Generative AI Matures, Businesses Mobilize: The Race for Collaborative Advantage
Generative AI (Gen AI) is a branch of artificial intelligence that uses advanced models to create new and unique content, such as images, code, or text, based on a user’s request. It is moving from experimentation to mainstream adoption, with enterprises rapidly scaling deployments across industries. According to a new Capgemini report, “Harnessing the value of AI: Unlocking scalable advantage,” nearly six in ten organizations expect AI to act as an active team member or even as a supervisor for other AI systems within the next year.
For eeNews Europe readers, this signals an inflection point in enterprise AI maturity. The expectation highlights both the opportunities and technical challenges of scaling AI systems responsibly.
Generative AI scaling outpaces readiness
Capgemini’s research reveals that enterprise generative AI adoption has increased fivefold in two years, with 30% of organizations (up from 6% in 2023) now scaling Gen AI initiatives. Around 93% are exploring or deploying Gen AI capabilities in 2025. Leading industries include telecommunications, consumer products, and aerospace and defense, with applications spanning marketing, customer service, risk management, and IT operations.
Despite this acceleration, readiness remains a concern. According to the report, two-thirds of organizations say they must restructure teams to support human-AI collaboration effectively. The report also warns that enthusiasm for AI often outpaces governance and strategy.
Investments, agents, and governance challenges
The report shows strong financial commitment to Gen AI, with 88% of organizations increasing their AI budgets by an average of 9% over the past year. Currently, 12% of IT budgets are devoted to Gen AI, and 61% of companies plan further increases in the coming year. However, scaling comes with unanticipated costs: more than half of enterprises experienced unexpected cloud spending spikes, or so-called “bill shocks.” In response, many are turning to small language models (SLMs) to reduce operating costs.
Meanwhile, AI agents, which are autonomous systems that perform specific tasks, are becoming increasingly common. About 90% of executives expect AI agents to handle one or more business processes within three to five years, and nearly half of those scaling AI agents are experimenting with multi-agent systems, the report indicates. Yet, trust remains an obstacle: 71% of organizations say they cannot fully trust autonomous AI systems for enterprise use, and fewer than half have established governance policies.
For engineers and technologists, Capgemini’s findings underscore the dual imperative of innovation and control. As AI transitions from pilot to production, the next challenge isn’t building smarter systems, it’s building trusted ones.
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