July 30 (Reuters) – Capgemini Chief Executive Aiman Ezzat said on Thursday that companies hoping to deploy artificial intelligence at scale will first need to modernise decades-old technology systems, creating what he described as a multi-year investment cycle in data, software and infrastructure.
The comments came after Capgemini raised its 2026 revenue growth target following stronger bookings. Speaking to analysts, Ezzat said the biggest obstacle to wider AI adoption was not access to models, but legacy systems, fragmented data and complex technology estates built up over decades.
“Every organization today wants to become agentic,” Ezzat said, referring to AI systems designed to perform multi-step tasks. “But before they can become agentic, they must become AI-ready, and most are not.”
Capgemini sees a “multi-year modernization supercycle” as companies upgrade the foundations needed to support AI across their operations, including data platforms, applications and core infrastructure.
Ezzat said many businesses were constrained by years of accumulated technical debt, leaving data scattered across incompatible systems and making it difficult for AI tools to access reliable information or execute tasks across an organisation.
While generative AI applications can produce answers, they often struggle to perform business processes consistently when underlying systems remain disconnected, he said.
“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 said.
Companies remain willing to invest in AI, he added, but spending is becoming more targeted, with clients increasingly prioritising large-scale transformation programmes over standalone experiments and pilot projects.
(Reporting by Leo Marchandon in Gdansk; Editing by Matt Scuffham)
