With AI accelerating from hype to full-scale enterprise transformation, leaders are stepping to the fore to guide the shift.

One such voice is Pascal Brier, Group Chief Innovation Officer at Capgemini. 

With decades of experience across technology and business leadership – including senior positions at Microsoft, AT&T, NCR and Altran – Pascal sits at the centre of Capgemini’s rapid evolution into an AI-powered transformation partner.

In his role, he oversees Technology, Innovation and Ventures for Capgemini across the globe – “tracking, analysing and implementing more than 1,000 emerging technologies each year”, ensuring the group “constantly strives to be at the forefront of innovation”. 

Building the AI-powered enterprise

Although AI is nothing new – both in terms of its 1950s foundations and when its popularity thrust it into the public domain in the 21st century – it is central to Capgemini and its ambitions.

This form of intelligence is also something deeply baked into Capgemini’s operations – something Pascal is continuing to ensure remains a core part of its foundation and strategy.

Now, the company positions itself as an AI-powered business and technology transformation partner, helping enterprises adopt, integrate and scale AI across industries from banking to automotive to telecommunications.

Capgemini boasts more than 30,000 data and AI consultants and engineers, all supported by industry-specific expertise and an extensive portfolio of delivery assets. 

“We are well positioned,” Pascal says, “thanks to our Resonance AI Framework, our ecosystem of 25 strategic AI partners and our innovation labs, including the AI Futures Lab and the AI Robotics & Experiences Lab.”

Underpinning these ventures is Capgemini’s conviction that the next wave of business transformation will depend on achieving what Pascal calls human-AI chemistry: the seamless collaboration between people and AI agents working under human control.

This chemistry extends beyond automation and instead enables new forms of creativity, foresight and strategic decision-making.

“In 2025 alone, we trained 310,000 team members in Gen AI and 194,000 in agentic AI,” Pascal continues.

“Our focus is on ensuring that every employee can leverage AI fluently, responsibly and effectively.”

The CXO dilemma of quiet adoption

Capgemini’s research into AI adoption at executive levels unveils a fascinating paradox.

While more than half of CXOs are already using AI to support or inform strategic decision-making, only a fraction are willing to say so publicly.

“Many are applying AI to research, analysis and content drafting,” Pascal explains.

“They expect to use it increasingly to augment and challenge their strategic thinking. Early adopters are already seeing gains in speed, cost efficiency, creativity and foresight.”

Yet despite these benefits, only 11% of executives plan to highlight the use of AI in their decision-making, mostly due to fears of reputational risk or potential backlash if AI-influenced decisions go wrong. 

“Just 41% of them report an above-average level of trust in AI for executive decision-making, with the main concerns being legal and security risks,” Pascal adds. 

What emerges, Pascal emphasises, is a nuanced picture of quiet transformation: leaders confident enough to use AI but cautious about admitting it.

Far from a lack of faith, he views this phase as a rational period of maturation. 

Who is leading in agentic AI?

Although the AI landscape is increasingly global, the pace of adoption is far from uniform across the world. 

Capgemini’s latest findings show 46% of Chinese organisations piloting or deploying agentic AI – systems capable of acting autonomously on behalf of humans – compared with 41% in the US and just 30% in Europe.

Pascal attributes China’s lead to a combination of experimentation culture, vast data ecosystems and an appetite for agile governance. 

“For this report, we surveyed a China-based digital commerce company that launched three AI agents to streamline cross-border e-commerce tasks, improving efficiency and productivity,” he recalls.

“For them, the past year marked a significant shift in how they deployed AI, reflecting broader industry needs and trends.” 

These agents are not replacements for people, however.

From where Pascal sits, he sees them as partners in complexity: “They’re evolving beyond standalone generative models toward dedicated AI agents that can tackle a wider range of complex business challenges efficiently for merchants engaged in cross-border trade worldwide.”

Human and governance challenges

Despite the technological leaps of recent years, Pascal is unequivocal about where the real bottleneck lies: people and governance. 

“Today, the barrier to unlocking AI value is not the technology itself,” he says.

“Organisations have already moved beyond experimentation with 38% operationalising Gen AI and six in 10 exploring agentic AI applications.”

The real challenge, Pascal stresses, lies in the human and governance foundations needed to scale responsibly. 

“Our research shows that leaders must be more deliberate about governance, skills, accountability and human-AI chemistry to realise AI’s long-term value,” he notes. 

According to Capgemini’s data, only 48% of organisations have clearly-defined roles and responsibilities for humans and AI systems to work together effectively.

“Two-thirds of CXOs say clearer governance and accountability frameworks would help them better leverage AI in decision-making,” Pascal goes on.

“While technology is ready, enterprise readiness, trust and the ability to embed AI into decision-making and operating models remain the determining factors.”

In Capgemini’s view, responsible AI adoption comes down to balance: automation with human insight, speed with control and ambition with governance.

Is the AI bubble bursting or just beginning?

Talk of an AI bubble has bounced around the industry for years, but Pascal’s perspective is refreshingly grounded. 

“Our latest research shows that after an era of ‘AI hype’, business leaders are now increasingly realistic and pragmatic about their AI strategies and have started using it in their decision-making,” he insists. 

“We have now entered a new era of AI-driven transformation, focused on longer-term, enterprise-wide implementations to improve not just productivity but revenue, customer experience, risk management, innovation or decision-making.

“AI has now crossed a critical threshold: the question is no longer whether to pursue AI, but how to embed it into the fabric of the enterprise. 

“Many organisations are rightly prioritising strong AI foundations – data, governance and human-AI chemistry – but one other area stands out as a critical factor in successful AI deployments and it is leadership readiness. How leaders set a clear vision for its use across the enterprise and take responsibility for it will be key to effectively harnessing its transformative power.”

The decision CEOs will regret most

Asked what decision CEOs might regret most in five years’ time, Pascal does not hesitate. 

“As AI moves from experimentation to enterprise-scale deployment, organisations face a new challenge: translating rapid advances in AI capabilities into sustained business impact,” he concludes. 

“This shift requires leaders to rethink how intelligence is embedded across the organisation, from core operations and decision-making to governance, talent and technology foundations. Success depends not only on deploying AI, but on building the capabilities, guardrails and ways of working that allow AI to scale responsibly and deliver value over time.

“The AI decision CEOs will regret most in five years is choosing near-term priorities over long-term vision and delaying the foundations required for scalable adoption.”