maddaisy – cultivating innovation

Tag: ai-strategy

  • The Strategy-Bandwidth Gap: Why CSOs Are Being Sidelined on Their Own AI Agendas

    Ninety-five per cent of chief strategy officers expect AI and technology-led disruption to materially reshape their strategic priorities this year. Yet only 28 per cent co-lead their organisation’s AI-related decisions. That gap — between expecting AI to change everything and actually having the authority to steer it — is the central finding of Deloitte’s 2026 Chief Strategy Officer Survey, and it has implications well beyond the strategy function.

    For maddaisy.com’s consulting audience, the data confirms a pattern that has been building for months. The executives whose job description is literally to chart the company’s future are being outpaced by the very transformation they are supposed to lead.

    The bandwidth problem is structural, not personal

    Deloitte’s survey paints a picture of a role under strain. More than half of CSOs report managing too many priorities with too little time. Approximately half have five or fewer direct reports. Many rely on rotating external support — often consultants — to advance critical initiatives, which ironically consumes more coordination time and leaves less room for the strategic thinking the role demands.

    Despite this, expectations continue to expand. Nearly two-thirds of CSOs now lead cross-functional transformation efforts, and more than half drive enterprise-wide agendas that stretch well beyond the role’s traditional remit. The mandate is growing; the resources are not.

    Only 35 per cent of CSOs say they co-lead or fully own decision-making for their organisation’s top priorities. As Gagan Chawla, Deloitte’s US Business Strategy Practice leader, puts it: strategy leaders should “reimagine their mandate and champion governance that aligns decision-making power with enterprise priorities to help ensure strategy drives value, not just insight.”

    That is a polite way of saying many CSOs are producing analysis that nobody with execution authority is acting on.

    The AI authority gap

    The most striking disconnect is on AI specifically. Nearly every CSO surveyed expects AI to reshape competitive dynamics. Yet the survey finds that only 28 per cent co-lead enterprise AI decisions, and just 16 per cent say their organisation uses AI to fundamentally reimagine lines of business or create new competitive advantages.

    This sits uncomfortably alongside data maddaisy.com has been tracking. PwC’s 2026 CEO Survey found that 56 per cent of chief executives cannot point to measurable revenue gains from AI. If the executives responsible for enterprise strategy are not materially involved in AI decisions, the ROI gap starts to look less like a technology problem and more like an organisational design failure. AI investments are being made by technology teams, approved by finance, and executed by operations — while the people tasked with ensuring these investments align with long-term competitive positioning watch from the sidelines.

    The Deloitte data suggests momentum is building — 51 per cent of CSOs now view AI as a strategic partner that enhances insight and accelerates execution, and 61 per cent report investing in AI literacy. But viewing AI as useful and having authority over its deployment are very different things.

    Where this connects to consulting

    The CSO bandwidth gap has direct consequences for the consulting industry. Strategy firms have traditionally sold to the strategy function — providing the research, analysis, and frameworks that CSOs use to set direction. If those CSOs lack the authority to act on strategic recommendations, the value proposition of strategy consulting shifts.

    This helps explain the trend maddaisy.com examined last week in the shift from billable hours to outcome-based consulting. When McKinsey ties a third of its revenue to measurable business outcomes rather than advisory engagements, it is partly responding to a reality where clients’ strategy functions cannot translate advice into action without hands-on support. The consulting engagement is moving from “here is what you should do” to “let us do it with you” — and that shift is driven not just by AI capabilities but by the operational reality that internal strategy teams are stretched too thin to execute.

    Accenture’s recent disclosure reinforces the point from the technology side. The firm announced in December that it would stop separately reporting advanced AI bookings because AI is now “embedded in some way across nearly everything we do.” With $11.5 billion in AI bookings across 11,000 projects and revenue of $4.8 billion, Accenture has reached a point where AI is infrastructure, not a standalone initiative. For CSOs trying to “own” the AI strategy, this creates an additional challenge: when AI permeates every function, there is no single strategy to own.

    Confidence without control

    Perhaps the most telling number in the Deloitte survey is the confidence gap. Seventy-two per cent of CSOs are optimistic about their organisation’s prospects, compared with just 24 per cent who feel the same about the global economy. Strategy leaders believe their companies can win despite external uncertainty — but this confidence sits alongside an admission that they lack the bandwidth and authority to ensure it happens.

    Adam Giblin, Deloitte’s US Chief Strategy Officer Programme lead, frames it directly: “CSOs are navigating a world where uncertainty isn’t episodic, it’s the status quo.” The implication is that strategy can no longer be a periodic exercise — an annual planning cycle punctuated by quarterly reviews. It needs to be continuous, embedded, and authoritative.

    For organisations that have not yet reconciled the CSO’s expanding mandate with actual decision-making power, the path forward is not complicated but it is uncomfortable. It means giving strategy leaders a seat at the AI governance table — not as observers, but as co-owners. It means aligning resource allocation with strategic priorities rather than expecting a team of five to drive enterprise-wide transformation. And it means recognising that the AI ROI gap is, in significant part, a strategy execution gap.

    The companies that close it will not be the ones with the most advanced AI models. They will be the ones where the person responsible for competitive positioning actually has the authority to shape how AI is deployed.

  • From Billable Hours to Shared Risk: Consulting’s AI-Driven Business Model Shift

    McKinsey’s 25,000 AI agents grabbed the headlines, but the more consequential number is the roughly one-third of the firm’s revenue now tied to outcome-based engagements. Across the industry, AI is not just changing how consultants work – it is rewriting how they get paid.

    When maddaisy.com examined McKinsey’s 25,000 AI agent deployment last week, the focus was on scale: was deploying one digital agent for every 1.6 human employees a first-mover advantage or an expensive experiment? The answer may depend less on the agent count and more on a quieter transformation happening in parallel – the shift from selling time to selling outcomes.

    The Model That Built an Industry Is Under Pressure

    For decades, consulting has run on a straightforward exchange: expertise for time, billed by the hour or the project. It is a model that has produced extraordinary margins, but it carries an inherent misalignment. Consultants profit from the complexity of a problem, not necessarily from solving it quickly.

    AI agents threaten that dynamic directly. When an algorithm can synthesise research in minutes that previously took analysts weeks, the hours-based model starts to look exposed. McKinsey’s own data – 1.5 million hours saved on search and synthesis work – quantifies exactly how much billable time AI has already removed from the equation.

    Rather than watching margins erode, McKinsey is pivoting. Speaking on the HBR IdeaCast in February, CEO Bob Sternfels confirmed that outcome-based engagements – where McKinsey co-invests alongside clients and ties fees to measurable business results – now account for roughly a third of the firm’s revenue. Two years ago, that figure was negligible.

    The Economics Only Work with AI

    This is where the 25,000 agents become strategically coherent. Outcome-based consulting is inherently riskier than fee-for-service; the firm only earns if the client succeeds. To make the economics work, you need two things: lower delivery costs and higher confidence in results.

    AI agents address both. QuantumBlack, McKinsey’s 1,700-person AI division, now drives 40% of the firm’s total work. Non-client-facing headcount has fallen 25%, while output from those teams has risen 10% – the “25 squared” model. The savings create the margin headroom needed to absorb the risk of outcome-based pricing.

    It is not just McKinsey making this calculation. BCG has deployed “forward-deployed consultants” who build AI tools directly within client organisations, effectively embedding methodology as software rather than slides. Capgemini has trained 310,000 employees on generative AI, though its agentic AI bookings only reached 10% of quarterly pipeline by Q4 2025. Accenture has stopped reporting AI bookings separately because, as the firm noted in its Q1 fiscal 2026 results, AI is now embedded in virtually every engagement.

    The Client Side of the Equation

    The timing is not coincidental. As maddaisy.com reported last week, PwC’s 2026 CEO Survey found that 56% of chief executives still cannot demonstrate revenue gains from AI. When the client cannot prove value, the consultancy offering to underwrite outcomes holds a powerful negotiating position – essentially saying, “we believe in this enough to stake our fees on it.”

    The irony is that consultancies are asking clients to trust their AI capabilities while the industry’s own track record on AI delivery remains uneven. Deloitte’s own 2026 State of AI report found that 42% of organisations consider their AI strategy “highly prepared” but feel markedly less ready on infrastructure, data governance, and talent.

    McKinsey faces this credibility gap from a different direction. The firm’s State of AI research found that only 5% of companies globally see AI hitting their bottom line. Positioning itself as the firm that can deliver measurable outcomes means McKinsey is, in effect, claiming to solve a problem that its own research says almost no one has solved.

    What Changes for Practitioners

    For consultants and the organisations that hire them, three practical implications stand out.

    Procurement shifts. If outcome-based pricing becomes the norm, procurement teams will need to evaluate consulting engagements more like joint ventures than service contracts. That means assessing the firm’s AI capabilities, data infrastructure, and delivery methodology – not just the partner’s credentials and the day rate.

    The talent model is splitting. Sternfels has been explicit that McKinsey wants “great consultants and/or great technologists, groomed to be both.” The traditional path – from analyst to associate to engagement manager – now runs alongside a technical track where consultants build and deploy AI systems. BCG’s vibe-coding consultants are an early version of this hybrid role.

    Governance becomes shared. When a consultancy co-invests in outcomes and deploys AI agents within a client’s operations, the governance question becomes bilateral. As maddaisy.com has covered extensively, the gap between AI deployment speed and governance maturity is already the defining risk of 2026. Outcome-based models widen this gap further, because neither party has clear precedent for who owns the risk when an AI agent produces flawed analysis that drives a business decision.

    The Bigger Picture

    The consulting industry has weathered previous disruptions – offshoring, automation, the rise of in-house strategy teams – by evolving its value proposition. This time, the change is structural. AI agents do not just reduce the cost of delivering advice; they make it possible to charge for results instead.

    McKinsey’s 25,000 agents are best understood not as a technology deployment but as a financial instrument – the infrastructure that underwrites a new revenue model. Whether the model works depends on something no agent can automate: whether clients actually achieve the outcomes both parties are betting on.

  • Shadow AI and the Year-Two Governance Challenge

    Over a third of employees have already used AI tools without their organisation’s knowledge or permission. As enforcement deadlines tighten, the gap between what staff are doing and what leadership has sanctioned is becoming one of enterprise AI’s most urgent — and most overlooked — governance problems.

    The term “shadow AI” describes AI tools adopted by employees outside official channels — free-tier large language models used to draft client communications, image generators repurposed for internal presentations, coding assistants plugged into development environments without security review. It is the AI equivalent of shadow IT, but with a critical difference: the data exposure risks are significantly higher and the speed of adoption is faster than anything IT departments have previously managed.

    According to the State of Information Security Report 2025 from ISMS.online, 37% of organisations surveyed said employees had already used generative AI tools without organisational permission or guidance. A further 34% identified shadow AI as a top emerging threat for the next 12 months. These are not projections about some distant risk — they describe what is happening now, in organisations that thought they had AI under control.

    The year-two reckoning

    The pattern is familiar to anyone who lived through the early cloud adoption cycle, but compressed into a fraction of the time. Year one — roughly 2024 into early 2025 — was characterised by experimentation. Departments trialled AI tools, leaders encouraged innovation, and governance was deferred in favour of speed. The implicit message in many organisations was: try things, move fast, we will sort out the rules later.

    Year two is when “later” arrives. And the numbers suggest most organisations are not ready for it. The same ISMS.online report found that 54% of respondents admitted their business had adopted AI technology too quickly and was now facing challenges in scaling it back or implementing it more responsibly. That figure represents a majority of enterprises acknowledging, in effect, that they have accumulated governance debt they do not yet know how to service.

    This aligns with a pattern maddaisy has been tracking across several dimensions. Deloitte’s 2026 State of AI report revealed that organisations report growing strategic confidence in AI but declining readiness on the operational foundations — infrastructure, data quality, risk management, and talent — needed to execute responsibly. Shadow AI is the ground-level expression of that paradox: strategy says “adopt AI,” but operations never built the guardrails to manage what adoption actually looks like across thousands of employees making independent tool choices every day.

    Why traditional IT governance falls short

    The instinct in many organisations is to treat shadow AI like shadow IT — block unsanctioned tools, enforce approved vendor lists, and route everything through procurement. That approach, while understandable, misses what makes AI different.

    When an employee uses an unapproved project management tool, the risk is primarily operational: data silos, integration headaches, wasted licences. When an employee pastes client data, financial projections, or intellectual property into a free-tier language model, the risk is fundamentally different. That data may be used for model training, stored in jurisdictions with different privacy regimes, or exposed through security vulnerabilities the organisation has no visibility into.

    As CIO.com recently noted, boards are increasingly recognising that AI is not waiting for permission — it is already shaping decisions through vendor systems, employee-adopted tools, and embedded algorithms that grow more powerful without explicit organisational consent. The question for governance teams is not whether employees are using unsanctioned AI. It is how much sensitive data has already left the building.

    The regulatory dimension

    Shadow AI also creates a specific compliance exposure that many organisations have not fully mapped. As maddaisy examined last week, the EU AI Act reaches its most consequential enforcement milestone in August 2026, with requirements for high-risk AI systems carrying penalties of up to €35 million or 7% of global annual turnover. Colorado’s AI Act takes effect in June 2026 with its own set of requirements around algorithmic discrimination and impact assessments.

    The compliance challenge with shadow AI is that an organisation cannot demonstrate responsible use of systems it does not know exist. If an employee in a hiring function uses an unsanctioned AI tool to screen CVs, or a financial analyst uses a free language model to generate risk assessments, the organisation may be deploying high-risk AI — as defined by regulators — without any of the documentation, monitoring, or impact assessment that compliance requires.

    This is not a theoretical concern. It is a direct consequence of the gap between adoption speed and governance maturity that maddaisy has documented across the enterprise AI landscape.

    What a pragmatic response looks like

    The organisations handling shadow AI most effectively are not the ones deploying the heaviest restrictions. They are the ones that recognised early that prohibition does not work when the tools are free, browser-based, and genuinely useful.

    A pragmatic governance approach has several components. First, visibility: understanding what AI tools employees are actually using, through network monitoring, surveys, and — critically — creating an environment where people feel safe disclosing their usage rather than hiding it. Second, clear usage policies that distinguish between acceptable and unacceptable use cases, specifying what data categories must never enter external AI systems regardless of the tool’s provenance.

    Third, an approved toolset that is genuinely competitive with the free alternatives. One of the most common drivers of shadow AI is that official enterprise AI tools are slower, more restricted, or simply worse than what employees can access on their own. If the sanctioned option requires a three-week procurement process while ChatGPT is a browser tab away, governance has already lost.

    Fourth, the frameworks exist. ISO 42001 provides a structured approach to establishing an AI management system, covering policy, roles, impact assessment, and continuous improvement through the familiar Plan-Do-Check-Act cycle. It is not a silver bullet, but it offers a starting point for organisations that currently have no systematic approach to AI governance beyond hoping the problem does not escalate.

    The window is narrowing

    The uncomfortable reality for many enterprises is that shadow AI has already created facts on the ground. Data has been shared with external models. Workflows have been built around unsanctioned tools. Employees have integrated AI into their daily routines in ways that would be disruptive to simply switch off.

    The year-two governance challenge is not about preventing AI adoption — that ship has sailed. It is about catching up with what has already happened, building the visibility and policy infrastructure to manage it going forward, and doing so before enforcement deadlines turn a governance gap into a compliance crisis.

    For consultants and practitioners advising organisations through this transition, the message is straightforward: audit first, policy second, technology third. The organisations that will navigate this best are not the ones with the most sophisticated AI strategies. They are the ones that know, concretely and completely, what AI is actually being used within their walls — and by whom.

  • Lloyds Banking Group’s £100 Million AI Bet: What the UK’s First Agentic Financial Assistant Means for Enterprise AI

    Lloyds Banking Group expects its artificial intelligence programme to deliver more than £100 million in value this year — double the £50 million it attributes to generative AI in 2025. The figures, disclosed alongside the group’s annual results in January 2026, represent one of the more concrete attempts by a major financial institution to attach a number to what AI is actually worth.

    That specificity matters. As maddaisy examined last week, PwC’s 2026 Global CEO Survey found that 56% of chief executives still cannot point to measurable revenue gains from their AI investments. Lloyds is not claiming to have solved the ROI puzzle entirely, but it is doing something most enterprises have not: publishing the numbers and tying them to specific operational improvements rather than vague promises of transformation.

    The financial assistant: what it actually does

    The headline initiative is a customer-facing AI financial assistant, which Lloyds describes as the first agentic AI tool of its kind offered by a UK bank. Announced in November 2025 and scheduled for public rollout in early 2026, the assistant sits within the Lloyds mobile app and is designed to help customers manage spending, savings, and investments through natural conversation.

    The system uses a combination of generative AI for its conversational interface and agentic AI to process requests and execute actions. In practical terms, a customer can query a payment, ask for a spending breakdown, or request guidance on savings options — and the assistant will interpret the request, plan the necessary steps, and carry them out. Where it reaches the limits of what automated support can handle, it refers users to human specialists.

    The scope is intended to expand. Lloyds has said the assistant will eventually cover its full product suite, from mortgages to car finance to protection products, serving its 28 million customer accounts across the Lloyds, Halifax, Bank of Scotland, and Scottish Widows brands.

    Testing at scale, not in a lab

    Before public launch, Lloyds tested the assistant with approximately 7,000 employees, who collectively completed around 12,000 trials. That is a meaningful pilot — large enough to surface edge cases and failure modes that a controlled lab environment would miss, and conducted with users who understand the bank’s products well enough to stress-test the system’s accuracy.

    The employee testing sits alongside a broader internal AI deployment that has already delivered measurable results. Athena, the group’s AI-powered internal search assistant, is used by 20,000 colleagues and has reduced information search times by 66%. GitHub Copilot, deployed to 5,000 engineers, has driven a 50% improvement in code conversion for legacy systems. An AI-powered HR assistant resolves 90% of queries correctly on the first attempt.

    These are not experimental pilots. They are production tools used at scale, and the fact that Lloyds is willing to attach specific performance metrics to each one distinguishes its approach from the many enterprises that describe AI impact in qualitative terms only.

    The ROI question: credible or convenient?

    The £100 million figure invites scrutiny, and it should. “Value” in corporate AI disclosures is notoriously slippery — it can mean cost savings, time savings converted to a monetary equivalent, revenue uplift, or some combination of all three. Lloyds has not published a detailed methodology for how it arrived at the £50 million figure for 2025 or how it projects the 2026 target.

    That said, the bank’s approach has features that lend it more credibility than many comparable claims. The internal tools have named user populations and specific performance benchmarks. The customer-facing assistant was tested with thousands of employees before launch, not unveiled as a concept. And the 2025 figure is presented as a delivered outcome, not a forecast — a distinction that matters when most enterprises are still struggling to prove any return at all.

    Lloyds also rose 12 places in the Evident AI Global Index last year — the strongest improvement of any UK bank — suggesting that external assessors see substance behind the claims.

    Agentic AI in financial services: the governance dimension

    The move to customer-facing agentic AI in banking raises governance questions that go beyond what internal productivity tools require. As maddaisy explored earlier this week, Deloitte’s 2026 AI report found that only one in five enterprises has a mature governance model for agentic systems. When those systems move from internal search assistants to customer-facing financial advice, the stakes escalate considerably.

    A banking AI that can execute transactions, provide savings guidance, and eventually handle mortgage queries operates in regulated territory. The Financial Conduct Authority’s expectations around suitability, fair treatment, and clear communication apply regardless of whether the advice comes from a human or an algorithm. Lloyds has acknowledged this by building in human referral pathways, but the real test will come at scale — when millions of customers interact with the system simultaneously, and edge cases multiply.

    Ron van Kemenade, the group’s chief operating officer, has framed the launch as “a pivotal step in our strategy as we continue to reimagine the Group for our customers and colleagues.” Ranil Boteju, chief data and analytics officer, has positioned it as a demonstration of responsible deployment, noting that the assistant “can understand and respond to specific, hyper-personalised customer requests and retains memory to offer a more holistic experience, ensuring the generated answer is safe to present to customers.”

    What this signals for the sector

    Lloyds is not the first bank to deploy AI, nor the first to make bold claims about its value. What distinguishes this move is the combination of a concrete financial baseline (£50 million delivered), a named and tested product (the financial assistant), a clear expansion roadmap (full product suite), and an institutional commitment to upskilling (a new AI Academy for its 67,000 employees).

    For practitioners watching the enterprise AI landscape, the Lloyds case offers a useful reference point against the prevailing narrative of deployment fatigue and unproven returns. It does not resolve the broader ROI question — one bank’s results do not establish an industry pattern — but it does suggest that organisations which invest in specific, measurable use cases and test rigorously before launch can move beyond the proof-of-concept purgatory that still traps most enterprises.

    The harder question is what happens next. An AI assistant that helps customers check spending patterns is useful. One that advises on mortgages and investment products enters a different category of risk and regulatory complexity. How Lloyds navigates that expansion — and whether the £100 million value target holds up under the scrutiny of real-world deployment — will be worth watching over the months ahead.

  • Capgemini Added 82,300 Offshore Workers Last Year. So Much for AI Replacing Everyone.

    If artificial intelligence is about to make IT services firms obsolete, someone forgot to tell Capgemini. The French consultancy ended 2025 with 423,400 employees — up 24% year-on-year — after adding 82,300 offshore workers in a single year. Its offshore workforce now stands at 279,200, a 42% increase, representing two-thirds of total headcount.

    At the same time, the company is planning €700 million in restructuring charges to reshape its workforce for AI. It is positioning itself as, in CEO Aiman Ezzat’s words, “the catalyst for enterprise-wide AI adoption.”

    The contradiction is only apparent. What Capgemini’s numbers actually reveal is the gap between the market’s AI narrative and the operational reality of large-scale enterprise transformation.

    The WNS factor

    The headline headcount surge requires context. The bulk of those 82,300 new offshore employees came from Capgemini’s acquisition of WNS, the India-headquartered business process services firm, completed in late 2025. Cloud4C, another acquisition, contributed further. This was not organic hiring — it was a deliberate strategic bet on scaling offshore delivery capacity at precisely the moment investors were questioning whether such capacity has a future.

    The acquisition arithmetic is revealing. Capgemini’s 2026 revenue growth guidance of 6.5% to 8.5% includes 4.5 to 5 percentage points from acquisitions, primarily WNS. Strip out the acquired growth, and organic expansion looks more like 2% to 3.5%. The company needed WNS to hit its targets.

    WNS brings something specific: expertise in AI-powered business process services, particularly in financial services, insurance, and healthcare. The company had already identified around 100 cross-selling opportunities and signed an intelligent operations contract worth more than €600 million. This is not a firm buying bodies for the sake of scale. It is buying the delivery infrastructure needed to operationalise AI at enterprise level.

    The restructuring counterweight

    The other side of the equation is the €700 million restructuring programme, most of which will land in 2026. Capgemini describes this as adapting “workforce and skills” to align with demand for AI-driven services. In plainer terms: some roles are being eliminated or relocated, while others — particularly those requiring AI engineering, data science, and agentic AI expertise — are being created or upskilled.

    As maddaisy noted when examining Capgemini’s full-year results last week, generative and agentic AI accounted for more than 10% of group bookings in Q4, up from around 5% earlier in the year. The company has trained 310,000 employees on generative AI and 194,000 on agentic AI. The restructuring is not a contradiction of the hiring — it is the other half of the same workforce transformation.

    The pattern is expand first, optimise second. Acquire the delivery capacity, then reshape it. It is a playbook that makes more commercial sense than the market’s preferred narrative of AI simply deleting headcount.

    What the market gets wrong

    Capgemini’s share price has fallen roughly 26% in 2026, driven largely by investor anxiety that AI will cannibalise the IT services business model. The logic runs: if AI can automate code generation, testing, and business process management, why would enterprises pay consultancies to do it?

    Ezzat addressed this directly in the post-earnings call. “I don’t think clients are thinking this way about reduction,” he told MarketWatch. “Clients are looking at how critical it is for them to adopt AI and where it can have an impact.”

    The distinction matters. Enterprises are not replacing their consulting relationships with AI tools. They are asking their consultants to help them implement AI — which, for now, requires more people, not fewer. The skills are different. The delivery models are changing. But the demand for hands-on expertise in making AI work within complex organisational environments is, if anything, increasing.

    This aligns with what Ezzat told Fortune separately: “AI is a business. It is not a technology. It cannot just be used to keep the house running.” His argument is that CEOs who treat AI as an efficiency tool for individual departments are missing the larger opportunity — and the larger threat — of enterprise-wide transformation.

    The offshore model evolves, but does not disappear

    The shift to 66% offshore headcount is not a return to the labour arbitrage model of the 2000s. Capgemini’s onshore workforce held steady at 144,200. What changed is the nature of offshore work. WNS’s strength is in intelligent operations — business process services enhanced by AI, automation, and analytics. These are not call centres or basic coding shops. They are delivery centres where AI tools augment human workers on complex processes.

    This is broadly consistent with what the wider consulting industry is signalling. McKinsey’s deployment of 25,000 AI agents across its workforce, as maddaisy reported this week, points in the same direction: AI as workforce augmentation, not replacement. The difference is that McKinsey is building internally, while Capgemini is acquiring externally. Both are betting that the future of professional services involves more AI and more people, deployed differently.

    What practitioners should watch

    For consultants and enterprise leaders, Capgemini’s workforce data offers a useful reality check against the AI replacement narrative. Three signals are worth tracking.

    First, the restructuring outcomes. If the €700 million programme results in meaningful upskilling rather than simple headcount reduction, it will validate the “expand and reshape” model. If it quietly becomes a cost-cutting exercise, the AI transformation story weakens.

    Second, the organic growth rate. With acquisitions contributing nearly five percentage points of 2026 growth, the underlying business needs to demonstrate it can grow on its own merits. The Q4 acceleration to over 10% AI bookings was promising, but one quarter does not make a trend.

    Third, the onshore-offshore ratio over time. If AI genuinely transforms delivery models, the 66% offshore share should eventually stabilise or even decline as automation reduces the need for large delivery teams. If it keeps rising, the industry is still in the scale-up phase, and the productivity gains from AI remain further away than the marketing suggests.

    Capgemini’s apparent paradox — adding 82,300 people while betting its future on AI — is not a paradox at all. It is the messy, expensive reality of what enterprise AI transformation actually looks like on the ground: more complexity before less, more people before fewer, more investment before returns. The firms that navigate this transition phase successfully will define the next era of professional services. The market just needs to be patient enough to let them.

  • McKinsey’s 25,000 AI Agents: First-Mover Advantage or the Industry’s Biggest Experiment?

    McKinsey now counts 25,000 AI agents among its workforce — roughly one for every 1.6 human employees. That ratio, disclosed by CEO Bob Sternfels at the Consumer Electronics Show and confirmed by the firm, makes the consultancy’s internal agentic build-out one of the most aggressive in professional services.

    The numbers have moved quickly. Eighteen months ago, McKinsey operated a few thousand agents. Today, through its AI arm QuantumBlack, AI-related work accounts for 40% of the firm’s output. The agents have saved an estimated 1.5 million hours on search and synthesis tasks. Non-client-facing headcount has fallen 25%, yet output from those teams has risen 10%.

    Sternfels’s stated ambition is to pair every one of McKinsey’s 40,000 employees with at least one AI agent within the next 18 months.

    Scale versus substance

    The scale is eye-catching. Whether it is meaningful depends on what you count as an agent and how you measure the return.

    McKinsey’s rivals are openly sceptical. EY’s global engineering chief has argued that “a handful of agents do the heavy lifting” and that value should be tracked through efficiency KPIs, not headcount. PwC’s chief AI officer has called agent count “probably the wrong measure”, advocating instead for quality and workflow optimisation. Their counterargument is clear: a smaller fleet of high-performing agents, rigorously measured, may deliver more than a vast deployment still being calibrated.

    The critique lands on familiar ground. As maddaisy examined earlier today, PwC’s 2026 Global CEO Survey found that 56% of chief executives still cannot point to revenue gains from their AI investments. The deployment-versus-outcomes gap is the central tension in enterprise AI right now, and McKinsey’s bet raises the question of whether the firm is racing ahead of the same problem — or solving it.

    From advisory to infrastructure

    The more consequential shift may be in McKinsey’s business model. Sternfels described a move away from the firm’s traditional fee-for-service approach toward a model where McKinsey works with clients to identify joint business cases and then helps underwrite the outcomes.

    This is a significant departure for a firm built on advisory fees and billable hours. It positions McKinsey less as a strategic counsellor and more as an infrastructure partner — one that brings its own AI workforce to bear on client problems and shares in the measurable results.

    QuantumBlack, with 1,700 people, now drives all of McKinsey’s AI initiatives. Alex Singla, the senior partner who co-leads the unit, has described the firm’s evolving recruitment profile: candidates who can move fluidly between traditional consulting and engineering, and who can work alongside AI rather than simply directing it.

    Boston Consulting Group is pursuing a similar direction, deploying “forward-deployed consultants” who build AI tools directly on client projects. But McKinsey’s scale of internal adoption — 25,000 agents embedded across the firm — gives it a data advantage that is harder to replicate. Every internal deployment generates operational insight into what works, what fails, and how agentic systems behave at enterprise scale.

    The governance question maddaisy has been tracking

    The timing of McKinsey’s announcement is worth noting against the backdrop of the agentic AI governance gap maddaisy covered earlier this week. Deloitte’s data showed that only 21% of companies have mature governance models for agentic AI, even as three-quarters plan to deploy it within two years. And a broader pattern has emerged across maddaisy’s recent coverage: enterprises are strategically confident about AI but operationally underprepared.

    McKinsey, as both a deployer and an adviser, sits at the intersection of this tension. If the firm can demonstrate that 25,000 agents operate reliably at scale — with governance, measurement, and accountability frameworks to match — it will have built the most persuasive case study in the industry. If the agents outrun oversight, the reputational exposure is equally significant. When an AI agent produces an analysis and the recommendation proves wrong, the liability question is not academic.

    What practitioners should watch

    For consulting professionals and enterprise leaders watching this play out, three things matter more than the headline number.

    First, the metric that matters is not agent count but outcome attribution. McKinsey’s 1.5 million hours saved is a process metric. The firm’s shift to underwriting client outcomes suggests it understands the need to move beyond efficiency and toward measurable business impact — the same gap that PwC’s CEO Survey identified industry-wide.

    Second, the talent model is changing faster than many firms acknowledge. McKinsey’s search for hybrid consultant-engineers, and BCG’s forward-deployed model, signal that the traditional consulting skill set is being augmented, not just supported, by AI fluency. Firms that treat AI as a productivity tool rather than a workforce design challenge will fall behind.

    Third, scale creates its own governance requirements. As McKinsey’s own Carolyn Dewar argued in Fortune, the real risk is not the technology but how leaders manage the fear and trust dynamics that surround it. Deploying 25,000 agents without the organisational infrastructure to govern them would validate every concern the firm’s rivals have raised.

    McKinsey’s wager is that first-mover scale in agentic AI creates a compounding advantage — more data, better workflows, stronger client proof points. The industry is about to find out whether volume leads to value, or whether a smaller, sharper approach gets there first.

  • The AI ROI Crisis: Why 56% of CEOs Still Cannot Prove Value From Their AI Investments

    More than half of the world’s chief executives cannot point to revenue gains from their AI investments. That is not a fringe finding from an alarmist report — it is the central conclusion of PwC’s 2026 Global CEO Survey, which polled 4,454 business leaders and was released at Davos in January.

    The figure — 56% reporting no measurable revenue uplift from AI — lands at a moment when enterprise AI spending continues to accelerate. Budgets are growing, headcounts in AI-adjacent roles are expanding, and the consulting industry’s order books are thick with transformation mandates. Yet the returns remain stubbornly elusive for the majority. Only 12% of CEOs surveyed reported achieving both revenue growth and cost reduction from their AI programmes.

    This is not, as some commentary has framed it, evidence that AI does not work. It is evidence that most organisations have not yet figured out how to make it work — a distinction that matters enormously for practitioners and consultants navigating the current landscape.

    The pattern maddaisy has been tracking

    PwC’s data confirms a dynamic that maddaisy has examined from several angles over the past fortnight. Deloitte’s 2026 State of AI report revealed that enterprises feel more strategically confident about AI than ever, yet less operationally ready — a paradox the report’s authors attributed to weak data infrastructure, insufficient talent, and immature governance. Research published in Harvard Business Review, which maddaisy covered last week, found that AI tools were making employees busier rather than more productive, with efficiency gains absorbed by task expansion rather than redirected toward higher-value work.

    The ROI gap is what happens when these operational failures compound. Organisations adopt AI tools without redesigning workflows. They measure deployment (how many teams have access) rather than outcomes (what changed as a result). They invest in the technology layer while underinvesting in the organisational layer — the process changes, role redesigns, and measurement frameworks that turn a pilot into a production capability.

    A measurement problem masquerading as a technology problem

    One of the more revealing aspects of the PwC data is what it implies about how enterprises are tracking AI value. CEO revenue confidence sits at a five-year low of 30%, and this pessimism correlates with the inability to demonstrate AI returns. But the question is whether the returns genuinely are not there, or whether organisations simply lack the instrumentation to detect them.

    The answer, for many enterprises, is likely both. Some AI deployments are genuinely failing to deliver — deployed in the wrong processes, aimed at the wrong problems, or undermined by poor data quality. But others may be generating real value that never surfaces in the metrics that CEOs review. Time saved in middle-office processes, reduced error rates in document handling, faster iteration cycles in product development — these are real gains, but they rarely appear on a revenue line unless someone has built the measurement architecture to capture them.

    This is a familiar pattern in enterprise technology adoption. The early years of cloud computing saw similar complaints: organisations spent heavily on migration but struggled to quantify the business impact beyond infrastructure cost reduction. The value was real — in agility, speed to market, and developer productivity — but it took years for finance teams to develop frameworks that could track it. AI is following the same trajectory, with the added complication that its benefits are often diffuse, spread across many small improvements rather than concentrated in a single, measurable outcome.

    What the 12% are doing differently

    PwC’s survey found that the minority of organisations achieving both revenue and cost benefits from AI share common characteristics. They have invested in data foundations — not just data lakes and pipelines, but governance structures that ensure data quality, accessibility, and appropriate use. They have moved beyond isolated pilots to embed AI into core business processes. And they treat AI adoption as an organisational change programme, not a technology deployment.

    None of this is conceptually new. Consultancies have been advising clients on change management, data governance, and process redesign for decades. What is new is the speed at which the gap between leaders and laggards is widening. The 12% who have cracked the ROI equation are pulling ahead, using AI-generated insights to inform strategy, AI-automated processes to reduce costs, and AI-enhanced products to capture new revenue. The 56% who have not are still running pilots, still debating governance frameworks, and still struggling to answer the board’s most basic question: what are we getting for this money?

    The consulting industry’s uncomfortable position

    For the consulting sector, PwC’s findings create an awkward tension. The firms advising enterprises on AI strategy are, in many cases, the same firms whose clients cannot demonstrate returns. This is not necessarily a reflection of poor advice — the operational barriers to AI value are genuine and deep — but it does raise questions about what consulting engagements are actually delivering.

    As maddaisy noted when examining Capgemini’s recent results, CEO Aiman Ezzat explicitly framed the company’s direction as a shift “from AI hype to AI realism.” Generative AI bookings exceeded 8% of Capgemini’s total for the year, but the company’s 2026 revenue guidance fell slightly below analyst expectations — a reminder that even firms positioning AI at the centre of their strategy face questions about whether the investment is translating into proportional growth.

    The risk for consultancies is that the ROI gap erodes client confidence in AI-related engagements. If more than half of CEOs see no revenue benefit, the appetite for further AI spending — and the advisory services that accompany it — may tighten. The counter-argument, which PwC’s own data supports, is that the solution to poor AI returns is not less AI but better AI implementation. That is a consulting engagement waiting to happen, provided firms can credibly demonstrate that they know how to close the gap.

    What practitioners should watch

    Three developments will shape whether the AI ROI picture improves or deteriorates over the coming quarters.

    First, the regulatory environment is tightening. As maddaisy recently examined, the EU AI Act’s high-risk system requirements become enforceable in August 2026, and state-level legislation in the US is creating a fragmented compliance landscape. Compliance costs will add to the total cost of AI ownership, making the ROI equation harder to balance for organisations that have not already built governance into their deployment model.

    Second, the rise of agentic AI — systems that plan and execute tasks with minimal human oversight — will test whether organisations can capture value from more autonomous AI without losing control. Deloitte’s data showed a nearly fivefold increase in planned agentic AI deployments over the next two years, but only one in five companies has a mature governance model for autonomous agents. The ROI potential is significant; so is the risk of expensive failures.

    Third, and perhaps most importantly, watch for a shift in how organisations measure AI value. The enterprises that move beyond “did revenue go up?” to more granular metrics — cycle time reduction, error rate improvement, employee capacity freed for strategic work — will be better positioned to demonstrate returns and justify continued investment. The measurement framework may matter as much as the technology itself.

    PwC’s 56% figure is striking, but it is a snapshot of a transition, not a verdict on AI’s potential. The technology is not the bottleneck. Execution is. And for consultants and practitioners, that distinction is where the real work — and the real opportunity — lies.

  • The Productivity Trap: Why AI Tools Are Making Employees Busier, Not Better

    The promise of AI in the workplace has always rested on a simple equation: automate the routine, free up humans for higher-value work. It is the pitch that has launched a thousand consulting engagements and underpinned billions in enterprise software investment. But new research suggests the equation may be running in reverse.

    A study published in Harvard Business Review this month, based on eight months of ethnographic research at a US technology company with roughly 200 employees, found that AI tools did not reduce workload. They intensified it. Employees worked faster, took on more tasks, and felt busier than before — despite the efficiency gains that AI was supposed to deliver.

    The researchers, Aruna Ranganathan and Xingqi Maggie Ye from UC Berkeley’s Haas School of Business, identified three distinct mechanisms through which AI was quietly ratcheting up the pressure.

    Task expansion: doing more with less becomes doing more with the same

    The first mechanism was task expansion. When AI tools made certain tasks faster, employees did not use the freed-up time for strategic thinking or creative work. Instead, they absorbed responsibilities that had previously belonged to other roles. Product managers and designers started writing code. Researchers took on engineering tasks. Workers assumed duties that, in the researchers’ words, “might previously have justified additional help.”

    This is a pattern that will be familiar to anyone who has watched organisations respond to efficiency gains over the past two decades. The spreadsheet did not eliminate accounting departments — it gave accountants more to do. Email did not reduce communication overhead — it multiplied it. AI appears to be following the same trajectory, with one critical difference: the speed at which task expansion occurs is considerably faster.

    The study also identified a secondary burden. Engineers found themselves spending additional time reviewing their colleagues’ AI-assisted work, a form of quality assurance that had not existed before because the work itself had not existed before.

    The vanishing boundary between work and rest

    The second mechanism was the blurring of work-life boundaries. Employees began incorporating work into what had previously been downtime — lunch breaks, gaps between meetings, even waiting for files to load. Because interacting with an AI tool felt, as one participant put it, “closer to chatting than to undertaking a formal task,” the psychological barrier to picking up work during off moments effectively disappeared.

    This is a subtler form of intensification, and arguably a more dangerous one. The physical cues that traditionally separated work from rest — closing a document, leaving a desk, switching off a screen — lose their power when work can be initiated through a conversational prompt on any device. The distinction between being productive and being available collapses.

    The multitasking illusion

    The third mechanism was increased multitasking. With AI handling parts of each task, employees managed multiple active threads simultaneously, creating what the researchers described as “continual switching of attention.” Worse, because AI made fast output visible to colleagues, it raised speed expectations across teams. Employees felt pressure not just to work with AI, but to keep pace with the output norms that AI made possible.

    The result was a self-reinforcing cycle: AI accelerated certain tasks, which raised expectations for speed, which made workers more reliant on AI, which widened the scope of what they attempted. As one engineer told the researchers, they felt “busier than before” despite the supposed time savings.

    Not new, but newly documented

    It is worth noting that the HBR findings are not entirely without precedent. Research from the University of Chicago and the University of Copenhagen published last year found that AI chatbots saved workers only about an hour per week — and that the tools created enough new tasks to largely nullify even that modest gain. What Ranganathan and Ye have added is the ethnographic depth: eight months of direct observation, 40 interviews, and a detailed account of the mechanisms through which intensification occurs.

    For consulting practitioners, this distinction matters. The earlier studies quantified the problem. This one explains the pathways. And that makes it actionable.

    Connecting the dots: from strategy gap to people gap

    The timing of this research is significant. As maddaisy noted last week, Deloitte’s 2026 State of AI in the Enterprise report revealed a widening gap between strategic confidence and operational readiness. Forty-two per cent of companies consider their AI strategy highly prepared, yet fewer feel equipped to execute it.

    The burnout research suggests one reason that gap persists: organisations are measuring AI adoption by deployment metrics — tools rolled out, processes automated, tasks per hour — while ignoring the human cost of that adoption. The operational readiness problem is not just about data pipelines and integration architecture. It is about whether the people using these tools can sustain the pace that the tools enable.

    This also has implications for the AI governance frameworks now coming into force across Europe and beyond. Most governance discussions focus on algorithmic bias, data privacy, and transparency. Workforce wellbeing — whether AI deployment is creating sustainable working conditions — barely features. As enforcement mechanisms sharpen, that gap may become harder to defend.

    What practitioners should watch

    The HBR researchers recommend what they call “AI practice” — a set of organisational disciplines designed to counteract intensification. These include intentional pauses (structured breaks for assessment), sequencing (deliberate pacing rather than continuous output), and human grounding (protected time for dialogue and connection).

    These are not revolutionary ideas. They are, in essence, good management practices adapted for an AI-augmented workplace. But the fact that they need to be articulated at all tells a story about how many organisations are deploying AI tools without thinking through the second-order effects on their people.

    For consultancies advising on AI transformation, this research is a prompt to broaden the conversation. Deployment is not the finish line. If the tools make employees faster but not better — busier but not more effective — then the productivity gains that justified the investment may prove temporary, eroded by turnover, cognitive fatigue, and declining work quality.

    The question, as the researchers put it, is not whether AI will change work, but whether organisations will actively shape that change — or let it quietly shape them.

  • Strategically Ready, Operationally Stuck: What Deloitte’s 2026 AI Report Reveals About the Enterprise Gap

    Deloitte’s 2026 State of AI in the Enterprise report, based on a survey of 3,235 senior leaders across 24 countries, delivers a finding that should give every consulting practitioner pause: more companies than ever believe their AI strategy is sound, but fewer feel ready to actually execute it.

    The gap between strategic confidence and operational readiness is the defining tension in enterprise AI right now — and it has implications for how consultancies sell, deliver, and staff their AI practices.

    The preparedness paradox

    According to Deloitte’s survey, 42% of companies now consider their strategy highly prepared for AI adoption, up from the previous year. That sounds encouraging until you read the next line: those same organisations report feeling less prepared than before on infrastructure, data, risk management, and talent.

    This is not a minor statistical wrinkle. It suggests that enterprises have become more fluent in talking about AI — they can articulate a vision, identify use cases, perhaps even secure board-level backing — but the operational foundations needed to move from pilot to production remain weak. Worker access to AI rose by 50% in 2025, and organisations expect the number with 40% or more of AI projects in production to double within six months. Whether the infrastructure exists to support that ambition is another matter entirely.

    The skills picture reinforces the point. Insufficient worker skills were identified as the biggest barrier to integrating AI into existing workflows. The most common response — educating the broader workforce to raise AI fluency (53%) — is necessary but not sufficient. Far fewer organisations are redesigning roles, workflows, or career paths around AI. Education tells people what AI can do. Restructuring tells the organisation how to use it.

    Sovereign AI moves from policy to procurement

    One of the report’s more significant findings is the emergence of sovereign AI as a practical enterprise concern, not just a political talking point. In Singapore, 77% of businesses surveyed said that data residency and in-country or in-region compute considerations are now important to their strategic planning.

    This echoes a dynamic that maddaisy recently examined in the European context, where Capgemini’s CEO Aiman Ezzat outlined a pragmatic four-layer sovereignty framework while signing partnerships with all three major US hyperscalers. Deloitte’s data suggests that the same tension — between the desire for local control and the reality of global infrastructure — is playing out across Asia Pacific and the Middle East, not just Europe.

    As Computer Weekly reported, organisations in the Middle East are approaching a similar inflection point, with sovereign and agentic AI expected to define the next phase of digital transformation. The pattern is consistent: governments want control, enterprises want capability, and the consulting industry is positioning itself to broker the compromise.

    Agentic AI outpaces its guardrails

    Perhaps the most striking data point in the Deloitte report concerns agentic AI — systems designed to plan, execute, and optimise tasks with minimal human oversight. Usage is set to rise sharply over the next two years, but only one in five companies has a mature governance model for autonomous AI agents.

    In Singapore, the numbers are particularly stark: 72% of businesses plan to deploy agentic AI across several operational areas within two years, up from just 15% today. That is a nearly fivefold increase in deployment with governance frameworks that are, by the report’s own assessment, not yet fit for purpose.

    The use cases are real enough. Deloitte cites financial services firms using AI agents to capture meeting actions and track follow-through, airlines deploying agents for common customer transactions, and manufacturers using autonomous systems to optimise product development trade-offs. These are not speculative applications — they are in production. But the governance question is not academic either. When an AI agent autonomously rebooks a flight or commits to a procurement decision, the question of accountability, audit trails, and regulatory compliance becomes urgent.

    Accenture stops counting

    A useful counterpoint to Deloitte’s enterprise survey comes from the supply side. In December, Accenture announced that it would stop separately reporting its advanced AI bookings — a category covering generative, agentic, and physical AI — because the technology had become “so pervasive” it was embedded across nearly everything the firm delivers.

    CEO Julie Sweet framed this as a sign of maturity: AI is no longer a distinct workstream but a feature of all client engagements. Advanced AI bookings hit $2.2 billion in the first quarter of fiscal 2026, double the prior year. The company has reached its target of 80,000 AI and data professionals.

    There is a less generous reading, of course. Stopping disclosure also makes it harder for investors and analysts to track whether AI is generating new revenue or simply being relabelled within existing services. But taken alongside Accenture’s acquisition of Faculty, a UK-based AI firm, in February 2026, the direction is clear: the major consultancies are absorbing AI into their core delivery model rather than treating it as a separate practice.

    What practitioners should watch

    The Deloitte report’s most useful contribution is not its optimism about AI’s potential — the industry has no shortage of that — but its honest accounting of where enterprises actually stand. Three signals are worth tracking.

    First, the strategy-execution gap will drive consulting demand, but not the kind that involves slide decks and maturity assessments. Enterprises need help with the operational plumbing: data architecture, infrastructure modernisation, and workflow redesign. The consultancies that can deliver engineering alongside strategy will win the next phase.

    Second, sovereign AI is becoming a procurement criterion, not just a policy aspiration. For firms operating across multiple jurisdictions — which includes most enterprise consulting clients — this means every AI deployment now carries a compliance dimension that did not exist two years ago.

    Third, the governance gap around agentic AI is a genuine risk, not a theoretical concern. As autonomous systems move from pilots to production, the organisations that invested early in oversight frameworks will have a structural advantage. Those that did not will find themselves either slowing down or taking on liability they have not fully priced.

    The AI story in 2026 is less about whether the technology works — increasingly, it does — and more about whether organisations can build the operational, regulatory, and governance foundations to use it responsibly at scale. The Deloitte data suggests most are not there yet, but they think they are. That gap is where the real work begins.

  • Capgemini’s Sovereignty Playbook: Bridging Europe’s AI Ambitions and American Infrastructure

    In the space of a single week in early February, Capgemini signed sovereignty-focused partnerships with all three major US hyperscalers — Google Cloud, AWS, and Microsoft. Days later, CEO Aiman Ezzat used the company’s full-year results presentation to publicly dismiss calls for complete European tech autonomy.

    The juxtaposition was deliberate, and it tells a more interesting story than the headline financials. Capgemini is not just adding AI capabilities to its consulting portfolio. It is building a distinct commercial proposition around one of Europe’s most politically charged technology questions: who controls the infrastructure that enterprises depend on?

    The gap between rhetoric and reality

    European digital sovereignty has been a policy preoccupation for several years now, accelerated by concerns over US government data access, the dominance of American cloud providers, and the growing strategic importance of AI infrastructure. The European Commission has pushed for greater technological independence. Member states have launched sovereign cloud initiatives. The language of autonomy is everywhere.

    The reality, as Ezzat put it bluntly during the post-earnings call, is more complicated. “There is no such thing as absolute sovereignty,” he told journalists. “Nobody has it, because no one has sovereignty over the entire value chain required to deliver services.”

    This is not a controversial claim among practitioners, but it is a notable one for a CEO whose company is headquartered in Paris and whose chairman also leads the digital working group at the European Round Table for Industry. Ezzat has been discussing sovereignty with the European Commission in Brussels and at Davos. His position is informed, not casual.

    A four-layer framework

    Ezzat outlined what amounts to a practical sovereignty framework built around four layers: data, operations, regulation, and technology. His argument is that Europe has meaningful independence on the first three — data residency and governance, operational control over services, and regulatory authority through instruments like GDPR and the AI Act. The fourth layer, the underlying technology stack, is where US Big Tech dominance means full independence is neither achievable nor, in his view, desirable.

    Rather than pursuing autonomy at every layer, Capgemini’s approach is to offer clients “the right sovereignty solution based on the use case, the client environment, the government.” In practice, this means European-managed services running on American infrastructure — sovereign in governance and operations, pragmatic on technology.

    As maddaisy noted earlier this week in examining Capgemini’s full-year results, the company estimates that over 50% of service contracts will include sovereignty requirements by 2029, up from just 5% in 2025. That trajectory, if it holds, represents a structural shift in how enterprise IT contracts are structured across Europe.

    Three partnerships, one message

    The timing of Capgemini’s hyperscaler announcements was no coincidence. On 6 February, the company expanded its partnership with Google Cloud, establishing a Sovereign Cloud Delivery Practice and Centre of Excellence. Capgemini will operate as a Google Distributed Cloud air-gapped operator — meaning it can deliver fully managed services with total isolation from the public internet, suited to defence, intelligence, and critical infrastructure clients.

    On 9 February, a similar announcement followed with AWS, focused on sovereign-ready cloud and AI capabilities. Two days later, Capgemini formalised integrated sovereignty solutions with Microsoft. Three announcements in five days, each offering variations on the same theme: Capgemini as the European operator sitting between the client and the American cloud.

    This is a positioning play with genuine commercial substance. For European enterprises navigating tightening regulation — particularly public sector organisations, financial institutions, and healthcare providers — the question is not whether to use cloud services but how to use them in ways that satisfy increasingly specific sovereignty requirements. Capgemini is betting it can be the answer to that question.

    Where AI and sovereignty converge

    The sovereignty proposition becomes more compelling when combined with Capgemini’s broader AI pivot. Generative and agentic AI bookings exceeded 10% of group bookings in Q4 2025, and the company has trained 310,000 employees on generative AI and 194,000 on agentic AI — systems designed to take autonomous actions rather than simply generate content.

    AI workloads are particularly sensitive from a sovereignty perspective. They involve large volumes of proprietary data, often require access to regulated information, and increasingly touch decision-making processes that organisations want to keep within controlled environments. A sovereign AI solution — where the model runs on infrastructure governed under European jurisdiction, operated by a European firm, but built on the technical capabilities of a US hyperscaler — addresses a specific and growing need.

    Ezzat framed AI itself with characteristic pragmatism in a separate interview with Fortune. “AI is a business. It is not a technology,” he said, warning leaders against treating it as a “black box being managed separately.” His caution against AI FOMO — “You don’t want to be too ahead of the learning curve. If you are, you’re investing and building capabilities that nobody wants” — suggests a company that has learned from watching the metaverse hype cycle play out.

    What to watch

    Capgemini’s sovereignty strategy raises several questions worth tracking. First, whether the 50%-by-2029 estimate for sovereignty-embedded contracts proves accurate, or whether it reflects the kind of optimistic forecasting that consulting firms are prone to when promoting a new service line. Second, how European competitors — particularly Atos, which has its own sovereignty ambitions, and smaller European cloud providers — respond to Capgemini’s hyperscaler-partnered model. Third, whether the European Commission’s own stance on sovereignty tilts toward the pragmatic Capgemini position or toward more aggressive technological independence.

    For consultants and practitioners, the practical takeaway is straightforward: sovereignty is moving from a compliance checkbox to a structural feature of European enterprise contracts. The firms that build credible delivery capabilities around it now — not just policy positions, but operational partnerships and trained workforces — will have a meaningful advantage as regulation tightens. Capgemini has placed its bet. The question is whether the market follows.