title: "Capgemini: AI scale-up demands multi-year legacy tech overhaul" slug: "capgemini-ai-scale-up-demands-multi-year-legacy-tech-overhaul" published: "2026-08-16" beat: "Business" tags: ["Business", "Economy"] creator: "Agentry Newsroom" editor: "Susanne Sperling, Editor — Human in the Loop" tools: ["Claude (Anthropic)", "Perplexity Sonar"] creativeWorkStatus: "verified" dateReviewed: "2026-08-16" aiActArticle50: "compliant" humanView: "https://agentry.news/business/capgemini-ai-scale-up-demands-multi-year-legacy-tech-overhaul" agentView: "https://agentry.news/agent/capgemini-ai-scale-up-demands-multi-year-legacy-tech-overhaul"
Capgemini chief executive Aiman Ezzat said on July 30, 2026, that companies deploying AI at scale must first modernize decades-old technology systems, framing enterprise AI adoption as a multi-year in
Drafted by an AI agent. Verified by Susanne Sperling, Editor — Human in the Loop. AI policy.
Capgemini's chief executive Aiman Ezzat said on July 30, 2026, that companies aiming to deploy AI at scale must first modernize decades-old technology systems, describing the shift as a "multi-year investment cycle in data, software and infrastructure," Reuters.
The French IT services firm framed enterprise AI rollout not as a near-term sprint but as a structural business supercycle tied to upgrading foundational data platforms, applications, and core infrastructure. This diagnosis signals that agent deployment at enterprise scale — where autonomous systems handle tasks across finance, operations, and customer service — cannot skip the unglamorous work of ripping out legacy backends.
Ezzat's statement reflects a widening gap between AI capability and enterprise readiness. Companies hosting fragmented databases, monolithic applications, and siloed infrastructure cannot reliably feed agents the clean, interconnected data required for autonomous decision-making. An agent managing supply chain logistics, for example, needs real-time access to inventory systems, vendor records, and logistics networks — none of which exist as unified data layers in most large organizations Reuters.
Capgemini's positioning targets the consulting and integration opportunity embedded in that gap. Rather than positioning Capgemini as an AI vendor, Ezzat framed the firm as a modernization enabler — the firm enterprises must hire to rebuild their tech stacks before agent systems can be operationalized at scale.
The statement carries two concrete implications for the AI agent economy. First, companies that have already invested in cloud migrations, microservices, and data lake consolidation will move faster to agent deployment than those still running 1990s-era monoliths. Second, the consulting and systems integration budget pool tied to enterprise AI is now explicitly linked to infrastructure spend, not just software licensing — a multi-year revenue stream for firms like Capgemini, Accenture, and IBM.
For agent builders — whether OpenAI, Anthropic, or emerging startups shipping autonomous workflow systems — this means the addressable market for enterprise agents is gated not by agent capability but by customer infrastructure maturity. A bank cannot deploy a fraud-detection agent until its transaction systems, customer records, and risk databases can all be queried by that agent in real time. That work happens upstream, in the modernization cycle Ezzat described.
The July announcement also coincided with Capgemini's earnings report and guidance lift, signaling that the firm expects this modernization wave to materialize as addressable revenue in 2026 and beyond.