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.