If you've watched executive boardrooms pour billions into artificial intelligence over recent years, you've probably noticed a frustrating operational bottleneck. Capgemini Chief Executive Aiman Ezzat recently declared that global enterprises face according to insiders, a massive hurdle: decades of accumulated technical debt and deeply fragmented data. Following a strong surge in quarterly bookings, the French IT consulting giant raised its 2026 revenue growth target. Ezzat highlighted that most companies aren't ready to run autonomous, multi-step agentic systems because their underlying software architecture remains hopelessly outdated.
To bridge this operational gap, businesses are kicking off a multi-year IT modernisation supercycle. Rather than throwing money at standalone experiments, executives are now rewiring legacy platforms, consolidating isolated data streams and updating core cloud architecture. Capgemini's latest earnings call signals a decisive industry shift away from flashy pilot projects toward foundational technology upgrades. You'll see organizations prioritize hard structural fixes over quick software gimmicks as they scramble to build dependable foundations for enterprise-scale artificial intelligence over the coming years. And fast.
The Reality Check Behind Enterprise AI Adoption
Strip away the polished chrome exteriors of most of the world's Fortune 500 companies, and you won't see sophisticated, cohesive tech architectures; you'll see a mess. Decades of stale COBOL code, creaking mainframes and fragmented databases support every single key business unit. When Capgemini's chief executive, Aiman Ezzat, was asked about enterprise adoption post-earnings, he frankly stated that whereas anyone could access powerful foundational language models right now, the main bottleneck to the deployment of AI would have remained within outdated business infrastructure, something that most failed to realize.
It's easy to believe that simply purchasing software licenses to the hottest technology will automatically solve the core bottleneck in your business operations; it won't. When core business numbers are stranded in siloed server stacks spread across six departments, any algorithm will fail. Ezzat commented on boards' keenness for agentic software-which is essentially a program capable of completing complex, multi-step business processes autonomously-and how difficult it is to achieve, as most companies encounter technical architectural barriers that modern enterprise software can't handle. This blocks data flow instantly and at every level, halting progress before measurable value can be achieved.
Unpacking the Legacy Obstacle
Quick band-aids and incomplete software migrations over thirty years have left corporate IT estates brittle. You can ask an intelligent agent to review client churn across regional branches. But if transaction history sits inside a 20-year-old mainframe while inventory logs live inside an unintegrated cloud app, the system chokes. Ezzat made it clear that these tools don't fail because they lack raw processing power. They fail because underlying database setups fail to supply accurate, structured context when queried. Hardly.
Fixing a corporate stack isn't as simple as slapping a fresh user interface over a dusty SQL server. It means ripping out archaic permissions, rewriting core data pipelines from the ground up and building reliable API networks across every arm of the business.
TRADITIONAL ENTERPRISE LEGACY STACK
[Mainframe Data] —> Siloed--> [Cloud Apps] —> Siloed --> [ERP DBs]
(Fails Agentic Tasks)
CAPGEMINI MODERNISATION MODEL
Unified Data Pipeline -> Structured Cloud Core -> Agentic AI Engine
Why Legacy Technical Debt Kills Agentic Workflows
Autonomous systems require flawless data hygiene. If you want software to act on its own, your underlying files, records and databases must be clean, structured, and instantly reachable. Sadly, that's almost never the case in major corporate offices today. Decades of hasty corporate buyouts, quick programming hacks, and half-baked updates left most brands buried under terrifying amounts of technical debt. You've likely seen consumer-facing generative AI applications write crisp paragraphs in seconds. Yet, those same tools falter when forced to update a complex enterprise resource planning tool or re-route a supply chain shipment.
"Every organization today wants to become agentic," Ezzat stressed during the investor briefing. "But before they can become agentic, they must become AI-ready — and most aren't." When underlying corporate tools can't talk to each other, smart software starts hallucinating, drops tasks mid-process, or spits out broken figures. To fix these painful glitches and actually turn generative AI enterprise spending into business margin, executive teams have no choice left. They've got to overhaul their foundation. Here’s the kicker.
The Agentic Failure Rate
- Autonomous software demands real-time, two-way communication across every corporate department.
- Ancient software setups lack standardized, open application programming interfaces (APIs).
- Broken database architectures breed conflicting records, causing automated tools to freeze.
- Unorganized document folders prevent models from verifying facts against internal truth sources.
Capgemini Raises Revenue Targets as Modernisation Boom Begins
And that brutal reality has unleashed torrents of capital spending across world IT consulting markets. Shares of Capgemini edged up after raising its long-term 2026 sales forecast, at least for the time being - fueled by strong year-end contract awards and an acceleration of enterprises buying into their services. The message is that CEOs and business managers are not curtailing their investments in technology - they’re just doing it differently. They aren’t stuffing individual pilot projects, goofy internally-developed chatbot attempts or disconnected, stand-alone proofs of concept with dough and then trying to stitch something together around it - they are directly writing massive capital checks for big re-architecting efforts and large-scale moves toward new cloud foundations and their data infrastructure.
The spending isn't just about writing code anymore, you get that all the way through to the underlying AI hardware supply chain; it's matching that step up with heavy backend modernization endeavors, as major corporations rush to patch their tech underbellies and the consulting firms round up multi-year deals to refit old server farm. It’s become obvious for corporate leaders you can’t deliver on that automated grid or those reliable on-site hardware AIs without cleaning the house code base first.. Not really.
Shifting Strategy: From Toy Pilots to Enterprise Transformation
The tone in executive boardrooms has shifted dramatically over the last eighteen months. The honeymoon phase with standalone models is officially over. Today, directors demand real, trackable returns on their capital investments. That means connecting smart engines directly to core operational tools. Ezzat observed that client spending patterns are far more disciplined today than they were two years ago. C-suite teams are pulling budget away from flashy visual tools and pouring those funds into core backend readiness. Case in point.
And this makes even more sense for physical server rooms and power infrastructure. Serious algorithmic computation needs proper processing capability, distributed server capabilities, hybrid clouds, and reliable grids, all prompting big betting odds in digital and sustainable infrastructure. Your commitment to deploying a new organizational-wide AI strategy means your company not only subscribes to an AI service. You’re also ready to transform your entire underlying infrastructure data from front to center while staying compliance without any risk.
Rerouting Enterprise priorities
Old Strategy (2023-2024) --> New Strategy (2025-2026+)
- LLM Chatbot pilot projects --> Full fledged Agentic workflows across the system
- Decentralized Data siloes --> Unified lake house platforms
- Surface level ui wrapping --> Complete re-architecture of existing legacy code
- Experiment / try & test phase --> Full product / service reimagination & cost optimization
What the Multi-Year Supercycle Means for Your Tech Roadmap
This is the clear technology roadmaps you seek for your next three to five years in terms of the big ideas in global business tech. There are a series of these at Capgemini. Big capability automation using complex software just can not happen over a weekend.
Heavy, intense software engineering needs to occur.
Putting intelligent-acting agents above two-decades-old brittle software is a cause for the operational hell many fear. It's where global companies must be in AI if their intent is large-scale adoption. Data hygiene, structural debt needs to be dealt with immediately as it relates to the implementation of large-scale enterprise AI in our companies. This involves both establishing enterprise architectures for AI deployment, as well as establishing rigorous access controls and safeguarding critical data at the enterprise level – and that requires security technologies, specifically the ones designed to ensure internal data doesn't migrate elsewhere as we plan this massive rewrite of our tech stacks.
This upgrade cycle will neither be inexpensive nor will it occur on a tight deadline.
Nonetheless, it's the price of entry if global enterprises seek to endure and prosper in increasingly autonomous economies.