Capgemini's chief executive, Aiman Ezzat, told analysts that companies seeking to deploy artificial intelligence at scale will first need to overhaul technology systems that in many cases have accreted over decades. He described the necessary work as a multi-year cycle of investment in data, software and core infrastructure.
The comments followed Capgemini raising its 2026 revenue growth target, a move the company attributed to stronger bookings. Ezzat argued the main barrier to broader AI adoption is not the availability of models but the state of legacy systems, dispersed data and the complexity of technology estates that organisations have built up over many years.
"Every organization today wants to become agentic," Ezzat said, using the term to refer to AI systems configured to carry out multi-step tasks. "But before they can become agentic, they must become AI-ready, and most are not."
Capgemini expects a "multi-year modernization supercycle" as companies upgrade the foundations required to support AI across their operations. Upgrades will include investments in data platforms, enterprise applications and underlying infrastructure designed to enable AI tools to work across business processes.
Ezzat pointed to accumulated technical debt as a constraint for many businesses. He said years of fragmented and incompatible systems have left data scattered, complicating the ability of AI tools to access reliable information or to execute tasks consistently across an organisation.
He noted that while generative AI applications can generate responses, they often falter when asked to perform consistent business processes if the underlying systems remain disconnected. "AI is not only creating demand for new business capability; it’s also accelerating the modernization of the technology foundation on which those capabilities depend," Ezzat added.
Despite those challenges, Ezzat said companies remain willing to put money into AI initiatives. However, spending patterns are shifting: clients are prioritising comprehensive, large-scale transformation programmes rather than isolated experiments or short pilot projects.
The company’s revised 2026 revenue growth target and stronger bookings underline the commercial implications Capgemini sees in this push toward modernising IT estates to support AI at scale.