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  • Capgemini’s CEO Makes the Unfashionable Case for Pacing Your AI Investment

    There is a particular kind of courage in telling a room full of executives to slow down. Aiman Ezzat, CEO of Capgemini, has been doing exactly that – and his reasoning deserves more attention than the typical “move fast or die” narrative that dominates AI strategy discussions.

    “You don’t want to be too ahead of the learning curve,” Ezzat told Fortune in February. “If you are, you’re investing and building capabilities that nobody wants.”

    Coming from the head of a €22.5 billion consultancy that has trained 310,000 employees on generative AI and is actively building labs for quantum computing, 6G, and robotics, this is not a counsel of inaction. It is a strategic position on pacing – one that puts Ezzat at odds with much of the technology industry’s current mood.

    The FOMO problem

    The fear of missing out on AI has become a boardroom affliction. Boston Consulting Group reports that half of CEOs now believe their job is at risk if AI investments fail to deliver returns. That pressure creates a predictable dynamic: spend big, move fast, worry about outcomes later.

    The data suggests the worry-later approach is not working. EY research shows that while 88% of employees report using AI at work, organisations are failing to capture up to 40% of the potential benefits. In the UK, only 21% of workers felt confident using AI as of January 2026. The tools are arriving faster than the capacity to use them well.

    Ezzat’s argument is that this gap is not a technology problem. It is a pacing problem. Companies are deploying AI capabilities ahead of their organisation’s ability to absorb them – and ahead of genuine customer demand for the outcomes those capabilities promise.

    AI is a business, not a technology

    The more substantive part of Ezzat’s case is about framing. Too many leadership teams, he argues, treat AI as “a black box that’s being managed separately” – a technology initiative bolted onto the existing business rather than a force reshaping how the business operates.

    “The question you have to focus on is: ‘How can your business be significantly disrupted by AI?’” Ezzat says. “Not ‘How is your finance team going to become more efficient?’ I’m sure your CFO will deal with that at the end of the day.”

    The distinction matters. Departmental efficiency projects – automating invoice processing, summarising meeting notes, generating marketing copy – are the low-hanging fruit that most enterprises are picking right now. They deliver incremental gains but rarely transform a business model. The harder question, the one Ezzat wants CEOs to sit with, is whether AI fundamentally changes what a company sells, how it competes, or what its customers expect.

    That question takes time to answer well. Rushing it produces expensive experiments that solve the wrong problems.

    The trust deficit

    Perhaps the most underexplored part of Ezzat’s argument is about human trust. “How do you get humans to trust the agent?” he asks. “The agent can trust the human, but the human doesn’t really trust the agent.”

    This cuts to a practical reality that technology roadmaps tend to gloss over. Agentic AI – systems designed to take autonomous actions rather than simply generate content – is the next wave of enterprise deployment. As maddaisy.com noted when covering Capgemini’s role in the OpenAI Frontier Alliance, the gap between a capable AI platform and a working enterprise deployment remains stubbornly wide. Trust is a significant part of why.

    Employees who do not trust AI agents will find ways to work around them. Managers who cannot explain AI-driven decisions to clients will revert to manual processes. Organisations that deploy autonomous systems faster than their culture can absorb them will create friction, not efficiency.

    Ezzat draws an analogy to ergonomics – the mid-twentieth century discipline of designing tools for humans rather than forcing humans to adapt to tools. “Bad chairs lead to bad backs,” he observes. “Bad AI is likely to be far more consequential.”

    Consistent with Capgemini’s own playbook

    What makes Ezzat’s position credible is that Capgemini’s recent actions align with it. The company’s approach has been to invest broadly but scale selectively.

    As maddaisy.com’s analysis of the company’s 2025 results highlighted, generative AI bookings rose above 10% of total bookings in Q4 – meaningful but not yet dominant. The company maintains labs for emerging technologies including quantum and 6G, keeping a foot in multiple possible futures without betting the firm on any single one.

    Meanwhile, the company added 82,300 offshore workers in 2025 – largely through the WNS acquisition – while simultaneously earmarking €700 million for workforce restructuring. The message is clear: AI changes the shape of the workforce, but it does not eliminate the need for one. Building the human infrastructure to deliver AI at scale takes as much investment as the technology itself.

    The metaverse lesson

    Ezzat’s most pointed comparison is to the metaverse – a technology that commanded billions in corporate investment before the market concluded that customer demand had been dramatically overstated. Capgemini itself experimented with a metaverse lab. Mark Zuckerberg renamed his company around it. Now, as Ezzat puts it, “like air fryers, its time may now have passed.”

    The parallel is not that AI will follow the metaverse into irrelevance – the use cases are far more concrete, and the enterprise adoption data is already stronger. The point is about the cost of overcommitment. Companies that invested heavily in metaverse capabilities before the market was ready wrote off those investments. The same risk exists with AI, particularly in areas like agentic systems where the technology’s capability is advancing faster than organisational readiness to use it.

    Ezzat’s prescription is agility over ambition: small pilots, constant monitoring, and the willingness to scale rapidly when adoption genuinely accelerates. “We have to be investing – but not too much – to be able to be aware of the technology, following at the speed to make sure that we are ready to scale when the adoption starts to accelerate.”

    What this means for practitioners

    For consultants advising clients on AI strategy, Ezzat’s framework offers a useful counterweight to the prevailing urgency. The question is not whether to invest in AI – that debate is settled. The question is how to pace that investment so that capability, demand, and organisational readiness move roughly in step.

    Companies that get the pacing right will avoid the twin traps of overinvestment (building capabilities nobody wants) and underinvestment (being caught flat-footed when adoption accelerates). In a market where half of CEOs fear for their jobs over AI outcomes, the discipline to move at the right speed – rather than the fastest speed – may prove to be the more valuable skill.

  • OpenAI’s Frontier Alliance Confirms What Consultants Already Knew: AI Vendors Cannot Scale Alone

    OpenAI announced on 23 February that it has formed multi-year “Frontier Alliances” with McKinsey, Boston Consulting Group, Accenture, and Capgemini. The four firms will help sell, implement, and scale OpenAI’s Frontier platform — an enterprise system for building, deploying, and governing AI agents across an organisation’s technology stack.

    For readers who have been following maddaisy’s coverage of the consulting industry’s AI pivot, this is not a surprise. It is the logical next step in a pattern that has been building for months — and it tells us more about the limits of AI vendors than about the ambitions of consulting firms.

    The vendor cannot scale alone

    The most revealing line in the announcement came from Capgemini’s chief strategy officer, Fernando Alvarez: “If it was a walk in the park, OpenAI would have done it by themselves, so it’s recognition that it takes a village.”

    That candour is worth pausing on. OpenAI’s enterprise business accounts for roughly 40% of revenue, with expectations of reaching 50% by the end of the year. The company has already signed enterprise deals with Snowflake and ServiceNow this year and appointed Barret Zoph to lead enterprise sales. Yet it still needs consulting firms — with their existing client relationships, implementation expertise, and organisational change capabilities — to get its technology into production at scale.

    This is not a story about OpenAI’s generosity in sharing the enterprise market. It is an admission that the gap between a capable AI platform and a working enterprise deployment remains stubbornly wide. As maddaisy reported last week, PwC’s 2026 CEO Survey found that 56% of chief executives still cannot point to measurable revenue gains from their AI investments. The technology is not the bottleneck. Integration, governance, and organisational readiness are.

    A clear division of labour

    The alliance structure reveals how OpenAI sees the enterprise AI value chain. McKinsey and BCG are positioned as strategy and operating model partners — helping leadership teams determine where agents should be deployed and how workflows need to be redesigned. BCG CEO Christoph Schweizer noted that AI must be “linked to strategy, built into redesigned processes, and adopted at scale with aligned incentives.”

    Accenture and Capgemini take the systems integration role: data architecture, cloud infrastructure, security, and the unglamorous work of connecting Frontier to the CRM platforms, HR systems, and internal tools that enterprises actually run on. Each firm is building dedicated practice groups and certifying teams on OpenAI technology. OpenAI’s own forward-deployed engineers will sit alongside them in client engagements.

    This two-tier model — strategy at the top, integration at the bottom — maps neatly onto the consulting industry’s existing hierarchy. It also creates a clear dependency: OpenAI provides the platform, the consultancies provide the last mile.

    The maddaisy continuity thread

    This announcement intersects with several stories maddaisy has been tracking. When we examined McKinsey’s 25,000 AI agent deployment, the question was whether the firm’s aggressive internal build-out was a first-mover advantage or an expensive experiment. The Frontier Alliance suggests McKinsey is now positioning that internal capability as a credential — evidence that it can deploy agentic AI at scale, which it can now offer to clients through the OpenAI partnership.

    Similarly, when maddaisy covered the shift from billable hours to outcome-based consulting, the question was how firms would make the economics work. Vendor alliances like this provide part of the answer: the consulting firm brings the implementation expertise, the AI vendor provides the platform, and the client pays for outcomes rather than hours. The risk is shared across the chain.

    And Capgemini’s dual bet — adding 82,300 offshore workers while simultaneously investing in AI — now makes more strategic sense. The offshore delivery capacity is precisely what is needed to operationalise Frontier at enterprise scale. The bodies and the bots are not competing; they are complementary.

    The SaaS vendors should be nervous

    As Fortune noted, the Frontier Alliance creates a specific tension for established software-as-a-service vendors. Salesforce, Microsoft, Workday, and ServiceNow all depend on these same consulting firms to market and deploy their products. Now those consultants will also be actively promoting an alternative platform — one that positions itself as a “semantic layer” sitting above the traditional SaaS stack.

    The consulting firms are not choosing sides. They are hedging. Accenture, for instance, signed a multi-year partnership with Anthropic in December 2025 and is now a Frontier Alliance member. The firms will sell whichever platform best fits a given client’s needs, which gives them leverage over the AI vendors rather than the other way around.

    For the SaaS incumbents, however, having McKinsey and BCG actively evangelise an AI-native alternative to C-suite buyers is a development they will not welcome. Investor anxiety in this space is already elevated — shares of several enterprise software companies have been punished over concerns that customers will choose AI-native platforms over traditional offerings.

    What to watch

    The Frontier Alliance is a partnership announcement, not a set of outcomes. The real test is whether this model — AI vendor plus consulting firm — can close the deployment gap that has kept enterprise AI adoption stubbornly below expectations.

    Three things matter from here. First, whether the certified practice groups produce measurably better outcomes than the piecemeal implementations enterprises have been attempting on their own. Second, whether Frontier’s “semantic layer” architecture genuinely simplifies agent deployment or simply adds another platform layer to an already complex stack. And third, whether the consulting firms’ simultaneous alliances with competing AI vendors — OpenAI, Anthropic, Google — create genuine client value or just a more complicated sales cycle.

    For practitioners, the immediate signal is clear: the enterprise AI market is consolidating around a vendor-plus-integrator model. If your organisation is planning an agentic AI deployment, the question is no longer which model to use. It is which combination of platform, integrator, and operating model redesign will actually get agents into production — and keep them there.

  • 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.

  • SAP’s “Break Glass” Cloud Plan Exposes the Limits of European Digital Sovereignty

    SAP, Microsoft, Capgemini, and Orange have announced a joint contingency plan for European cloud services — a “break glass” option in case US hyperscalers are legally blocked from operating in Europe. The partnership, routed through SAP’s German subsidiary Delos Cloud and the French entity Bleu (co-owned by Capgemini and Orange), promises business continuity in crisis scenarios ranging from sanctions to military conflict.

    It is a notable development, and it connects directly to the sovereignty narrative maddaisy.com has been tracking. But before treating it as a solution, it is worth examining what the plan actually offers — and what analysts say it cannot.

    The deal in context

    When maddaisy examined Capgemini’s sovereignty strategy earlier this month, the picture was clear: European digital sovereignty is converging on a pragmatic middle ground. Rather than building independent infrastructure from scratch, European firms are positioning themselves as trusted operators running workloads on American hyperscaler platforms — sovereign in governance and operations, reliant on US technology underneath.

    The SAP-Microsoft-Capgemini-Orange agreement is the logical extension of that approach. SAP’s announcement describes a mutual assistance framework where Delos Cloud and Bleu would cooperate on cross-border crisis response, including “early detection, analysis, defence, and remediation of cyber incidents.” Separately, Delos Cloud and Microsoft signed a business continuity agreement allowing Delos to access source code and maintain operations if sanctions restrict Microsoft’s European services.

    In other words: if the worst happens, European operators would run a local copy of Azure, disconnected from Microsoft’s global network.

    The wildcard is Washington, not Brussels

    Analysts are broadly aligned on one point: the EU itself is highly unlikely to block American cloud providers. Some 75% of the European cloud market sits with US hyperscalers, according to Forrester senior analyst Dario Maisto. Cutting off that access would amount to economic self-harm on a significant scale.

    The real concern is the reverse scenario — the US government using its leverage over hyperscalers to pressure European governments. As Maisto put it to CIO: “What if the US administration pulls the kill switch? It would be the weaponisation of IT, because the US knows about this dependency.”

    Danilo Kirschner, managing director at European cloud consulting firm Zoi, was blunter: “There have been non-logical, nonsensical decisions in the past year. From a European perspective, we need to prepare for anything.”

    The likelihood of such a scenario remains low. But the fact that SAP and Microsoft are publicly planning for it signals that enterprise customers are asking uncomfortable questions — and expect answers.

    A lifeboat, not a luxury liner

    The technical reality is where the plan runs into difficulty. Running a severed version of Azure in a European data centre sounds feasible in a press release. In practice, as Kirschner explained, Azure is millions of lines of code updated daily. Disconnected from Microsoft’s global security intelligence, engineering updates, and optimisation pipelines, a local copy would degrade rapidly.

    “This is a lifeboat, not a luxury liner,” Kirschner said. “Your disaster recovery plans must account for the fact that a sovereign cloud in crisis mode will likely be a static, maintenance-only environment.”

    The hardware question compounds the problem. Azure runs on proprietary, custom-designed server infrastructure. If geopolitical tensions are severe enough to block software access, sourcing replacement hardware under the same sanctions regime becomes equally difficult. And if a crisis lasts months rather than weeks, the global Azure platform will have evolved while the European fork remains frozen — creating what Kirschner described as “a technological dead end that requires a total rebuild to reconnect.”

    Even the legal framework is untested. “This agreement will have to be tested in court once the problem happens, when it could be too late,” Maisto noted. “This is not compliance as much as risk management.”

    The sovereignty paradox deepens

    There is an irony at the heart of this deal that Kirschner identified clearly: by offering a break-glass option for European sovereignty, Microsoft has paradoxically strengthened its own position. The single biggest political risk to using American hyperscalers in the European public sector — the theoretical possibility of a forced disconnection — has been partially neutralised. European governments and enterprises can now point to a contingency plan, however imperfect, and continue building on US infrastructure.

    As maddaisy’s earlier analysis of Capgemini’s sovereignty framework noted, Capgemini CEO Aiman Ezzat has been candid that “there is no such thing as absolute sovereignty” because no entity controls the entire value chain. The SAP deal underscores that position. Europe is not building an alternative to American cloud infrastructure. It is building contingency plans that assume American cloud infrastructure remains the default.

    For hardliners in France and elsewhere who want European-built alternatives at the highest sovereign classification levels, this approach will be unsatisfying. But the practical question — what is the alternative? — remains unanswered. The European Cybersecurity Certification Scheme continues to evolve, yet the gap between regulatory ambition and infrastructure reality shows no sign of closing.

    What practitioners should take from this

    For enterprise architects and CIOs managing European workloads, the SAP-Microsoft-Capgemini deal changes the conversation without changing the underlying calculus. It provides a political answer to a political risk — a contingency plan that reassures procurement committees and satisfies sovereignty checkboxes. It does not, however, solve the fundamental dependency.

    The practical takeaway is threefold. First, organisations should treat this as risk management, not a guarantee — the plan’s viability in a real crisis remains unproven and potentially short-lived. Second, workload portability and multi-cloud strategies become more important, not less, in a world where even the contingency plans assume degraded service. Third, the sovereignty requirements that Capgemini estimated would feature in over 50% of European service contracts by 2029 are becoming structurally embedded in how deals are structured — and this agreement is part of that shift.

    Europe’s cloud sovereignty story is not moving toward independence. It is moving toward managed dependency, with increasingly elaborate safety nets. Whether those nets would hold under real stress is a question no one can answer yet — and the honest participants in this deal are not pretending otherwise.

  • 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.

  • Capgemini’s AI Pivot: From Hype to Realism, with €22.5 Billion at Stake

    Capgemini’s full-year 2025 results, released on 13 February, offered one of the clearest snapshots yet of how a major IT consultancy is repositioning itself around artificial intelligence — and the distance still to travel.

    The French group reported revenues of €22.5 billion, up 3.4% at constant currency. Growth was modest by historical standards, weighed down by cautious enterprise spending across Continental Europe. But the fourth quarter told a different story: 10.6% growth, suggesting that client hesitancy may be starting to ease.

    From hype to realism

    CEO Aiman Ezzat framed the company’s direction as a shift “from AI hype to AI realism” — a phrase that carries weight given how frequently the consultancy sector has leaned on AI as a growth narrative without always delivering measurable outcomes.

    The numbers behind the claim are worth examining. Generative AI bookings exceeded 8% of Capgemini’s total for the year, rising above 10% in Q4. The company has trained 310,000 employees on generative AI and 194,000 on agentic AI — the emerging category of AI systems designed to take autonomous actions rather than simply generate content.

    These are significant investments in capability. Whether they translate into proportional revenue growth remains the open question. Capgemini’s 2026 guidance of 6.5% to 8.5% revenue growth fell slightly below the 7.2% analyst consensus, suggesting the market expected more from a company positioning AI at the centre of its strategy.

    The sovereignty opportunity

    Beyond AI, Capgemini is betting on a second structural trend: digital sovereignty. The company estimates that over 50% of service contracts will include sovereignty requirements by 2029, up from just 5% in 2025. For a European-headquartered firm, this represents a genuine competitive advantage over US-based rivals.

    Ezzat was notably pragmatic on the sovereignty question, dismissing calls for full European tech autonomy. “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.” Instead, he advocated for finding “the right sovereignty solution based on the use case, the client environment, the government.”

    This positions Capgemini as a bridge between European regulatory ambitions and the practical reality that most enterprise infrastructure still runs on AWS, Google Cloud, and Microsoft Azure. The company has signed partnerships with all three US hyperscalers to deliver what it calls “sovereign” AI solutions — European-managed services running on American infrastructure.

    The uncomfortable parts

    Not everything in the results paints a straightforward growth picture. Net income declined 4.2% year-on-year to €1.6 billion. The stock has fallen approximately 29% so far in 2026, caught in a broader selloff of companies perceived as vulnerable to AI-driven disruption — an ironic position for a firm making AI the centrepiece of its strategy.

    There is also the matter of Capgemini Government Solutions, the US subsidiary the company announced it would sell following public backlash over a $4.8 million contract with US Immigration and Customs Enforcement. It is a reminder that the consulting business operates in political as well as commercial environments, and reputational risk can force strategic decisions that have little to do with market fundamentals.

    France, Capgemini’s home market, contracted by 4.1% — a concern for a company headquartered in Paris. The bright spots were North America (7.3% growth), the UK and Ireland (10.5%), and Asia Pacific and Latin America (13.8%). Financial Services led sectors with 9.2% growth, accelerating sharply to 20.4% in Q4.

    What to watch

    Capgemini’s results matter beyond its own balance sheet because they reflect broader dynamics affecting the consultancy and IT services sector. The shift from selling AI as a concept to delivering AI as an operational capability is where the next wave of value — and differentiation — will come from.

    Three things are worth tracking. First, whether GenAI bookings continue to accelerate past the 10% threshold reached in Q4, or whether that was an end-of-year surge. Second, how the sovereignty proposition develops as European regulation tightens — this could become a meaningful differentiator for European-headquartered firms. Third, whether the “Fit-for-growth” restructuring programme, which will cost an additional €200 million in cash outflow, delivers the operational efficiency the company is banking on.

    The consultancy sector has spent the better part of two years talking about AI. Capgemini’s latest results suggest it is now moving into the phase of proving it can deliver.