analytics align to this model, transformation becomes coordinated rather than fragmented, and agile to change rather than a rigid waterfall. 

From documentation to execution: The evolution of enterprise architecture 

Enterprise architecture has often been misunderstood as static documentation; diagrams that describe how systems are organised. 

The role of architecture is changing, however. As organisations face increasing complexity, architecture is evolving from passive description into active orchestration. 

The next generation of platforms does not simply document reality; it drives behaviour from it. 

Model-driven approaches enable: 

• Applications generated from enterprise structure 
• Governance embedded into workflows
• Decision impact analysed before implementation

Architecture becomes not a record of change, but the engine that enables it safely. 

This shift represents a broader evolution: from understanding complexity to operationalising it. 

The future enterprise platform: A digital twin for decision-making 

The concept of a digital twin has moved beyond engineering into the organisational domain. 

A digital twin of the enterprise is not merely a visualisation of assets or data. It is a dynamic representation of how an organisation functions; capturing relationships between capabilities, processes, systems and outcomes. 

Such a platform allows leaders to: 

• Simulate change before execution 
• Understand cross-domain impact
• Align strategy with operational reality

As AI and automation accelerate the pace of change, organisations will need more than tools that execute tasks quickly. They will need systems that understand context. 

The future enterprise platform will not be defined by how many apps it builds or dashboards it produces, but by how effectively it helps organisations to understand themselves and evolve intentionally. 

Don’t know where to start?

If the limitations of low-code, blockers by lack of IT resources or worries about the consequences of citizen development are impacting your organisation, CACI can help. 

Reach out to us for a free consultation on how a digital twin may help your organisation become more agile to change. 

Fuel volatility has become one of the most significant challenges facing logistics leaders. The industry is highly susceptible to variability, and with ongoing disruption in global energy markets, rising fuel prices are driving up operating costs and putting wider network performance under strain. 

Amidst the uncertainties, one thing is clear: logistics leaders must act now to prevent losses in their networks. So, what does this fuel volatility and rising uncertainty mean for the industry and how can leaders counter these effects? 

What fuel volatility means for logistics operations

Three themes are emerging consistently across the sector. 

Efficiency becomes non‑negotiable

Tiny inefficiencies scale fast across a fleet. What was once considered a “good enough” plan that worked at £x/litre often will not survive at £y/litre.  

As fuel costs increase, efficiency is no longer a nice-to-have. Downstream, domestic fleets are particularly impacted, as higher fuel prices amplify the cost of everyday decision-making from route choice and stop density to vehicle utilisation and realistic drive times.  

Cost forecasts must reflect real operations

Forecasting costs is more than just refreshing a spreadsheet. It is about grounding forecasts in what happens on the road, not what logistics leaders hope a plan will deliver.  

While many cost models rely on planned mileage and theoretical routes, rising fuel prices expose a gap between what was planned and what happened, which becomes expensive quickly.  

Re‑forecasting in this environment requires operational truth: understanding real mileage, real execution behaviour and where cost is genuinely being added, not assumed away. 

Route compliance becomes the lever that matters most

Optimisation only creates value when executed. If the plan is not followed, you are not just missing savings, but layering on cost through extra miles, minutes and exceptions.  

Route deviations, congestion and last‑minute re-planning add unplanned miles at much higher costs per mile. Extended upstream transit times increase pressure on domestic distribution to recover service levels, often at the expense of fuel efficiency. Fleet and light commercial vehicles have been swelling the electric vehicle (EV) market, so logistics organisations in a position to adopt electric vehicles (EV) into their fleet can further reduce their fuel dependency and cut costs.  

How can logistics leaders counter the effects?

Logistics organisations that are coping best with fuel volatility are the ones treating efficiency as an ongoing operational discipline, not a one‑off optimisation exercise. Those prioritising the optimisation of their logistics operations via the most advanced algorithms and real-world data will stay afloat amidst uncertainty.  

Planning optimal routes

When fuel prices rise, every unnecessary mile becomes a direct hit to margins. Organisations can counter this by using CACI’s advanced route optimisation to continuously minimise distance travelled, time on the road and fuel consumed – without compromising service levels.  

By dynamically calculating the most efficient routes using advanced algorithms, organisations can reduce empty miles, avoid congestion and balance workloads more effectively across fleets. 

Focusing on operationally realistic routes

Organisations that account for vehicle constraints, compliant roads and what drivers experience on the ground are creating the most operationally realistic routes and best placed to counter the effects of fuel volatility.  

Closing the loop between planning & execution

Leaders shifting from planning quality alone to execution quality can:  

• Understand where and why deviations occur  
• Distinguish necessary exceptions from avoidable behaviour  
• Feed execution insight back into better planning

These help safeguard from fuel volatility and encourage efficiencies. By embedding efficiency as a discipline, grounding forecasts in operational reality and closing the gap between route planning and execution, organisations can move from reactive cost management to predictable and resilient logistics operations, even in uncertain conditions. 

How CACI can help

CACI’s Logistics experts help organisations design efficient routes, re‑forecast costs using real operational data and ensure planned routes are executed. This ensures rising fuel costs do not automatically translate into rising inefficiency. 

Pin Routes, CACI’s route optimisation software, is designed to help organisations cut costs, navigate uncertainties and increase efficiency, so that these rising costs have less of an impact. Pin Live, our delivery and collection management software, helps drivers take the correct detour and improve last-minute decision-making when changes arise on the road. Together, these tools help logistics leaders improve route compliance and maintain predictable operations despite market uncertainty. 

To learn more about how CACI can help your organisation effectively navigate fuel volatility at cost, get in touch with us. 

Service design may be a familiar term among senior leaders, but clearly articulating what it means in practice can be a challenge. While awareness of service design is high, only around 3% can define it accurately, highlighting a long‑standing understanding gap.  

As the market currently stands, this is costly. In 2025, 70% of executives said customer expectations are evolving faster than their organisations can keep up, with 52% of consumers stopping using a brand due to a poor experience. Internal pressure is simultaneously mounting, with two‑thirds of leaders describing their organisations as overly complex and inefficient and only half feeling prepared for external shocks.  

Clarity around service design is imperative for performance. So, how does understanding the intricacies of service design impact your organisation’s end-to-end performance? 

What is service design?

In commercial and operational terms, service design is the discipline of improving end‑to‑end service performance. It aligns the entire service ecosystem, people, processes, technology, data, policy and experience, ensuring services function accordingly.  

Where a UX designer focuses on research and purely digital components like websites, a service designer will consider all touchpoints (telephony, physical spaces, technology infrastructure, etc.) for both its users and employees, discovering and fixing pain points.  

Service design is: 

• Understanding how a service works today (across frontstage and backstage) 
• Identifying what users need and where the service breaks down 
• Designing how the service should work: consistently, efficiently and at scale 
• Aligning digital, operations and experience into a unified service model 
• Creating a roadmap that is actionable, measurable and ready for delivery

Service design is not: 

• Just journey mapping 
• An isolated discovery exercise 
• A purely creative or theoretical activity 
• A handover document expecting someone else to deliver it 
• A UX‑only discipline

At its core, service design is about making services more efficient for end-to-end customers users and the teams delivering them while enabling growth. 

Understanding the impact of service design on end-to-end performance

While service design has become popularised across digital transformation, customer experience, and operational change, understanding its place (whether it mirrors journey mapping or UX), where it fits within your organisation’s objectives and whether it will improve performance remain in question. 

Many organisations invest in fragmented discovery work, generate compelling artefacts and still struggle to fix the operational issues that matter. This deduces service design to a capability, not a driver of performance. 

Meanwhile, AI is accelerating change faster than most companies can absorb. Nearly two‑thirds of organisations yet to scale AI effectively, emphasising the need for a clear, practical and end‑to‑end approach to service design. When service design is poorly understood, opportunities are missed along with potential performance gains. When integrated from discovery through to delivery, organisations see:   

• Modernise faster with less rework 
• Adapt to market disruption 
• Reduced programme risk and operational waste through meaningful change that sticks 
• Deliver services that are easier for users and more efficient for teams 
• Cut costs to unlock value across your entire service ecosystem. 

Service design is more than just a way to fix broken experiences. It is a strategic lever for growth, efficiency, resilience and competitive advantage. 

How CACI enables service design built for implementation

At CACI, service design begins the moment insight turns into direction. Unlike traditional models where discovery and delivery sit far apart, our approach embeds service design thinking directly into the core functions that drive change. From data and analytics to digital engineering, architecture, technology delivery, operational transformation, change management and programme assurance.

By integrating these capabilities, we remove the gaps and hand‑offs that typically slow organisations down. It means the services we design can be implemented without translation, the solutions we deliver are measurable from day one, and the insights we capture continually feed improvement. Ideas don’t get diluted as they move downstream, they gain momentum. 

Why this matters for modern organisations 

Leaders typically operate in environments defined by rising expectations, increasing complexity, legacy constraints and mounting pressure to deliver seamless, reliable and efficient services. 

Service design plays a critical role in enabling this by helping organisations: 

• Align services with strategic intent, policy goals or commercial outcomes 
• Improve operational performance and reduce friction across journeys 
• Deliver measurable, user‑centred improvements that stand up to scrutiny 
• Modernise processes and technology to unlock value from existing and future platforms 
• Strengthen accessibility, compliance, trust and resilience 
• Enable data‑driven transformation that can scale across teams and channels

CACI’s integrated model blends service design, research, data, engineering and delivery to translate insights into meaningful operational change. Organisations across complex, high‑stakes environments rely on CACI to redesign, modernise and optimise the services that matter most, improving experience, reducing cost‑to‑serve and accelerating performance through practical, evidence‑led transformation. 

Organisations in complex, high‑stakes environments work with CACI to address root‑cause issues across their services, improving experience, reducing operational cost and driving performance gains that hold up in delivery.

Contact our team to get started.

Stay tuned for the next blog in our service design series, exploring the importance of discovery and leveraging insights for operational change. 

This year’s MCA Food to Go conference unveiled the key growth drivers, future trends and exciting developments shaping the sector. It highlighted everything from innovative technology and formats to trendsetting menus and marketing, ultimately exploring how successful brands are navigating market challenges.

At the conference, I showcased transaction trends and growth opportunities emerging in 2026 based on three months of data from CACI’s Brand Dimensions dataset. By tracking 30+ food to go brands from November 2025 to January 2026, I assessed the trends and opportunities fuelling growth questions this year. 

Here is what the data revealed. 

Food to Go transaction trends & growth opportunities in 2026

The findings showed: 

• +6% YoY revenue growth in the Cafés & Coffee Shop market 
• A slight decrease in Quick Service Restaurant (QSR) transactions, but a slight increase in Average Transaction Value (ATV)  
• Transactions and revenue dropping across the wider F&B sector

Which brands are leading industry trends in 2026?

From the 30+ up-and-coming and major players in the food to go sector tracked, I identified the leading brands as those achieving YoY growth above inflation and sorted them by increase in growth percentage. 

Premium healthy lunches: Atis & Farmer J

Consumers continue to prioritise premium healthy lunches this year.  

The leading brands were Atis, growing 140%, and Farmer J, growing ~30%. Atis’ skyrocketing growth is driven by the opening of a third new space in the last year. While substantial and impressive, it is the smallest brand in CACI’s Food to Go tracker, meaning the overall GBP shift in the market is small.  

The largest share of the customer mix for these brands comes from CACI’s Acorn profiles Prosperous Professionals at 15% of spend followed by Up-and-coming Urbanites at 11%. 

For new entrants, the challenge to growth is proving value in each transaction, precise targeting and mission expansion without undermining the brand or cannibalising sales. 

Continued growth in chicken QSR: Popeyes, Wingstop & Slims

Consumers continue to seek indulgence and novelty. In the chicken QSR sector, our findings concluded Popeyes grew ~30%, Wingstop ~20% and Slims ~9% (who were +46% in the first quarter of the year). While this may counter the premium healthy lunch trend, consumers are finding ways to balance health-conscious choices with indulgent ones. 

Caffeine & matcha on the rise: Blank Street & Grind

Both Blank Street and Grind grew over 20%, indicative of the brands’ innovative products, strong social media presence and matcha-led menus. These brands have evidently appealed to younger, experience-driven consumers by creating excitement through their product innovation. 

Established brands are driving growth by harnessing loyalty 

The biggest takeaway is that while new entrants win on excitement, established brands win on loyalty.  

New brands have brought excitement, and with that, percentage growth, but most saw YoY growth rates slow across the year. Meanwhile, more established brands like Pret a Manger, Costa, Starbucks and McDonald’s saw stronger growth in the latest quarter. When assessing actual pounds versus percentage growth, established brands are back growing and seeing very substantial sales gains. This reiterates the impact of loyalty on long-term growth.  

The formula of the current state of the market then becomes:  

Excitement = short-term percentage growth. Loyalty = long-term monetary growth. 
 
New brands, social media influence and new cuisine are fuelling excitement. Loyalty is driven by familiarity, perceived value, brand resonance and communication. Brands that can achieve a sweet spot between both are poised for sustainable growth. However, our findings suggest tension between excitement and loyalty. This prompts brands to reflect on how to maintain excitement or build customer loyalty.  

Four strategies to drive growth in a tough climate

1) Having the right products in place 

Brands must understand how to appeal to existing customers and excite new ones. Product and menu innovation should be strategically considered to open new missions and tailor to the right locations, dayparts and missions.

2) Getting the right space

While growth can be achieved by acquiring new spaces, established brands are always optimising their spaces to reach the right people, in the right place, at the right time. This is why some brands are shifting to drive-through locations as town centres decline and why many have opted to offer FMCG products in the chilled sections of supermarkets.

3) Appealing to customers through the right message 

Tailored content sent to the right target group at the right time with the right incentive is critical to success. 

4) Delivering with the right service

Profitably staffing each location, determining which locations will best suit trialling self-service kiosks and avoiding alienating or upsetting customers who value your brand’s personal service are critical considerations.

This is often easier in the new entry “excitement” phase, but new and established entrants must constantly evaluate that they have the right mix of these factors to remain relevant in a rapidly changing market. Each of these strategies has a ‘people, place and time’ lever that can be pulled to maximise growth by leveraging customer loyalty.  

How CACI’s Brand Dimensions can help your Food to Go business thrive

With so much complexity in the food to go sector, brands need more than just internal customer data to keep on top of the mix. Supplementary market data through CACI’s Brand Dimensions can help you answer your growth questions, combining the right data with the right tools to project long-term growth through the right mix of products, services, places and messaging. 

Highly detailed, timestamped transaction data is at the heart of Brand Dimensions, indicating anonymised customers and specific outlets to infill any data gaps and gain unique performance and competitor outlet insights.

When combined with anonymised mobile activity data and demographic classifications, it creates a cohesive base to address the people, place and time levers driving growth. This can also be topped off with lifestyle attributes linked to those demographics, competitor location data and competitor sentiment data. 

Through this, businesses can better prepare for the future by understanding consumer behaviour at brand level. 

Although Brand Dimensions is typically tracked on a monthly basis, these findings have been summarised quarterly for this blog.  

If your brand could benefit from these data insights, book a Brand Dimensions demo with us. 

As the NHS evolves, understanding the characteristics of local populations and the patients it serves has never been more critical. Demographic data ensures healthcare teams and organisations can anticipate demand, identify at-risk patients and create services that respond to patients’ needs rather than operating by assumption. It also reduces unnecessary costs and improves the efficiency of stretched NHS budgets. 

Healthcare demographics in isolation are not enough, however. To understand patient demand, cost and service usage, healthcare services require a combination of demographic data and patient-level costing information. This is where CACI’s integrated data solutions— Synergy and Acorn— make all the difference in your ability to unlock unparalleled insights into who uses healthcare services, why and when they are most likely to. 

Importance of demographic data in healthcare

The importance of demographic data in healthcare is rooted in its ability to help the NHS shift its focus from delivering proactive to preventative care. With healthcare demographics ranging from age and gender to ethnicity and socioeconomic status, these insights combine to become a powerful tool to improve care quality and system efficiency.  

Healthcare authorities rely on demographic information to:  

• Assess population health, including birth rates, mortality and life expectancy. 

• Understand population structures and changes, including ageing populations, reproduction patterns or areas with high levels of deprivation.  

• Plan and forecast healthcare facilities, ensuring the right services are in the right places with the right capacity. This will not only improve access but prevent the costly over- or under-provision of services and encourage cost optimisation into the future. 

• Evaluate the effectiveness of public health interventions by tracking how various populations or patient groups respond to programmes or services. 

Demographic data essentially underpins everything from GP capacity planning to urgent care demand modelling. Inequalities and vulnerable communities can be identified to determine where preventative interventions will make the greatest impact.  

CACI’s Acorn geodemographic segmentation helps teams achieve this by unveiling social, economic and behavioural insights that shape health outcomes. This enables earlier intervention and more precise targeting from the NHS through more effective, efficient engagement and staying focused on service delivery, prioritisation and long-term planning while reducing avoidable costs.

Why demographic data matters for preventative care

With the NHS increasingly focusing on prevention rather than cure, demographic data will be critical to making this shift a reality. Intervening earlier will reduce avoidable admissions, improve outcomes and alleviate the pressure that frontline services face. This shift also significantly reduces costs, as preventing deterioration is more cost-effective than treating advanced illness.  
 
Through accurate, granular demographic data and insights, healthcare teams and organisations can more confidently focus on prevention by:  

• Identifying high-risk patients before they reach services: Demographic data enables healthcare teams and organisations to understand where potentially vulnerable populations (such as older patients) live or where social isolation may be higher.  

• Designing targeted interventions that address root causes: Demographic data reveals environmental and behavioural factors that drive poor health, which enables effective outreach from healthcare teams and organisations.  

• Allocating resources more effectively: By clearly understanding the needs of the local population and anticipating future demand, workforce planning and service design can become more cost optimised. 

• Enhancing patient outcomes: When services are designed based on real-world demographic data, patient care becomes more personalised, accessible and impactful.  

This aligns with the NHS’ 10 Year Health Plan for England, which sets out how new technologies, medicines and innovations will transform patient care. Three major shifts within this plan include:  

• From hospital to community: More care at people’s doorsteps and in their homes  

• From analogue to digital: New technology to support staff and simplify care management  

• From sickness to prevention: Reach patients earlier and encourage healthier decision-making. 

Why combining demographic and costing data reduces healthcare costs

The context provided by demographic data is augmented by patient-level costing data, as it offers a deeper understanding of cost drivers and service utilisation. CACI delivers this through the integration of Synergy, our patient-level costing solution, and Acorn, our postcode-level population segmentation.  

Together, these datasets help healthcare teams answer critical questions surrounding costs, such as:  

• Which cohorts are over utilising services and driving higher costs?  
• Can they be treated in a more efficient way?  
• What opportunities are there to move from cure to prevention?  
• What does future demand look like? 

Informed patient costing and behaviour decision-making

Combining costing and demographic data enables team to:  

• Identify areas of high cost and service usage  
• Explore different treatment and prevention paths to deliver services more efficiently  
• Understand patient costs and behaviours by postcode and demographic  
• Explore cost drivers versus the segmentation of the population to which costs are attributed  
• Tackle specific health issues such as obesity and smoking where they are most prevalent  
• Forecast future demand for healthcare services and act accordingly. 

If you need a more comprehensive understanding of who uses your healthcare services, why they use them and the cost implications arising from each cohort of the population, contact us today to find out more.  

What is subscription fatigue?

Subscription fatigue refers to consumers’ deteriorating interest in a subscription or service, resulting in their cancellation. This is often due to feeling overwhelmed by their numerous subscriptions or losing sight of the value each subscription brings. It goes hand-in-hand with churn, where uncertainty, mental exhaustion and subscription overload leads to diminished satisfaction with the subscription experience.  

What is causing subscription fatigue? 

With the ever-increasing number of subscriptions consumers have, decision overload is inevitable. Mounting costs, managing multiple accounts and the pressure to maximise each subscription all contribute to declining satisfaction. When value is unclear, questioning a subscription’s worth surfaces. 
 
Value must therefore be constantly reiterated and subscriptions models must be flexible enough to meet consumers’ unique needs. Signs of fatigue must be identified early on and actions to mitigate fatigue must be taken.  
 
CACI understands the challenge: people want convenience and personalisation, but they also want affordability and control. 

Over-subscription

Subscribing to and managing multiple subscriptions can be mentally draining. The simple fix in consumers’ minds is typically to unsubscribe, even if the service itself is not the problem.

Inability to reinforce value

If consumers feel that they are paying for a service they do not use, the feeling will quickly lead to subscription fatigue. When it comes to subscriptions, low perceived value or service underutilisation are often the driving factors behind cancellations. If value cannot be demonstrated, even your most loyal subscribers may be lost.

Lack of flexibility

When feelings of frustration or overwhelm creep up among the plethora of subscriptions a consumer has, offerings that do not feature flexibility are likely the first to go. Rigid plans will not appeal to already-fatigued consumers. If subscribers feel as though they maintain control over their subscription, they will be easier to retain and keep satisfied. Establishing tiered memberships, flexible pricing, pause options, add-ons or various payment plans can help rectify this.  

How can brands fight subscription fatigue? 

Subscription fatigue may be inevitable within an oversaturated subscription landscape, but understanding the origin of fatigue and the strategies that your organisation can implement to combat this will make a tremendous difference. Leveraging predictive modelling, customer insights and data and segmentation are among the most effective approaches.

Use predictive modelling

AI-driven predictive models forecast customer behaviours and guide the next best actions. Proactive retention and upsell strategies can therefore be developed, resources can be prioritised towards customers with the highest potential and a measurable performance uplift can be seen in metrics like LTV, conversion and engagement. 

Focus on customer insights 

By integrating transactional, behavioural, attitudinal and external data, CACI helps you attain a comprehensive view of your subscribers that will improve your decision-making across acquisition, retention and product development. 

These insights help you:

• Build strategic confidence by grounding it in real customer behaviour  
• Identify high value customers 
• Understand churn drivers 
• Uncover growth opportunities 
• Benchmark performance against your competitors 
• Better understand your position within the market  
• Spot underperforming segments or categories where competitors are gaining share

Grounding strategic decisions in external evidence also improves internal storytelling and stakeholder alignment. 

Focus on acquisition through segmentation

Poor segmentation drains budget by targeting low-value audiences. Without precise targeting, campaigns miss the mark and media mix decisions lack data-driven optimisation.  

CACI’s bespoke segmentation capabilities give you intuitive, data-rich segments reflective of the diversity of your customer behaviours, values and attitudes. This enables personalised marketing and CRM journeys, enhances media targeting and campaign ROI and bolsters strategic planning by revealing which segments to grow, retain or re-engage across three core areas: 

• Data: Curated, high-quality foundational data with diverse input lenses and no personally identifiable information (PII).  
• Segment simulation and validation: Segment-level data layer, validation to assess predictive accuracy with guardrails in place and performance audited.  
• Persona enhancement: Defined by segment characteristics and enriched with psychological and behavioural traits, every step is tested by experts to ensure it is structured, auditable and iterative.

Through this tailored approach, CACI equips you with segmentation that reflects your customers, leading to better decision-making, campaigns and long-term growth.

How CACI can help you overcome subscription fatigue

CACI helps subscription brands unlock growth by transforming fragmented customer data into actionable insight. Through advanced data science and AI-powered decisioning, we support acquisition, retention and personalisation at scale. 
 
We can help you:

• Build deeper customer understanding and target the right audiences 
• Forecast behaviour, improve retention and justify investment 
• Turn insights into action across media and CRM 
• Simplify data and bridge capability gaps

To find out more about how your organisation can successfully overcome subscription fatigue, get in touch with us.

“Our digital transformation is failing because it is fragmenting”. This was the defining statement from a recent roundtable with C-suite leaders from global enterprise organisations, met with nods and echoes of agreement across the room.  

Many of these leaders went through mergers and acquisitions, regional expansion and business proposition changes. The end result was the same: hundreds of disconnected tools and platforms, masses of digital sprawl, rising inefficiencies, disjointed customer experiences and a tangled web of overlapping technologies.  

If this sounds familiar, you are not alone. Over 40% of organisations now operate four or more separate systems, and while multiple platforms can signal maturity, the lack of integration between them often introduces operational friction—slowing delivery, increasing costs, limiting personalisation and constraining AI adoption.  

This is where ecosystem orchestration becomes strategically imperative in designing how your entire digital ecosystem works together. 

What is ecosystem orchestration?

Ecosystem orchestration is the discipline of designing, connecting and governing all digital platforms, experiences and data as a unified system rather than a disparate collection of isolated tools and journeys. It defines how these technologies should work together to deliver efficient operations, connected customer experiences and AI-ready foundations. 

For most organisations, this ecosystem spans experiences, content, data and their supporting platforms. 

Ecosystem orchestration focuses on: 

• How data flows across your CRM, CDP, CMS, analytics and personalisation 
• How experiences are assembled across channels, regions and brands to make them seamless 
• How your platforms integrate, scale and evolve alongside your organisation  
• How governance, security and performance are embedded by design. 

What is digital fragmentation? 

Fragmentation rarely appears as a single problem. Instead, it develops gradually as new platforms, regions and business needs are layered on existing digital estates. If one layer is weakened, it reduces the effectiveness of the entire structure and ultimately damages both your business outcomes and perceived value to your customers. This inefficiency prevents your organisation from reaching its potential.

Fragmentation tax: The unwanted cost of disconnected systems

When digital ecosystems grow without orchestration, the impact compounds over time. You may start to see: 

Operational inefficiencies rise 

When your teams jump between multiple systems, duplication and manual work skyrocket. Delivery slows and administrative load increases. 

Maintenance outweighing innovation 

Technology teams spend more time maintaining integrations, bug fixing and patching software than building new value-generating features. 

Data reporting inconsistencies

Inaccurate data creates reporting inconsistencies and data teams spend more time reconciling data than generating insights.  

Personalisation becoming impossible

Disconnected CMS, CRM and data platforms mean your organisation does not have a single customer view. This leads to segmentation being non-existent or superficial. 

AI-readiness severely constrained

AI requires unified data, modern architecture and consistent governance. Poor data hygiene and siloed insights create unstable foundations for predictive modelling and limit automation at scale. 

Brand and experience consistency breaking down

Multiple regions and brands lead to inconsistent UX, duplicated content and disconnected customer journeys. 

Costs quietly increasing

Duplicated platforms, unnecessary licences, security vulnerabilities and inefficient workflows inflate spend. 

Leadership is struggling to make data-driven decisions

Fragmented data erodes trust, making it harder for leaders to drive strategy or prove ROI. 

What ecosystem orchestration will enable

Fragmented digital estates can derail even the most ambitious digital transformation plans. Ecosystem orchestration is the solution to ensuring your business is future-ready, laying the foundation for scalable experiences, operational efficiency and AI-ready growth. 

If the challenges described here feel familiar—from disconnected journeys to rising operational effort—it may be time to reassess how your ecosystem is designed to work together.  Speak to our team about simplifying your digital ecosystem. 

What is subscription churn?

Subscription churn refers to the number of subscribers or customers that stop their subscription with your organisation within a specific period, measured against the overall customer base. Churn can be interpreted in several ways and organisations may have their own method of calculating churn depending on what suits them. However, the principle remains the same: churn shows how effectively you retain customers. 

A high churn rate means that customer retention may present difficulties, whereas a low churn rate is indicative of successful retention. 

Why is churn important in the subscription sector?

Subscriptions have embedded themselves into consumer behaviour, with 4 in 5 UK adults now signed up for at least one subscription service and nearly one-third subscribed to a subscription box delivery service. While this shows how appealing the convenience of subscriptions is, cost is a key barrier. As the cost of living rises, subscriptions are often the first thing customers look to cancel. 

In the subscription sector, churn directly affects revenue predictability, customer acquisition, lifetime value (LTV), growth and brand reputation. Even small churn rises can lead to longer-term financial instability. Understanding churn is therefore essential to uphold customer and subscriber satisfaction and retention. 

Types of customer churn

To mitigate churn, organisations must distinguish between its two types: voluntary and involuntary. Each provides a unique lens on customer behaviour and organisational performance, also requiring their own prevention and combative methods. 

Voluntary churn

Voluntary churn is when customers choose to end their relationship with a service or product. These are instances when they no longer recognise a service’s value, have opted for a competitor’s service, can no longer afford the service or other considerations.

Involuntary churn

Involuntary churn happens when customers unintentionally end their subscription with a service due to reasons beyond their control. Financial pressures are one of the most substantial driving forces behind churn, especially for discretionary spend on products that are optional rather than essential. 

Average churn rates for subscription sector

Customer churn can be expected to an extent but determining the amount of churn that your organisation can withstand and the maximum length of time in which losses can be made up will be critical for long-term growth. 
 
Churn rates also vary by customer segments. Through Acorn, our geodemographic segmentation, we found that younger Acorn groups like Tenant Living might avoid long-term subscriptions as cost is a hugely influential factor in their circumstances. Customers within Acorn’s Commuter Belt Wealth group might enjoy the convenience of subscriptions, but busy and irregular schedules can complicate commitment. We also found that subscription drop-off after discount periods is common across different segments. 
 
By recognising these behavioural differences, your subscriber retention strategies can be more effective.

Subscription churn reduction

To counter the effects of churn, organisations may turn to offering incentives that attract price-sensitive customers who churn post-offer. While this may remedy the situation to an extent, the following approaches will bolster your understanding and reduction of churn by combining proactive and reactive strategies with data. 

Bespoke segmentation

Poor segmentation leads to wasted budget on low-value audiences. Campaigns miss the mark without precise targeting and media mix decisions lack data-driven optimisation. 

CACI’s bespoke segmentation capabilities enable you to create intuitive, data-rich segments reflective of the diversity of your customer behaviours, values and attitudes. This powers personalised marketing and CRM journeys, improves media targeting and campaign ROI and supports strategic planning by revealing which segments to grow, retain or re-engage in three capacities:

• Data: Curated, high-quality foundational data with diverse input lenses and no personally identifiable information (PII). 
• Segment simulation and validation: Segment-level data layer, validation to assess predictive accuracy with guardrails in place and performance audited. 
• Persona enhancement: Defined by segment characteristics and enriched with psychological and behavioural traits, every step is tested by experts to ensure it is structured, auditable and iterative.

Predictive modelling

Through predictive modelling, AI-driven models forecast customer behaviours and guide the next best actions. This enables proactive retention and upsell strategies, prioritises resources towards customers with the highest potential and drives measurable performance uplift in metrics like LTV, conversion and engagement. 

Customer insights

CACI’s data offers a holistic view of customers that helps organisations better understand churn drivers. Customer insights are divided among: 

Core demographics

• Affluence 
• Disposable income 
• Age band 
• House size 
• Occupation 
• Number of children

Key behaviours

•  Price sensitivity 
• Loyalty 
• Motivated by premium/value 
• Convenience 
• Environmental attitudes

Digital behaviours

• Posts/reads ratings & reviews 
• Social networks 
• Influencers 
• Newspaper & magazines read

Brand engagement

• Websites visited 
• Loyalty cards 
• TV channels 
• Newspapers 
• Streaming sites 
• Magazines

An understanding of customers’ lifestyles is enriched through additional layers of their interests and hobbies, lifestyle attitudes and shopping behaviours. For subscription brands, this reveals not just who your customers are, but why they subscribe. Our insights showed that customers tend to be mindful of ethical and environmental issues and are concerned about their online security. They also tend to focus on provenance when it comes to shopping, considering where products are made/grown, the value they place on quality goods and those that make life easier. These motivations influence a subscription’s perceived value, a customer’s loyalty to a subscription and brand and what may sway their thought process in terms of staying or cancelling. 
 
Through this holistic view, you can also benchmark your organisation’s performance against competitors to gain a clear view of market position and competitive dynamics. This helps you understand where you stand in the market, who you are winning with, where you are losing and why. It identifies underperforming segments or categories where competitors are gaining share, enabling focused interventions. It also supports internal storytelling and stakeholder alignment by backing up strategic decisions with external evidence.

How CACI can help you navigate churn reduction

CACI helps retail subscription brands unlock growth by transforming fragmented customer data into actionable insight – driving acquisition, retention and personalisation at scale through advanced data science and AI-powered decisioning. 
 
We can support you in:

• Building deeper customer understanding and targeting the right audiences 
• Forecasting behaviour, improving retention and justifying investment 
• Turning insights into action across media and CRM 
• Simplifying data and bridging capability gaps

To find out more about how your organisation can successfully navigate churn reduction and strengthen customer loyalty, get in touch with us. 

Understanding your population and the factors contributing to their health is key to delivering effective, fair healthcare. This understanding is particularly important in helping the NHS shift its focus from reactive to preventative care. Through healthcare demographics, at-risk individuals can be identified and reached out to earlier, reducing the burden on NHS services and improving outcomes for patients and communities through a data-led approach to healthcare. 

Healthcare demographics only paint part of the picture, however. A combination of demographic data with patient-level costing information allows healthcare services to understand patient demand, cost and service usage. This is where CACI’s integrated data solutions can make the greatest difference: supporting you in unlocking unparalleled insights into who uses healthcare services, why and when they are most likely to. 

What are healthcare demographics and who are they for?

Healthcare demographics are the characteristics of a population that influence health outcomes, service usage and care needs. The characteristics range from basic identifiers such as age and location to more complex determinants such as income, ethnicity and lifestyle factors. Combining these characteristics ensures clinicians, commissioners and public health teams can understand who is most at risk and why. 

In England, Wales and the Isle of Man, the NHS uses Personal Demographics Service (PDS), a database of all registered NHS patients who have ever sought help from NHS clinics, to collect data. It stores: 

• Name 

• Address 

• Date of birth 

• Contact details 

• Registered GP 

• Nominated pharmacy 

• NHS number. 

This supports direct care– clinicians or public health teams facilitating direct client or patient interactions– and non-direct care– information governance and data sharing.

Examples of demographic data in healthcare

Demographic data is used in healthcare in a range of scenarios, from frontline care to strategic planning. Some common examples of demographic data in healthcare include: 

Adding new patients

Ensuring the accuracy and completion of new patient registrations helps maintain continuity of care and supports safe clinical decision-making. 

Managing patient information

Keeping demographic data up to date ensures communications reach the right patients and minimises missed appointments, especially if or when patients change addresses or transfer GPs. 

Designing targeted interventions

Analysing demographic and social characteristics cohesively enables healthcare teams to better identify high-risk populations or cohorts and devise targeted, preventative interventions before conditions escalate.

Why healthcare demographics matter now more than ever

Demographic data and insights have become strategic assets in offering integrated, preventative models of care. Through clean, connected data, healthcare teams can more effectively shift from sickness to prevention, gaining: 

• A granular understanding of local health needs: Social determinants from housing quality to the environment can be mapped alongside clinical data to reveal the root causes of poor health and showcase where early prevention can have the greatest impact. 

• Targeted interventions that reduce admissions: Identifying at‑risk individuals early on allows services to reach out before they reach crisis point, alleviating pressure on urgent and emergency care. 

• Data‑driven planning and resource allocation: Real‑world population data supports smarter workforce planning, service design and long‑term transformation strategies that anticipate future demand rather than react to it. 

This directly supports the NHS’ 10 Year Health Plan for England that will leverage new technologies, medicines and innovations to improve patient care, with three major shifts within this plan including: 

• From hospital to community: More care at people’s doorsteps and in their homes 

• From analogue to digital: New technology to support staff and simplify care management 

• From sickness to prevention: Reach patients earlier and encourage healthier decision-making. 

How CACI’s Acorn & Synergy help the NHS enhance preventative care

Data sits at the heart of everything we do at CACI. If you need a more comprehensive understanding of who uses your healthcare services, why they use them and the cost implications arising from each cohort of the population, CACI can help. 

We deliver this by integrating its products and datasets. Through Synergy patient-level costing solution, Acorn, our geographic segmentation of the UK’s population at postcode level and Wellbeing Acorn, a deeper dive into Acorn’s population data to analyse social, health and wellbeing characteristics at postcode level, we can help you unlock unparalleled insights into who uses healthcare services, why and when they are most likely to. 

This enables teams to ask critical questions such as:  

• Which cohorts are over utilising services and driving higher costs?  

• Can they be treated in a more efficient way?  

• What opportunities are there to move from cure to prevention?  

• What does future demand look like? 

As a result, the NHS can act earlier through more effective and efficient engagement and focus on service delivery, prioritisation and long-term planning. 

The features

• Industry leading data: CACI’s data solutions are proven and widely utilised across public and private sector organisations  

• Supported by experts: Our data teams will support you in creating insights, interpreting them and gaining the information you need  

• Combine your data: Easily identify cohorts driving high costs, highlight service demand by area and demographic and understand cost drivers  

• A complete picture: From economic, income and employment factors to the impact of diet, smoking, alcohol and exercise on demographics and cohorts. 

If you are already using Synergy, you can simply add Acorn’s data to your own costing data.   
 
Contact us today to find out more about our healthcare data analytics solutions and how CACI’s data-led approach to healthcare can make a difference for your organisation.  

Dissatisfaction with a marketing platform rarely arrives suddenly. It tends to build gradually through small frustrations, workarounds and compromises that feel manageable on their own, but increasingly costly when they accumulate. 

Enterprise marketing platforms have not necessarily become weaker. In many cases, they are more powerful than ever. What has changed is how you are expected to operate as a marketing leader:  the speed at which you must respond, the need for technology to directly translate into measurable outcomes and the pressure to do more with less. 

This shift has prompted many senior leaders to ask a different question. Instead of “Is our platform capable?” it has become “Is it still fit for how we need to operate today?”  

In this blog, we uncover the driving factors to that question, from cost and operational complexity to real-time capability and drag, and why many organisations are revisiting their platform architecture.

Why enterprise marketing platforms are being re-evaluated now

Several pressures are converging at once: customer expectations continue to rise, particularly around relevance, timing and the consistency of communications across channels. At the same time, teams are being asked to move faster, demonstrate clearer value and operate with leaner resources. Against this backdrop, platforms designed for a previous era of marketing are being stretched in new ways, particularly as you try to support real-time journeys, unified customer data and faster campaign development. Data ingestion is increasingly event- and profile-based, enabling real-time digital conversations. 

These tensions are most obviously felt during moments of operational change: renewal cycles, organisational shifts or attempts to introduce new real-time use cases. What may once have been accepted as the cost of scale can start to feel like complexity rather than capability. 

When cost becomes a strategic question

Rising costs are rarely the starting problem. The pressure tends to surface around licence renewals, expanding data volumes or the addition of new modules that promise incremental capability. Over time, the cost of operating and maintaining the platform can begin to grow faster than the value it delivers.  

Many enterprise marketing platforms were originally adopted on the promise of breadth, future-proofing and long-term stability. Licensing models expanded over time, new modules were introduced and capabilities were layered in to support growth. That made sense when scale and consolidation were the priority. Today, however, operations are expected to have faster cycles and leaner teams, where value is judged less by the number of features available and more by how quickly features translate into outcomes. You may still be using the platform extensively, but usage alone is no longer enough. 

The harder question is whether that usage is translating into impactful outcomes: faster speed to market, more relevant experiences and the ability to respond while customer intent is still live. When incremental gains demand disproportionate effort or when specialist skills and parallel tools are required to unlock value, cost pressure becomes a strategic signal rather than a purely financial one.

The hidden weight of operational complexity 

As platforms grow in scope, complexity often follows. What may have started as a powerful central system can become a heavyweight environment that requires specialist expertise to operate effectively. While advanced querying, scripting and complex journey logic offer flexibility, they can also introduce dependency and bottlenecks, particularly if your teams are expected to move quickly. 

This operational overhead rarely appears in executive reporting, but it is felt day to day. Longer lead times, reliance on a small group of experts and limited ability for marketers to test and iterate independently all begin to slow momentum. Over time, the platform can feel like something your teams work around rather than something that actively enables them. 

When ‘fast enough’ is no longer fast enough

Speed has always mattered in marketing, but the threshold for what is considered acceptable has changed. 

In an environment shaped by real-time signals and event-driven interactions, delays of hours or even minutes can mean missed opportunities. Despite this, many marketing environments still rely heavily on batch processing, scheduled workflows and manual handovers between systems. 

When insight takes too long to become action, you are pushed into more reactive ways of working. Campaigns must be planned further in advance, personalisation lags behind behaviour and responsiveness becomes constrained by technology rather than strategy. 

Data fragmentation and orchestration limits

As your digital estate expands, data rarely lives in one place. Transactional systems, analytics platforms and engagement tools all play a role, but unifying them cleanly remains challenging. 

Many marketing platforms were never designed to act as the primary data layer. As a result, you may rely on connectors, middleware or separate data foundations to bridge the gaps. While workable, these approaches often introduce latency, instability and added complexity, particularly at scale. 

The impact is most visible in orchestration. When data is fragmented, journeys tend to become channel-led rather than customer-led, limiting your ability to deliver coherent experiences across touchpoints.

When friction becomes systemic 

Individually, none of these challenges are unusual. What matters is when they coexist. 

Cost pressure, operational complexity, slow execution and fragmented data tend to reinforce one another. As environments become harder to manage, extracting value becomes more difficult. As value becomes harder to demonstrate, scrutiny increases. Over time, you may find your teams becoming less able and less willing to push the platform in new directions. 

This is often the point at which conversations shift from optimisation to re-evaluation. 

A changing view of platform architecture

In response, many organisations are reassessing the role their marketing platform plays within the wider ecosystem. Rather than expecting a single system to do everything, there is growing interest in more modular, composable approaches that separate data, decisioning, orchestration and activation. 

This shift is not about chasing trends. It reflects a desire to align technology more closely with how you currently operate and how you expect to evolve over time. 

How CACI can help you optimise your marketing platform

The most productive platform conversations do not start with vendors or features. They start with clarity. 

If you are questioning whether your current platform still supports how your teams work, it may be time for a more structured conversation about fit, value and operational friction. 

To support this, we have created a short Marketing Platform Health Check to help you sense-check whether your current setup still fits how you operate today. It highlights common friction points and provides a structured way to assess where further investigation may be valuable.

The Care Quality Commission’s single assessment framework was introduced with one clear purpose: to raise care standards across the sector. A goal shared by every provider. Delivering this level of change at scale is complex, and while the ambition is right, the transition has brought challenges such as registration delays, inspection backlogs, and increased documentation demands. These issues reflect the size of the task, not a lack of commitment from the regulator. 

The CQC is actively working to address these challenges following independent reviews, but providers still face operational and financial pressures. Understanding these pressures, and planning for them, can help providers stay focused on what matters most – delivering outstanding care. 

Registration delays remain a challenge

Registration delays continue to impact providers as the new framework beds in. For example, CQC performance data shows 54% of pending registration applications exceeded the 10-week target at the end of 2023–24, up from 22% the previous year. Industry reports suggest applications can take up to six months to process. These delays often mean new care homes sit empty and funding is held back until registration is confirmed, adding pressure for providers and the regulator alike.

While the CQC’s intention is never to stifle care capacity nor quality, these delays highlight the challenge of balancing rigorous standards with the urgent need to bring new services online.

Re-inspection backlogs create prolonged uncertainty

Inspection backlogs add further complexity. According to the Homecare Association, 70.3% of community social care providers either have never been rated or have ratings that are 4–8+ years old, up from 60% in August 2024.

The average wait for re-inspection after a ‘requires improvement’ rating is now 360 days — a 153% increase since 2015. For homecare, uninspected locations rose 64% in 14 months between June 2024 and August 2025 from 2,879 to 4,727. At current inspection rates, the backlog will continue to grow.

These delays have real consequences for providers, but they also affect care seekers and commissioners who depend on current ratings to make confident, informed choices. When ratings are outdated or missing, it can restrict options and make decisions harder. Which is clearly something the CQC is committed to improving through transparency and timely assessments. At the same time, these challenges underline the scale of the task facing the CQC as it works to deliver a more consistent, modern regulatory approach.

Framework complexity reflects ambition

The single assessment framework introduced significant complexity, requiring structured approaches to evidence and documentation. Independent reviews have highlighted these challenges.

Professor Sir Mike Richards’ review stated the framework “is far too complex and, as currently constituted, does not allow for the huge differences in the size, complexity and range of functions of the services that CQC regulates.” Initially, the framework required six evidence types for each quality statement, up to 204 evaluation points, though this was simplified in December 2024.

Even with revisions, compliance remains demanding. This reflects the CQC’s ambition to raise standards and drive best practice across the sector. While the process can feel challenging for providers, the intention is not to penalise or create unnecessary burden, the ultimate goal is to ensure safer, higher-quality care for the people who rely on these services. Providers therefore need systematic ways to organise evidence and maintain audit trails to align with this shared mission.

Understanding the broader impact

While £50,000 is the maximum penalty for compliance failures, the bigger challenge lies in maintaining standards amid reduced inspections and extended delays. Inspections have fallen sharply from around 16,000 in 2019/20 to approximately 6,700 in 2023/24, making progress harder to evidence.

Financial impact goes beyond fines: in the residential care home sector, Knight Frank research shows that inadequate-rated homes operate at profit margins of around 22%, compared with 34% for outstanding-rated homes – a significant disparity that compounds over time. Occupancy rates also drop when ratings remain outdated, as families and commissioners seek higher-rated alternatives.

In a competitive market, this new framework can ultimately have a positive impact by creating more robust standards and building trust among care seekers. It also provides a clearer roadmap for providers needing improvement, helping them demonstrate progress and raise their potential over time. While it may not feel that way pre- or post-audit, the intention is to lift quality across the sector. These pressures highlight why proactive compliance planning is essential, not just for providers’ commercial stability, but to support the shared goal of improving care quality.

Regulatory improvements underway

The CQC acknowledges these challenges and is implementing improvements following reviews by Dr Penny Dash and Professor Sir Mike Richards. These include stabilising its regulatory platform, upgrading the provider portal, and refining processes to make compliance smoother.

Dr Dash emphasised that effective regulation “can identify failings in the delivery of care and assist providers in making improvements,” while Richards recommended a fundamental reset, noting success depends on recruiting and training sufficient inspection staff with sector expertise.

These steps will help, but the single assessment framework, with its 34 quality statements and comprehensive evidence requirements, remains central to compliance. Providers will still need robust systems to organise evidence, track compliance, and maintain audit trails.

How Certa supports providers through regulatory complexity

Certa helps providers turn compliance challenges into manageable processes:

Streamlined evidence preparation: Generate comprehensive inspection-ready reports quickly, reducing stress and saving time.

Real-time compliance monitoring: Track essential checks like DBS, right-to-work, and care plan reviews, with alerts to prevent non-compliance.

Communication logs: Capture compliments, complaints, and key interactions with full audit trails for inspection evidence.

Advanced medication management: Electronic MAR integrated with NHS Medicines API ensures accuracy and visibility, with alerts for missed doses.

Conclusion

Beyond compliance, Certa gives providers a competitive edge. By combining regulatory tools with care planning, scheduling, and family engagement in one platform, Certa helps providers stay compliant, protect revenue, and deliver exceptional experiences that set them apart in a crowded market. Regulatory complexity doesn’t have to be a barrier – with the right systems, providers can focus on what matters most: outstanding care.

Discover how Certa can enhance your regulatory readiness and operational excellence at: www.caci.co.uk/software/certa

In my first blog of this two-part series, I broke down the five automation metrics and principles I rely on most to help leadership demonstrate value. This second blog builds on that thinking. In my e-book, ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I explained that one of the biggest challenges that network and operations leaders face today is making every change safe. 

Automation is not just about efficiency, but maintaining control within modern networks that are dynamic, distributed and tightly-connected to cloud platforms and third-party services. While automation is essential, speed without control creates risk. By unifying the three capabilities of assurance, observability and lifecycle management, it becomes possible to execute network changes in a safe and repeatable way.

Assurance: Validate before and after every change

For me, assurance is the foundation. Validate every change is safe and compliant before it goes live, then confirm it behaves as intended after deployment. Continuous validation before and after every change is now expected, helping to ensure changes are safe and compliant. Streaming telemetry and service mesh architectures provide real-time visibility, making it easier to spot issues and respond quickly

How to implement assurance:

• Define policies as code and embed them in your pipeline. 
• Run intent checks to catch misconfiguration and drift early. 
• Use change windows that include automated validation and safe rollback paths.

Outcome: Fewer failed releases and emergency fixes and better audit outcomes because evidence is generated as part of normal work. 

Observability: Real insight from streaming telemetry

In my first blog, I covered MTTR and MTTD with the time it takes you to detect issues and restore normal service. Observability is what drives this. Move beyond static, device-centric health checks to provide continuous visibility across paths, services and users.

How to implement observability: 

• Stream telemetry from network and edge assets into a common model. 
• Use service mesh patterns where appropriate to trace requests end-to-end. 
• Align dashboards to service objectives, not individual devices. 

Outcome: Faster detection, clearer root cause and performance data that stakeholders can actually trust. 

Lifecycle management: Remove tech debt as you modernise

Teams often try to automate on top of legacy risks. Lifecycle management prevents that. You plan upgrades, renewals and retirements proactively to prevent new changes from piling risk onto legacy.

How to implement lifecycle management: 

• Maintain an accurate inventory and map controls to business risk. 
• Standardise on reference designs that are easier to secure and support. 
• Budget for renewal and decommissioning alongside new projects. 

Outcome: Lower exposure, simpler operations and a platform that adapts as the business evolves. 

How to implement a safe automation framework

To bring assurance, observability and lifecycle management together for safe automation, I recommend organisations consider the following best practices:  

1. Start with responsibility: Assign clear owners for providers and controls. Everyone should know who approves what. 
2. Use reference designs: Build simple patterns that map known threats to specific controls, then reuse them. 
3. Automate safely: Codify configuration and policy, prevent drift and escalate recovery with tested rollbacks. 
4. Adopt Zero Trust: Assume breach, verify access and enforce least privilege across sites and clouds. 
5. Strengthen monitoring: Track performance, changes, access and compliance in one place. 
6. Keep governance practical: Set standards that teams can follow, measure them and iterate. 

What to measure

To make progress visible and defensible, you can refer back to the core metrics from my e-book and previous blog:  

• Change success rate and rollback avoidance 
• MTTR and MTTD
• Compliance score and drift
• Latency and packet loss against service objectives.

These metrics will help you determine whether your automation is actually making change safer.  

Two quick wins for the first 30 days

If you want to quickly build momentum, I recommend: 

• Pre-change validation on one high-traffic service: Add automated checks for policy compliance and performance impact, then track the effect on change success rate. 
• Drift detection with weekly remediation: Choose a critical domain, enable drift alerts and close gaps to raise your compliance score. 

Where SD-WAN and SASE fit

At the edge, SD-WAN and SASE extend consistent policy and observability to every site. They simplify operations, support identity-led access that aligns to Zero Trust and reduce risks from technical debt and legacy systems so networks can adapt securely as business needs evolve. 

How we can help

In my work with clients, I see the same challenge time and again: network change needs to move faster, but it also needs to be safer and more predictable. At CACI, we help organisations bring structure, visibility and governance to complex networks so change can happen with confidence. 

We support teams in putting practical assurance and observability in place, improving lifecycle management and reducing configuration drift, without slowing delivery. That means fewer regressions, clearer accountability and a more predictable change pipeline.
 
If you’d like to explore how this approach could work in your environment, visit our Network Automation page to start the conversation with our specialists. 
 
You can also download my new Network Automation in 2026 eBook for a deeper dive into how assurance and automation work together to build resilient, future-ready networks. 

In my new e-book ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I uncover how network automation is no longer just about speed, but about reducing operational risk, strengthening compliance and stabilising services when the unexpected strikes. To meet the expectations of leadership, network automation must clearly demonstrate its ability to deliver on outcomes.  

This first blog in a two-part series breaks down five automation metrics and principles I rely on to help advise leadership: practical, executive-friendly and aligned to how boards evaluate resilience, risk and customer experience.

1. Change success rate and rollback avoidance 

What it is: This is the proportion of changes that complete as planned without causing incidents or requiring rollback. 
Why it matters: In my experience, this is one of the fastest ways to prove to leadership that automation is about increasing safety and predictability, not just throughput. 

How to improve:  

• I always begin with applying pre-change validation, policy gates and standardised reference designs that map controls to threats with simple, repeatable patterns. These give teams simple, repeatable patterns that map controls to threats. 
• Instrument your pipelines to capture change outcomes automatically.
• Assign clear ownership to execute each change and align teams.  

What good looks like: A steady rise in successful, first-time changes and a consistent fall in rollbacks over consecutive release cycles. 

2. Mean time to detect (MTTD) and mean time to repair (MTTR)

What it is: The time it takes you to detect issues and restore normal service. 
Why it matters: I find that detection and recovery are very important for leadership, especially because automation and observability deliver measurable business value. 

How to improve:  

• Stream all of your telemetry into a single view, then use intent checks to highlight drift or policy violations and automate first line remediation where safe.  
• Strengthen monitoring by tracking network performance, changes, access, compliance and security events.

What good looks like: Faster detection windows followed by runbook-driven recovery that is measured in minutes, not hours.

3. Compliance score and configuration drift

What it is: A combined indicator of how closely your estate aligns to policy and how far it strays from approved configurations. 
Why it matters: Boards and auditors need confidence that controls are enforced consistently across hybrid estates. 

How to improve:  

• Treat policies as code and run continuous checks.  
• Block non-compliant changes before they land.  
• Generate audit evidence automatically to save a huge amount of time.  
• Keep governance practical by setting clear standards, control owners and measurable policies. 

What good looks like: A rising compliance score with drift trending down. Exceptions are documented and time-boxed. 

4. Alert volume reduction

What it is: A measure of how many alerts actually correlate to meaningful incidents. 
Why it matters: High alert volume hides real risk and drains team capacity. 

How to improve:  

• Consolidate tooling, de-duplicate at the source, only measuring what maps to user or service objectives.  
• Safely automate by applying Infrastructure as Code and Policy as Code to prevent drift and speed up recovery.

What good looks like: Fewer alerts, higher signal quality and a clear link between alerts and customer impact. 

5. Latency and packet loss against service objectives

What it is: End-to-end performance measured against the targets that matter most for your services. 
Why it matters: User experience is the ultimate goal. Device health means little if transactions stall. 

How to improve:  

• Set service-level objectives (SLOs) for your priority journeys, instrument path visibility and factor network changes into performance reviews.  
• Adopt Zero Trust principles to assume breach, verify access and enforce least privilege.  

What good looks like: Stable or improving latency and loss for your top services, even during high change periods. 

How to get started 

I recommend teams start small when adopting these metrics, but take the following into consideration: 

1. Select two high impact metrics that you can measure today. 
2. Automate the collection and reporting so data is timely and trusted.
3. Share a simple scorecard with trend lines and short commentary.
4. Only add more metrics when the first set is stable. 

How we can help

In my work with CIOs, one of the biggest challenges I see is turning network automation into something that’s measurable, governed and trusted. At CACI, we help organisations align automation with business goals, reduce operational risk and create real clarity around performance and compliance. 

We bring proven architectures, practical operating models and clear measurement frameworks, so teams can track success rates, reduce configuration drift and improve incident response. We also help teams build simple, outcome focused scorecards that connect day-to-day network activity to executive priorities. 

If you’d like support establishing a metrics baseline or shaping an automation roadmap around the principles in this blog, visit our Network Automation page to learn more or get in touch with our specialists. 

You can also download my Network Automation in 2026 eBook for a deeper look at the frameworks and metrics that high performing organisations are using today. 

In the next blog in this series, I’ll explore how assurance, observability and lifecycle management work together to make every network change safe. 

Refactoring in cloud migration means making significant architectural and code-level changes to an existing application to optimise it for cloud environments. Instead of simply lifting and shifting a workload, refactoring restructures it to use cloud native services such as managed databases, containers, microservices or serverless computing. 

Common migration patterns include rehosting, re-platforming, refactoring, rebuilding or replacing. Refactoring sits in the middle of the modernisation scale, keeping the core application but improving internal structure, removing legacy dependencies, updating frameworks and unlocking new capabilities. 

This approach is growing in adoption, with a large percentage of enterprises now combining cloud migration with application modernisation to remain competitive. When done well, organisations can reap substantial benefits of refactoring from cloud elasticity and faster development to improved resilience and long-term cost efficiency, which this blog uncovers. 

Benefits of refactoring in cloud migration

Refactoring requires investment, but the long-term gains are often significant. In doing so, organisations can gain: 

Improved scalability and performance

By adapting applications to use cloud native components such as container orchestration, managed databases or asynchronous workloads, organisations can achieve higher performance and better resilience under load. 

Reduced long-term costs

Although refactoring may increase migration effort, it often leads to lower operational costs. Cloud-native services offer auto-scaling, pay-per-use pricing and more efficient resource consumption. Over time, this results in better financial performance than traditional lift-and-shift. 

Faster delivery and innovation

Refactored applications are usually more modular and easier to update. This supports continuous deployment, quicker releases and faster time to market, which are ideal for product teams and digital delivery. 

Lower technical debt and easier maintenance

Refactoring replaces old libraries, removes legacy components and reduces complexity. This improves stability and simplifies systems for engineering teams to maintain and enhance. 

Stronger security and compliance

Modern cloud architectures embed identity management, encryption, monitoring and audit controls. This makes it easier to meet regulatory requirements and improve security posture.

Future-readiness and flexibility

Refactored solutions adapt more easily to new technologies, cloud services and business requirements. They are better positioned for AI integration, data platform modernisation and future cloud strategies. 

Challenges of refactoring in cloud migration

Refactoring is one of the more advanced cloud migration strategies, which lends itself to complications. Some of the challenges to be aware of include: 

Higher upfront effort and cost 

Refactoring requires redesigning and rewriting parts of the application. This means more time and investment compared to rehosting or re-platforming. 

Complex transformation risk

Innate changes to architecture may introduce new bugs or operational risk. Without careful planning, live services may face disruption during cutover. 

Legacy constraints and dependencies

Some applications are tightly coupled or built on outdated frameworks, which makes refactoring more time consuming. Legacy systems may require major rework before they are cloud-ready. 

Risk of cloud provider lock-in

Cloud-native services offer significant value, but can complicate multi-cloud strategies. Organisations must balance innovation with portability requirements. 

Cloud skill gaps across teams 

Refactoring requires cloud architecture expertise, software engineering capability, DevOps skills and updated security practices. Many organisations are still building on skills in these areas. 

Delayed return on investment

Refactoring benefits increase over time. Stakeholders may expect instant cost savings, which can create pressure if results take longer to appear. 

Best practices for cloud migration refactoring

Refactoring is most successful when approached with structure and clarity. The following best practices can help reduce risk and improve outcomes: 

1. Carry out a complete application assessment

Review application dependencies, integrations, data flows, technical debt, scalability and risk. This helps map the complexity of the estate and segment workloads based on refactoring suitability. 

2. Prioritise the right applications

Focus refactoring on high-value workloads such as customer facing services, highly scaled systems or applications requiring innovation. Avoid refactoring low-value or soon-to-be-retired solutions. 

3. Create a clear business case and measurable KPIs

Define long-term success: improved performance, cost efficiency, error reduction, increased release frequency or reduced maintenance overhead. Tie each refactoring decision to a measurable outcome. 

4. Adopt cloud native architecture patterns

Use microservices, event-driven design, serverless functions, containers, managed data services, API gateways and infrastructure as code. CACI’s Cloud Engineering and Implementation Services helps organisations effectively adopt this. 

5. Embed security and governance from the beginning

Security must not be retrofitted. Implement identity and access management, encryption, logging, monitoring, network controls and compliance checks early.  

6. Invest in skills and organisational readiness 

Support DevOps adoption, cloud architecture upskilling and platform engineering capabilities. Consider establishing a cloud centre of excellence.  

7. Deliver refactoring in waves

Avoid large, risky transformations. Move applications into the cloud in phases: pilot, assessment, refactor, migrate, validate and optimise. This will reduce risk and increase confidence. 

Cloud migration with CACI

Refactoring during cloud migration can unlock scalability, performance, agility and long-term cost savings. However, success depends on having the right expertise, governance, cloud architecture and migration strategy. 

CACI helps organisations design and deliver modern cloud solutions through its 
Cloud Engineering and Implementation Services, including:  

• Cloud readiness assessments 

• Refactoring planning 

• Modernisation frameworks 

• Cloud native delivery. 

We also provide Platform Migration for complex legacy estates and Solution Implementation to build secure, scalable platforms for modern applications. 

If you are planning to refactor applications for cloud or considering a modernisation strategy, get in touch with us to find out how CACI can help you achieve scalable, secure and cost-effective results. 

In a world where it feels like data has reached saturation point, how do you decide what matters? 

The ability to tell stories with the data that matters is only going to become more critical. When everyone is time poor and feels like they’re drowning in data, reducing the time to insight is essential. GenAI tools will only get you so far, but what’s missing is the contextual information about your business, customers or stakeholders. It’s also about being able think about “where next” or “so what”, which is where the human brain still adds value. Good data storytelling can persuade, inform and influence, but it’s also a skill in itself. 

At CACI, we support our clients with projects that improve their democratisation of data and speed to insight. Our experience in dealing with an array of data across a variety of industries has led to us becoming masters of data-led storytelling. In this blog, we’ll outline what data storytelling is and why it should matter to your organisation.  

What is data storytelling?

Data storytelling is the ability to set up and frame your data insights in a way that is engaging, compelling and impactful. It’s more than just choosing the right chart or visual to display your data. It provides a structured explanation that gives context, guiding the audience through what the data means and why it matters.  

With well-executed data storytelling, your stakeholders will be able to understand the reasons behind your key insights, what the implications are, and take appropriate action off the back of your narrative. 

Benefits of data storytelling

At CACI, we’ve identified some clear benefits to applying data storytelling when talking to both clients and colleagues: 

1. Providing clarity and focussing attention 
   • Highlighting the important trends, themes or data points that need bringing to attention.
   • Ensuring that the audience knows what the key points are and the actions that should be taken.
   • Considering the business context behind decisions being made. 
2. Reinforcement of key information  
   • Details are more likely to be retained if they’re delivered in a story format, which allows for repetition and reinforcement of key messages.  
   • In setting up the context, showing the key data points behind decision points followed by the recommended action can act as powerful reminders of the “why”. 
3. Boost investment in the salient points and improve resonance 
   • Storytelling formats are more likely to get audience buy-in if that audience can understand how insights were formed and how the decisions from these insights have been (or should be) made. 

Should I use a data storytelling framework?

Using a data storytelling framework ensures readers of all technical levels can make sense of the data presented to them, understand the implications, and how to move forward by turning numbers into narrative.

Data storytelling process

The essential elements of the data storytelling process are:

Understanding that data is fundamental

Understanding your data is essential – are there outliers, for example? Is your dataset robust? You need to develop familiarity with your data in order to think about the reasons behind any trends or changes you are seeing. 

The profiles of the data, the trends and the outliers are why your audience is going to care when you deliver your story. The stronger your understanding of it, the clearer your message will become, and the passion with which your story will resonate. 

Visuals enhance data accessibility and reduce time to insight

Representing data visually is key to getting your message across, however it relies on good choices. Does the visual draw out the right elements that you want to draw attention to? Is it easy to understand?  

You can shortcut through a thousand datapoints with a well-constructed visual. It may take a lot of investment in advance to get that right, but the payoff comes when your audience understands and sees the impact straight away. 

Narrative helps to sew it all together

What is the important business context you need to include? Are there any hypotheses that you are looking to validate or debunk? Why are you doing this analysis in the first place and what are you looking to achieve? 

A story with data is still a story, and every story has a narrative flow. The skill comes in working out how to drip feed the data in and using it to enhance the narrative devices (plot points). 

The “So what”?

What are the actions or recommendations that can be taken off the back of your insights? What are the implications of your results and what should your audience change after seeing your findings? 

Any data story should build up to an action – the key purpose of using storytelling devices is to build persuasion and conviction – so ensure that this is how your story finishes (or calls back to an initial statement. 

How CACI can help

If you’re thinking about communicating data with anyone you need to think about the story. Whether it’s customers, internal stakeholders, clients or colleagues, you need to apply these narrative devices and skills.  

CACI is here to help. Contact us today to find out how you can make the most of your data by applying the right data storytelling techniques.  

Stay tuned for an upcoming blog post from Sophie Williams and Mark Edwards who will bring their expert lens on real-time examples where this has been successful. 

Cloud migration has become essential for organisations modernising their digital services, but the process can quickly become complex, costly and slow when not guided by a clear structure. Studies consistently show that cloud transformations fail when organisations lack visibility, governance and coherent decision-making.  

Enterprise architecture solves these challenges by aligning business strategy, technology, data and operations around a unified migration plan. It provides the frameworks, roadmaps and governance needed to move to the cloud in a controlled, secure and cost-efficient way. It offers teams a clear view of what to migrate, when to migrate it and how to deliver the business outcomes expected from cloud. 

In this blog, we explore how enterprise architecture supports cloud migration, the capabilities it provides and how organisations can use it to deliver faster, safer and more value-driven cloud programmes. 

What enterprise architecture means in cloud migration

Enterprise architecture helps businesses understand how their capabilities, applications, data flows and technology platforms fit together so they can smoothly transition to the cloud. It offers clarity across four core areas: 

• What systems exist today 
• How they connect and depend on each other 
• How the future cloud architecture should operate 
• Which steps are needed to migrate safely and incrementally. 

Without this context, cloud migration can lead to performance problems, security gaps, cost overruns and delays. Enterprise architecture provides the visibility and alignment needed to avoid these issues. 

Resources such as the Microsoft Cloud Adoption Framework reinforce the importance of architectural foundations, landing zones, security baselines and governance when preparing for cloud migration at enterprise scale. 

Why enterprise architecture is essential for cloud migration

Enterprise architecture enhances cloud migration across strategic, operational and technical dimensions through: 

1. Complete visibility across the application estate

Large organisations often lack a single view of their systems, making cloud migration risky. Enterprise architecture documents: 

• Application inventories 
• Dependencies 
• Data flows 
• Integration patterns 
• Infrastructure and hosting 
• Business criticality. 

This visibility prevents migrations that break key services or overlook important interdependencies. 

2. Prioritisation of workloads for migration

Enterprise architecture identifies which workloads should be: 

• Rehosted 
• Re-platformed 
• Refactored 
• Replaced 
• Retired

This prevents wasted effort on low value systems and accelerates value by prioritising high impact workloads. 

3. Defining target cloud architecture

A well-defined cloud architecture reduces long term cost, improves resilience and accelerates delivery. Enterprise architecture establishes: 

• Cloud landing zones 
• Identity and access management 
• Networking and security models 
• Platform engineering standards 
• Data and integration architecture. 

Cloud providers such as the AWS Well Architected Framework outline best practices that support this approach to achieve secure, efficient and reliable cloud environments. 

4. Strategic alignment to business priorities

Enterprise architecture ensures cloud migration is linked to business priorities, including: 

• Resilience 
• Cost optimisation 
• Customer experience 
• Regulatory compliance 
• Agility and innovation 
• Sustainability targets. 

This turns migration into a strategic programme, not just a technical activity.

5. Strong governance and decision-making 

Enterprise architecture establishes guardrails that: 

• Remove duplication 
• Enforce tagging and cost allocation 
• Standardise cloud patterns 
• Improve design quality 
• Ensure compliance with organisation wide standards. 

Frameworks like the Open Group’s TOGAF standard support consistent enterprise architecture governance across the organisation. 

6. Better risk management and security

Enterprise architects plan for: 

• Secure landing zones 
• Identity and access control 
• Encryption and data residency 
• Compliance requirements 
• Resilience and disaster recovery. 

Guidance such as the NCSC cloud security collection strengthens these architectural decisions and helps organisations adopt secure cloud services. 

7. Cost control and value realisation

Enterprise architecture is crucial for cloud cost optimisation because it defines efficient architectures that avoid waste. It supports: 

• Rightsizing decisions 
• Refactoring choices 
• Lifecycle governance 
• FinOps alignment 
• Workload placement strategies. 

This ensures cloud spend remains predictable and aligned with business value. 

Key enterprise architecture practices that accelerate migration

1. Portfolio assessment and rationalisation

Enterprise architecture evaluates: 

• Application value 
• Lifecycle stage 
• Fitness for cloud 
• Risk and complexity 
• Technical debt. 

This prevents migrating applications that should be modernised, consolidated or retired instead. 

2. Cloud readiness assessments

Readiness assessments evaluate: 

• Code quality 
• Performance and scalability needs 
• Security posture 
• Compliance requirements 
• Integration and data dependencies. 

These insights inform accurate migration strategies and help teams choose the right approach. 

3. Target state cloud architecture

Enterprise architecture defines the target state, including: 

• Cloud landing zones 
• Identity, access and network architecture 
• Platform engineering 
• Observability and logging 
• CI/CD pipelines 
• Automation standards. 

This ensures consistency across all migration waves. 

4. Business capability alignment

By mapping applications to business capabilities, enterprise architecture ensures migration aligns with organisational goals and modernises the areas that deliver the most value. 

5. Modern data and integration architecture

Cloud migration requires robust integration design. Enterprise architecture helps define: 

• API-first approaches 
• Event-driven architecture 
• Hybrid integration 
• Data pipelines 
• Governance and lineage. 

The Google Cloud Architecture Framework offers structured guidance that supports these principles. 

6. Phased migration wave planning

Enterprise architecture supports incremental migration by planning: 

• Migration waves 
• Dependency sequencing 
• Testing and validation 
• Operational readiness 
• Change management. 

This reduces risk and improves delivery speed. 

How enterprise architecture reduces cloud migration risks

Enterprise architecture enables organisations to avoid common cloud migration risks, such as: 

• Downtime, through dependency and impact analysis 
• Security gaps, by defining robust access and identity models 
• Cost overruns, by aligning with FinOps and workload sizing 
• Architecture drift, through strong governance 
• Integration failures, through complete visibility of data and interfaces 
• Scope creep, through clear migration sequencing. 

The UK government’s cloud guidance reinforces this structured, architecture-led approach for public sector organisations. 

Enterprise architecture and cost optimisation

Enterprise architecture helps organisations reduce cloud costs through: 

• Designing efficient cloud architectures 
• Choosing the right migration pattern 
• Removing technical debt 
• Preventing duplication across teams 
• Optimising data and storage strategies 
• Enforcing tagging and lifecycle policies 
• Supporting FinOps capabilities. 

Without enterprise architecture, cloud environments often become fragmented, expensive and difficult to manage. 

Enterprise architecture and AI-ready cloud platforms

AI adoption adds new complexity to cloud estates. Enterprise architecture ensures cloud platforms are AI-ready by defining: 

• Scalable GPU architectures 
• Cost efficient AI training environments 
• Data governance and lineage 
• Vector database integration 
• Secure access patterns 
• Hybrid data strategies. 

This ensures AI is adopted safely, efficiently and sustainably. 

How CACI supports enterprise architecture for cloud migration

CACI delivers robust enterprise architecture and cloud engineering services that accelerate migration while reducing risk, cost and complexity. 

Contact us today to learn more about how our structured architectural approach can help improve your migration quality, accelerate delivery and ensure your cloud investments generate measurable business value.  

The care sector is under immense pressure: staff shortages, rising demand and tighter compliance standards have created a perfect storm for providers. In response, many care management software vendors are racing to add artificial intelligence (AI) features, promising smarter decisions, predictive insights and faster outcomes. 

Is outsourcing thinking to algorithms really the answer though? Or does it risk eroding the very foundation of care – trust, safety, and human connection? 

Why the AI rush in care management software?

AI isn’t just a buzzword. It’s being embedded into care management software in ways that sound transformative: 

• Automatically building care plans by analysing assessments, medical history and wearable data 

• Predicting risks such as falls or hospital readmissions before they happen 

• Optimising rosters by matching carers to clients based on skills, continuity and location 

• Summarising notes and ensuring compliance using natural language processing 

For care providers under pressure to cut admin, stay compliant and deliver person-centred care, these promises are compelling. The narrative is clear: AI will save time, improve outcomes and reduce costs. 

In reality, however, AI in care is still unproven, often opaque and can introduce risks if adopted without clear guardrails. Algorithms are only as good as the data they’re trained on, and in social care, data can be fragmented, inconsistent and context-dependent. When decisions about vulnerable people are delegated to black-box systems, the consequences can be serious: misaligned workflows, compliance gaps and even mistrust among staff and clients. 

The risks of overreliance on AI in care management software 

AI isn’t magic. It’s a set of algorithms trained on data, and in care, that data often comes from fragmented systems, inconsistent records and human interpretation. When decisions about vulnerable people are delegated to unproven tools, the risks multiply: 

Unproven technology

• Many AI features in care software are still in early stages. Without rigorous testing in real-world settings, outputs can be unreliable, workflows misaligned and operational complexity increased. Care plans built by algorithms may look efficient, but do they truly reflect the individual behind the data? 

Compliance gaps 

• Regulators like the Care Quality Commission (CQC) emphasise person-centred documentation, accountability and evidence-based decision-making. If AI decisions can’t be explained or audited, providers could face compliance risks. Person-centred care isn’t just a phrase, it’s a legal and ethical requirement that demands human oversight. 

Staff pushback 

• Care is a human profession. Tools that feel impersonal or difficult to use can create mistrust, lower morale and cause resistance. Technology should empower staff, not alienate them. When carers feel sidelined by algorithms, the essence of care is lost. 

Client experience 

• Person-centred care is the cornerstone of quality ratings and client satisfaction. Poorly implemented AI can create barriers between carers and clients, undermining trust and connection. A truly person-centred approach means listening, adapting and responding in real time, something no algorithm can fully replicate. 

The missing human element

• Care isn’t just about tasks; it’s about empathy, intuition and the ability to respond to subtle cues. Experienced carers bring a rich, dynamic understanding shaped by years of hands-on work – something no dataset can capture. Compassion is a uniquely human trait. AI can process information, but it cannot care. 

Automation: The smarter alternative

Instead of chasing hype, CACI believes in automation with accountability – care management software that streamlines admin, reduces errors and frees staff to focus on what matters most: caring for clients. 

Automation works within parameters set by the provider, ensuring transparency and control. It’s innovation without compromise. 

Efficiency without risk 

• Automated rostering, travel time optimisation and digital care planning reduce admin burden without replacing professional judgment, keeping the person at the centre of every decision.

Compliance built in 

• Automation ensures accurate records, audit trails and SAF-aligned reporting – critical for inspections and quality assurance. Providers stay in control, not algorithms.

Human-centric design

•  By removing repetitive tasks, automation gives carers more time for meaningful interactions with clients. Technology should support the relationship between carer and client, not replace it. Person-centred care needs a person. 

Our approach with Certa 

At CACI, we’ve built Certa, our care management software, around three guiding principles: 

Connecting 

• Bringing people, data and processes together seamlessly. Everyone works from the same trusted source, whether in the office or in the field.

Confirming 

• Compliance, accuracy and transparency are non-negotiable. Certa helps providers evidence quality standards with ease.

Caring 

• Technology should never replace empathy. Certa empowers staff to focus on the human side of care. 

From smart rostering and travel optimisation to digital care planning and real-time reporting, Certa automates the complex while preserving the human touch. 

Where does AI get in? 

AI isn’t the enemy. It has a role, but only when it enhances, not replaces, person-centred care. Predictive analytics, for example, can help identify trends in service demand or flag potential compliance risks. However, these tools must be transparent, tested and always under provider control. 

The safest path is a measured one: 

• Adopt technology that grows with your service 

• Keep compliance front and centre 

• Strengthen relationships between carers and clients 

That’s what Certa delivers. 

The bottom line 

The best care management software combines innovation with empathy. It automates the complex, supports compliance and preserves the human connection that defines quality care. 

AI may be part of the future, but rushing in without a clear strategy can lead to wasted investment, unhappy staff and compromised care. A balanced approach will make all the difference. 

Why Certa makes a difference

Certa is designed for care providers who want technology that works for them. Not the other way around. With features like: 

• Person-centred care planning 

• Advanced rostering and travel optimisation 

• Real-time reporting and SAF-aligned compliance tools 

• Secure-by-design architecture (ISO27001, Cyber Essentials Plus) 

Certa helps you deliver outstanding care while staying efficient, compliant and connected. Get in touch with us today to find out how automation can help your staff focus on what matters most: providing care. 

Cloud is now firmly mainstream, with roughly 94% of enterprises using cloud services and a growing majority running over half of their workloads in the cloud. Worldwide end-user spending on public cloud was forecasted to reach roughly $723 billion in 2025, underlining just how critical cloud has become to a business’ strategy.  

Yet despite this investment, cloud migration challenges remain stubbornly persistent. One major study found that organisations spend on average 14% more on migration than planned and 38% of migrations are delayed by more than a quarter, driven by complexity, poor planning and skills gaps. Another widely cited report notes that 84% of organisations struggle to manage cloud spend effectively.  

This guide explores the most common cloud migration challenges, why they occur and how to design a migration strategy, tooling approach and operating model that gives you a much higher chance of success. It also demonstrates how CACI’s cloud, engineering and implementation services can support your journey. 

What is cloud migration and why is it so challenging?

Cloud migration is the process of moving applications, data, workloads and underlying infrastructure from on-premises or legacy environments into cloud platforms. It can also include moving between clouds or from one cloud service model to another.

Types of cloud migration

Understanding the main migration patterns is a useful starting point for setting expectations: 
 

• Rehost (lift-and-shift): Moving workloads with minimal changes. 
• Replatform: Making modest optimisations (e.g. managed databases) during migration. 
• Refactor: Re-architecting applications to use cloud-native services. 
• Rebuild: Rewriting systems from scratch for the cloud. 
• Replace: Retiring legacy apps in favour of SaaS solutions. 

Most organisations end up using a mix of these approaches across workloads.

Complex deployment models

Modern estates typically combine: 

• Public cloud for scale and agility 
• Private cloud for specific compliance or performance needs 
• Hybrid cloud spanning on-prem and cloud 
• Multi-cloud using several providers. 

Gartner expects 90% of organisations to adopt hybrid cloud by 2027, reflecting the reality that few businesses are “all in” on a single environment. More choice is valuable, but it amplifies governance, integration and cost-management challenges.

Cloud benefits versus migration risks

The benefits of cloud are well documented: agility, scalability, resilience, innovation, access to AI services and more. IDC’s overview of cloud market trends highlights how cloud is now the foundation for data, automation and AI use cases. 

However, without a structured approach, migrations can lead to: 

• Higher-than-expected operating costs 
• Outages and performance issues 
• Security gaps and compliance risk 
• Stalled programmes and change fatigue.

This is where understanding the main cloud migration challenges becomes essential. 

Most substantial cloud migration challenges (by phase)

Grouping cloud migration challenges by phase of the journey helps you anticipate issues before they derail your programme.

1. Strategy & business alignment challenges

No clear business case

Many migrations begin with a general desire to “move to the cloud” without defining measurable success criteria. Are you aiming for reduced costs, faster product delivery, better resilience, improved security or all the above?

Lift-and-shift by default

Under pressure to move quickly, organisations often default to lift-and-shift. While appropriate in some cases, this often leads to increased cloud costs and disappointed stakeholders once workloads land in an environment they were not designed for.

Misaligned stakeholders

Finance wants predictable spend, IT wants stability and business units want new features tomorrow. Without a shared roadmap and governance model, priorities can easily clash.

How to mitigate these challenges

• Define a clear business case with KPIs (e.g. target cost savings, uptime, deployment frequency)
• Involve IT, finance and business leaders from the outset
• Use a structured migration framework and consider partnering with specialists such as CACI’s cloud, engineering and implementation services to co-create your strategy.

2. Discovery & assessment challenges

Poor application and dependency visibility

It is not uncommon for organisations to start migration planning and then discover that they do not have a complete, up-to-date inventory of applications, databases, integrations and dependencies. Missing a single critical dependency can cause outages when workloads are moved.

Legacy constraints

Older platforms, bespoke middleware and tightly coupled integrations obfuscate cloud migration. Some systems may be out of vendor support or lack documentation.

Underestimating integration complexity

Hybrid and multi-cloud architectures must integrate cleanly with on-prem systems and SaaS applications. Underestimating integration can lead to brittle connections and security gaps.

How to mitigate these challenges

• Use automated discovery and assessment tools to build a realistic view of your estate
• Map dependencies visually and prioritise high-blast-radius systems
• Classify workloads using a structured model (retain, retire, rehost, re-platform, refactor, replace)
• Consider a Platform Migration approach with expert support, such as CACI’s dedicated Platform Migration service.

3. Architecture & technical challenges

Choosing the right architecture

The breadth of cloud services is both a blessing and a curse. Teams must choose between virtual machines, containers, serverless, managed databases, message queues, data lakes and more, often with incomplete information and tight deadlines.

Performance and latency issues

Network design, data placement and application architecture all influence latency and throughput. Poor decisions in these areas can degrade customer experience and internal system performance.

Vendor lock-in

Leveraging cloud-native services maximises value but may also increase dependence on specific providers. Regulatory and data-sovereignty discussions, particularly in the UK and EU, are causing many organisations to carefully consider portability and digital sovereignty strategies.

How to mitigate these challenges

• Define reference architectures and guardrails early
• Run performance tests in pilot migrations
• Make conscious choices about where you accept lock-in for higher value and where you prefer portability.

4. Cloud migration security challenges

Security is consistently cited as one of the top cloud migration challenges. Government and industry bodies emphasise that cloud— used correctly— can be more secure than on-prem infrastructure. The UK government’s Cloud First policy and accompanying guidance stress the importance of security-by-design, shared responsibility and robust governance.

Identity and access management (IAM)

Misconfigured IAM, overly broad privileges and lack of role-based access control are a major root cause of cloud incidents.

Data protection

Sensitive data must be encrypted in transit and at rest, with careful key management and robust backup and recovery procedures.

Compliance and shared responsibility

Regulated sectors must demonstrate compliance with standards and regulations in a model where security responsibilities are split between provider and customer.

How to mitigate these challenges

• Establish an IAM strategy with least-privilege access and strong authentication
• Implement encryption, key management and robust logging from day one
• Use security posture-management tools and align with public guidance such as the UK cloud guide for the public sector
• Build security into your cloud platform as part of solution implementation rather than as an afterthought.

5. Data & integration challenges

Moving large volumes of data

Migrating terabytes or petabytes of data without impacting operations requires careful planning. Complex cutover plans, bulk transfer tools and synchronisation mechanisms are often needed.

Data quality and consistency

Inconsistent schemas, duplication and poor data governance can lead to mistrust in analytics and operational systems post-migration.

Integrating cloud with on-prem and SaaS

APIs, message queues and integration platforms must be carefully designed to avoid fragile, tightly coupled connections.

How to mitigate these challenges

• Treat data migration as a dedicated workstream
• Clean and reconcile data before moving it
• Design integration patterns (e.g. event-driven architectures) aligned to your target operating model
• Draw on lessons from real-world programmes like CACI’s case study on HMCTS Court Store and Bench’s move to AWS.

6. Cost, governance & FinOps challenges

Cloud is often sold as a route to lower costs, but the reality is more nuanced. In 2025, 84% of organisations struggled to manage cloud spend and cost optimisation remains a top priority year after year.

Bill shock and opaque spend

Without robust tagging, budgeting and monitoring, costs can escalate quickly. Bursty workloads, test environments left running and underused instances are common culprits.

Weak financial governance

Traditional budgeting models are not always suited to variable, usage-based pricing. Cloud makes it easy to spend money, but not to spend wisely.

Unclear total cost of ownership

Many organisations underestimate the ongoing cost of running cloud environments, including observability, security, data transfer and platform teams.

How to mitigate these challenges

• Adopt FinOps principles early, not after migration. A growing number of organisations are doing this specifically to tackle cloud waste and align spend to business value
• Tag resources consistently to enable accurate cost allocation
• Use budgets, alerts and dashboards to track spend against KPIs
• Consider getting external support from cloud specialists such as CACI’s Cloud Services to design your governance model.

7. People, skills & operating model challenges

Skills gaps

Cloud-native, DevOps and automation skills are in high demand. Internal teams may lack experience in designing and operating cloud platforms at scale.

Operating model friction

Existing ITIL-style processes and siloed teams do not always translate well to cloud environments, where continuous delivery and shared ownership are essential.

Cultural change

Cloud is not just a technology shift, but a cultural one. Teams must embrace new ways of working, from infrastructure-as-code to platform teams and product-centric delivery.

How to mitigate these challenges

• Create a Cloud Centre of Excellence (CCoE) to set standards and share best practice
• Invest in training and hands-on enablement
• Partner with experienced providers to accelerate learning, as described in CACI’s article on the actual experience of cloud migration for business.

How to build a cloud migration strategy that avoids these challenges

A structured cloud migration strategy is your best defence against these pitfalls.

Step 1: Define business outcomes and KPIs

Start with the “why”:

• Cost optimisation (e.g. target percentage reduction in run-rate costs)
• Improved resilience (e.g. RPO/RTO targets, availability SLAs)
• Faster time-to-market (e.g. release frequency, lead time for changes)

Better customer and employee experience.

Step 2: Assess your current

• Catalogue applications, services, databases and integrations
• Classify each workload by business criticality, technical complexity and risk
• Identify “quick wins” and high-risk areas needing more design work.

Step 3: Plan migration waves

Avoid trying to move everything at once. Instead:

• Group workloads into waves with clear objectives
• Start with lower-risk, high-learning systems
• Use pilot migrations to refine patterns and tooling.

Step 4: Design your target cloud architecture

Make conscious choices about:

• Compute models (VMs, containers, serverless)
• Data platforms (managed databases, data lakes, warehouses)
• Networking and connectivity (VPNs, private links, SD-WAN)
• Platform services for security, observability and CI/CD.

Step 5: Embed security and governance upfront

• Define IAM, logging, monitoring and compliance controls before migration
• Align with recognised guidance such as the UK government’s cloud technology collection
• Treat “platform hardening” as a prerequisite for production workloads.

Step 6: Establish a cloud operating model

Clarify:

• Who owns the central platform
• How product and application teams consume it
• How changes are tested, deployed and supported.

This operating model is where the concept of a cloud-appropriate strategy (rather than “cloud at all costs”) really takes shape.

Step 7: Plan for continuous optimisation

Cloud migration is not a one-off event. After cutover, you should:

• Right-size resources and use auto-scaling
• Tune performance and storage tiers
• Modernise where there is clear value
• Review costs and security posture regularly.

Cloud migration tools, platforms & frameworks

Choosing the right tools reduces risk and effort at each stage of migration.

Discovery, assessment & dependency mapping

• Infrastructure discovery tools and CMDBs
• Application performance monitoring (APM) platforms
• Dependency mapping and visualisation tools.

Data migration & synchronisation

• Cloud-native database migration services
• ETL/ELT tools for structured data movement
• Bulk transfer technologies for large datasets.

Application migration & modernisation

• Containerisation and orchestration tools
• Refactoring accelerators and code analysis tools
• CI/CD platforms to support new deployment models.

Security, compliance & governance

• Cloud security posture management (CSPM) and policy-as-code
• Identity and access management, secrets management and HSMs
• SIEM and threat-detection tooling.

Observability, performance & FinOps (H3)

• Monitoring, logging and tracing platforms
• Cost-management and optimisation tools aligned with FinOps practices.

The specific mix will depend on your chosen cloud providers and operating model, but the categories remain consistent.

Cloud migration best practices

This checklist outlines a practical reference throughout your programme:

Pre-migration

• Business case and KPIs agreed
• Application inventory and dependency maps completed
• Migration patterns decided per workload (rehost / replatform / refactor / etc.)
• Security and governance baselines designed
• Cost management and tagging strategy defined.

During migration

• Workloads migrated in waves, with rollback plans
• Performance and resilience tested in each wave
• Security controls verified before go-live
• Costs monitored against forecasts.

Post-migration

• Workloads rightsized and tuned
• Modernisation opportunities assessed
• Security posture and compliance reviewed regularly
• KPIs tracked and reported to stakeholders.

Measuring cloud migration success: KPIs & metrics

You cannot improve what you do not measure. Useful KPIs include:

Technical

• Availability and uptime
• Latency and response times
• Error rates and incident frequency.

Financial

• Monthly cloud run-rate vs baseline
• Cost per transaction or per user
• Savings from rightsizing or modernisation initiatives.

Business

• Release frequency and deployment lead times
• Time-to-market for new features
• Customer satisfaction or NPS impact.

Security

• Number of critical vulnerabilities
• Mean time to detect (MTTD) and mean time to remediate (MTTR)
• Compliance audit findings.

These metrics help you demonstrate whether your cloud migration is delivering on its promises or whether strategy and execution need to be re-thought.

Turning cloud migration challenges into an advantages with CACI

Cloud has moved from a novelty to a business necessity, but the real differentiator is how effectively your organisation navigates cloud migration challenges: strategy, security, cost, people and operations.

With the right roadmap, tools and operating model, you can turn those challenges into an advantages: more resilient services, faster innovation and a technology foundation ready for AI and future growth.

If you are ready to move from theory to practice, explore CACI’s Cloud, Engineering & Implementation Services and our dedicated Platform Migration and Solution Implementation offerings. You can also learn from real projects in our article on the actual experience of cloud migration for business.

What is Model Based Systems Engineering (MBSE)? A practical explainer for modern engineering 

Engineering domains like defence, automotive, manufacturing and critical infrastructure have always dealt with complexity. But today that reality is compounded by volatility. One seemingly small change can ripple across an entire architecture: a single component going end of life forces updates to requirements, interfaces and test plans or single regulatory change means revisiting assumptions and evidence across multiple teams.  

Traditional, document heavy engineering methods simply weren’t designed for this pace, scale and level of interdependence. Big static specifications, linear stage gated processes and manual drafting and review cycles are slow, siloed and paperwork driven; they just can’t keep up with environments that depend on fast iteration, shared data, and real-time collaboration. 

Model Based Systems Engineering (MBSE) offers a more coherent way forward. It makes models, rather than documents, the primary way of understanding how a system is put together and how it behaves under change. And while it’s often discussed in abstract terms, its value is practical: clearer decisions, fewer surprises and systems that can evolve with the world around them. 

Understanding Model Based Systems Engineering 

Traditional systems engineering spreads knowledge across separate artefacts: requirements lists, design specifications, interface control documents, test plans and more. Each serves a real purpose, but together they create a fragmented picture that engineers must mentally stitch together. 

MBSE brings this information into a single system model. Instead of navigating isolated, and typically manual, documents, engineers work with a visual, traceable representation of requirements, behaviours, structures and constraints across the system’s lifecycle: from concept and design through to operation and decommissioning. 

This connected view enables teams to: 

• Simulate and validate designs before physical implementation 
• Understand the implications of a change across the whole system or system-of-systems 
• Maintain traceability between requirements, design and testing as the system evolves 
• Accommodate iterative and Agile delivery without losing architectural coherence 
• Establish a strong foundation for digital twins and digital continuity 

In short, MBSE replaces a fragmented understanding with a coherent one. By shifting the focus from assembling information to analysing the system as a dynamic whole, it makes decisions clearer and enables swifter action. 

MBSE vs. Enterprise Architecture – what’s the difference? 

As an approach, MBSE is often mentioned alongside or confused with Enterprise Architecture (EA) because both use models to bring structure to a changing, interconnected world. They sit on a continuum, but they don’t do the same job. 

Enterprise Architecture works at the organisational level, the so-called ‘30,000ft view’. It defines the capabilities the business needs, the processes that support them, the information that flows between them and the technology principles that keep everything aligned. EA sets the strategic intent and the architectural constraints within which engineered systems must operate. 

Model Based Systems Engineering works at the system level and, critically, does so visually. It uses graphical models to capture requirements, behaviour, structure and constraints so engineers can see how a system works, how its parts interact and how changes flow across the architecture. MBSE can represent a single engineered system or a “system of systems”, depending on the scale of the environment.  

In plain engineering terms: 

• EA defines the environment: capabilities, context, constraints.
• MBSE defines the system: behaviour, architecture, verification.

EA sets the intent; MBSE delivers the model‑based technical design that realises that intent. So, even when a “system of systems” MBSE model approaches EA in scope, it’s still serving a different purpose. Both disciplines tackle the same operational pressures but address them from different vantage points. 

Model Based Systems Engineering in practice 

In practice, MBSE means working from a dynamic system model that brings together the elements that matter most in complex engineering environments. Typically visualised in a dashboard, it provides a traceable, queryable representation of the system as a single point of truth, containing: 

• Requirements
• Behaviours and interactions
• System structure and architecture
• Constraints and dependencies
• Lifecycle considerations from concept to decommissioning

The shift from documents to models isn’t cosmetic. Documents age; models evolve. Documents sit in silos; models connect disciplines. Documents tell you what the system was; models show you what the system is — and what it could be as it adapts to new constraints, technologies or missions. 

Most organisations use modelling languages such as SysML and tools like Cameo, Rhapsody or Enterprise Architect. SysML remains the most widely used, giving teams a standardised way to express structure, behaviour and constraints across complex systems. But the tools are only the enablers. The real value lies in the clarity, consistency and shared understanding that modelling brings. 

The operational benefits – why MBSE matters in modern engineering

 MBSE gives teams a coherent view of how a system behaves and how change in one area affects others and, fundamentally, a more honest representation of how systems behave in the real world. That shift enables: 

• Earlier validation and simulation
• Clearer communication across disciplines
• Faster impact analysis
• Stronger traceability between requirements, design and testing
• Enhanced collaboration across teams and suppliers
• Scalability for managing large, multicomponent or “system of systems” architectures

This is why MBSE has become particularly relevant in sectors where systems are large, long-lived and safety or mission critical.  

In defence and aerospace, it supports mission level traceability, interoperability across suppliers and stronger evidence for certification. In automotive, it helps integrate mechanical, electrical and software design in increasingly software defined vehicles. And in digital and critical infrastructure, it provides a way to map dependencies, model resilience and design for long-term adaptability. The common theme being MBSE provides the clarity needed to make confident decisions. 

What good MBSE delivery looks like in practice 

Successful MBSE programmes have less to do with tools and more to do with delivery behaviours. The organisations that get the most value tend to share a few consistent patterns: 

• Models are treated as living artefacts. They evolve as understanding deepens, rather than being produced once and filed away. 
• Iteration is normal. Teams model early, test assumptions quickly and refine as they learn, instead of waiting for a single ‘big reveal’. 
• Commercial and governance frameworks allow change. MBSE only works when contracts, schedules and decision gates accept that things will evolve. 
• Practitioners lead the work. Systems engineers, architects and domain specialists shape the model, ensuring it reflects real world behaviour rather than abstract theory. 
• Collaboration is built in. Modelling becomes a shared activity across disciplines, not something done in isolation by a single specialist. 

These principles also shape how CACI deliver MBSE.  

Our teams work iteratively, use models to drive shared understanding and keep architectures traceable as requirements evolve. We focus on the behaviours that make MBSE effective, clarity, adaptability and practitioner led modelling – because these consistently help programmes navigate complexity and make better decisions. 

Why MBSE is becoming essential 

 Recent research finds the number and intensity of system level dependencies is rising across every major engineering domain, increasing the likelihood that local failures propagate far beyond their point of origin. The PanIberian blackout in April 2025 made this clear: the energy disturbance cascaded across two national grids, disrupting transport, healthcare and communications within minutes.  

In this context, MBSE becomes a core competency rather than a niche specialism. But its value depends on how it is delivered, and by who.  

A strong MBSE approach provides clarity, traceability and better decisions. It reduces risk. It helps engineering systems evolve with the environment. And in sectors where the stakes are high like defence, automotive, aerospace and critical infrastructure, that combination is not optional, it’s foundational — and increasingly essential if organisations are to stay ahead of the rising fragility built into the systems they depend on. 

To find out how CACI can help your organisation build the resilience needed to operate effectively in an increasingly volatile, interconnected engineering environment, get in touch with our experts today. 

FAQs about Model Based Systems Engineering (MBSE)

What does “model-based” actually mean in Model Based Systems Engineering (MBSE)?

In Model Based Systems Engineering (MBSE), “model-based” means that system information is stored in a structured, machine-readable model rather than free-text documents. This allows relationships, dependencies and constraints to be queried, analysed and validated automatically instead of being inferred manually.

Is Model Based Systems Engineering only suitable for large or complex systems?

No. While MBSE is most visible in large, complex programmes, it can also be valuable for smaller systems where change is frequent or assurance requirements are high. Even lightweight models can reduce ambiguity, improve communication and prevent rework as designs evolve.

How does MBSE support verification and validation activities?

MBSE enables verification and validation by explicitly linking system behaviours and constraints to verification criteria within the model. This allows teams to assess test coverage, identify gaps early and maintain alignment between design intent and evidence as the system changes.

What skills are required to work effectively with Model Based Systems Engineering?

Effective MBSE requires a combination of systems thinking, domain expertise and modelling literacy. While familiarity with languages such as SysML is useful, the most important skills are the ability to reason about system behaviour, understand trade-offs and communicate across disciplines using models as a shared reference.

How does Model Based Systems Engineering improve decision-making?

MBSE improves decision-making by making assumptions, dependencies and impacts explicit. Engineers and stakeholders can explore “what-if” scenarios, assess trade-offs and understand consequences before changes are committed, reducing the risk of late-stage surprises.

Can Model Based Systems Engineering be applied to legacy systems?

Yes. MBSE can be introduced incrementally to legacy environments by modelling critical parts of an existing system rather than attempting a full re-engineering effort. This approach helps organisations gain insight into dependencies, constraints and risks without disrupting ongoing operations.

How does MBSE fit with safety, regulatory and assurance frameworks?

MBSE supports safety and regulatory assurance by providing a structured way to demonstrate traceability from requirements through design to verification evidence. This can simplify audits, improve confidence in compliance claims and reduce the effort required to respond to regulatory change.

What are common misconceptions about Model Based Systems Engineering?

A common misconception is that MBSE is primarily a tooling or documentation exercise. In practice, its effectiveness depends on how models are used to support collaboration, learning and decision-making — not on the level of detail or the sophistication of the tools alone.