Capgemini is forecasting 6.5-8.5% revenue growth in 2026, and the French IT giant is making no secret of what’s driving that confidence: artificial intelligence is moving from conversation topic to budget line item for enterprise clients, and Capgemini wants to be the firm implementing it.
CEO Aiman Ezzat described a “clear pivot” toward AI-led transformation programs, and the numbers from Q4 2025 back up the strategic shift. Generative and agentic AI now account for more than 10% of quarterly bookings, up from 5% earlier in the year. That doubling in AI’s share of new business isn’t a rounding error—it’s a signal about where enterprise spending is heading.
The Financial Foundation
Capgemini’s 2025 results give the 2026 forecast credibility. The company delivered €22.5 billion in revenue, up 1.7% year-over-year, and €1.6 billion in net profit despite genuine macro headwinds. Q4 bookings hit €7.2 billion, a 9.1% increase driven by the integration of the WNS acquisition and accelerating AI services demand.
North America was the standout performer, growing 20% quarterly—a number that reflects both the maturity of AI adoption among large U.S. enterprises and Capgemini’s positioning in that market. The 11% constant-currency revenue surge in North America for the full year tells a similar story. European markets outside France, the UK, and Ireland returned to expansion after a difficult period, adding geographic breadth to what had been a more concentrated growth story.
Operating margins are expected to land between 13.6% and 13.8% in 2026—stable performance for a firm investing heavily in capability buildout while managing the transition from traditional IT services to AI-led transformation work.
The share price tells a different story. Capgemini is down nearly 30% year-to-date, reflecting broader tech sector anxiety about AI disruption rather than anything specific to Capgemini’s fundamentals. The market is pricing in disruption risk; the company’s bookings suggest clients are pricing in transformation opportunity.
What the AI Pivot Actually Looks Like
Ezzat’s description of agentic workflows as “automating complex processes beyond RPA capabilities” is worth unpacking. Robotic process automation—the previous generation of enterprise automation—handled well-defined, rules-based tasks: moving data between systems, filling forms, triggering standard workflows. It was valuable but brittle, breaking whenever processes changed or exceptions appeared.
Agentic AI handles ambiguity. It can reason about novel situations, coordinate across multiple systems, escalate appropriately, and adapt to changing conditions without human intervention at each step. The difference between RPA and agentic AI is roughly the difference between a conveyor belt and a logistics manager. One executes a fixed sequence; the other makes decisions.
Capgemini’s strengthening of the SAP RISE platform with custom AI agents reflects where large enterprise transformation is actually happening. SAP runs the core business processes of a significant fraction of the world’s large companies—finance, supply chain, human resources, procurement. Embedding AI agents into those workflows means AI is operating at the center of how these businesses function, not on the periphery.
Intelligent supply chain offerings gaining traction makes sense in the current environment. Supply chains that experienced severe disruption during and after the pandemic are now being rebuilt with AI-driven visibility and responsiveness as design requirements rather than optional features. Capgemini is positioned to implement those systems at enterprise scale.
Generative AI surpassing cybersecurity as the top investment priority in transformation programs is a meaningful data point about how enterprise priorities have shifted. Cybersecurity has been the dominant IT investment theme for years. Generative AI overtaking it in budget priority signals genuine commitment rather than exploratory spending.
The Acquisition Strategy
Acquisitions are expected to contribute 4.5 to 5 percentage points of Capgemini’s 2026 growth target—a substantial portion of the overall forecast. The WNS acquisition bolsters AI-enhanced business process services, adding capabilities in analytics-driven outsourcing that complement Capgemini’s consulting and technology implementation work. Clou4C adds cloud migration and optimization expertise that’s increasingly inseparable from AI deployment.
The acquisition strategy reflects a pattern across major consultancies: building AI capability faster through acquisition than organic development, particularly in specialized vertical knowledge and proprietary data assets that make AI models more effective for specific industries.
Turning Disruption Into Opportunity
One of the more interesting elements of Capgemini’s positioning involves the Anthropic warnings about AI making software coding roles obsolete. Rather than treating this as a threat to its technology services workforce, Capgemini is framing coding obsolescence as a services opportunity.
The logic holds up. If AI dramatically accelerates code generation, enterprises still need help with architecture decisions, system integration, security review, quality assurance, and the organizational change management required to actually deploy and operate AI-generated code in production environments. The skills required shift, but the need for expert guidance through complex technology transformation doesn’t disappear—it arguably intensifies as the pace of change accelerates.
Migration and optimization work—helping enterprises move existing systems to AI-compatible architectures, cleaning and structuring data for AI training, and optimizing AI deployments for cost and performance—represents a substantial addressable market that grows as AI adoption grows.
Client Hesitation and Efficiency Mandates
Capgemini acknowledges client hesitation on discretionary spending, which is real and reflects the broader macro environment. When CFOs are uncertain about economic conditions, technology projects that don’t have clear near-term ROI get delayed or cancelled.
But efficiency mandates are creating a countervailing force. When organizations face pressure to reduce costs without reducing output, AI-driven automation becomes a budget priority rather than a discretionary investment. Cloud, data infrastructure, and AI projects that demonstrably reduce operating costs are getting funded even when discretionary transformation spending is frozen.
This creates a bifurcated market that plays to Capgemini’s strengths. The firm can position AI implementations both as growth investments (for clients focused on revenue acceleration) and as efficiency tools (for clients focused on cost reduction), addressing different budget conversations with the same underlying capability.
The Broader Consultancy Race
Capgemini’s pivot mirrors moves across the major global consultancies. Accenture, Deloitte, McKinsey, and IBM are all racing toward agentic AI deployment capabilities, and the competitive differentiation is increasingly about which firm has the deepest vertical knowledge, the most effective implementation methodology, and the strongest relationships with the AI platform providers whose technology underlies client deployments.
The 1:30 human-to-agent ratios being reported at companies like Intuit and Uber validate the scale of transformation being implemented—and the complexity of implementing it. Getting to those ratios requires careful process redesign, change management, technical integration, and ongoing optimization. That’s consulting work at its core, regardless of how the underlying technology evolves.
PwC’s forecast of 5% AI budget allocation across enterprises aligns with Capgemini’s trajectory and suggests the demand environment supporting 6.5-8.5% growth has structural rather than cyclical foundations.
What the Numbers Mean
For investors looking at Capgemini’s 30% share price decline against its operational performance, the gap is striking. €22.5 billion in revenue, positive net profit, accelerating AI bookings, and a credible growth forecast don’t typically produce a 30% discount to prior valuations.
The market is essentially pricing in significant disruption risk—the possibility that AI transformation of software development and IT services will compress the market for traditional consulting and implementation work faster than Capgemini can pivot to AI-native services. That’s a legitimate concern, but Capgemini’s Q4 bookings data suggests the pivot is already underway and gaining momentum.
The enterprise AI spending cycle that Capgemini is positioning to capture is early. The 10% of bookings from generative and agentic AI today becoming 30% or 40% in two to three years is a plausible trajectory if enterprise adoption continues on its current path. At that point, the disruption narrative and the opportunity narrative converge—Capgemini either executes the pivot successfully or it doesn’t, and 2026’s booking trends will be an early indicator of which direction it’s heading.
