When organisations talk about AI transformation, the conversation often starts with tools, platforms, models, and architectures. But as the leaders from Capgemini remind us, the real transformation begins with people.
In a recent episode of Data & AI Mastery, Dr. Raoul-Gabriel Urma sat down with Claire Williams (VP for Analytics & AI), Arlene Carsley (Head of Workforce Transformation), and Mansukh Mann (Managing Consultant in Workforce Transformation) to unpack what it truly takes to make AI work at scale.
Their message is clear: Technology enables change, but people make it stick.
Here are the key lessons every data and AI leader can learn from Capgemini’s approach.
According to Claire Williams, the AI revolution is now impossible to ignore. The rise of large language models and the mainstreaming of tools like ChatGPT have moved AI from the fringes to the frontlines of business strategy.
But what sets successful organisations apart is where that urgency originates.
“In the best transformations, it’s driven from the board,” Claire explains. “We’re seeing leaders who are humble enough to acknowledge what they don’t yet know and curious enough to learn.”
Capgemini’s clients, she notes, increasingly view AI as essential to competitiveness. Whether it’s optimising pricing models, improving forecasts, or driving efficiencies across global supply chains, organisations that invest early and invest wisely will stay ahead.
Yet urgency alone isn’t enough. To deliver value, AI needs a foundation of culture, collaboration, and clear strategy.
As Mansukh Mann puts it, many companies have already invested in AI but in a fragmented way. “They’ve spent money on tools and pilots, but the return hasn’t matched the expectation. The conversation is now shifting from experimentation to value.”
That shift demands a new kind of transformation: one that connects technology and people under a unified purpose.
For Capgemini, this means aligning the C-suite around a common vision for data and AI and ensuring the strategy is co-owned by the business, not just the IT function. “It’s not a transformation you can do to people,” Claire adds. “It has to be done with them.”
That partnership between leadership, data teams, and the wider workforce is what turns a collection of projects into a cultural movement.
While technology moves fast, people need time to adapt. As Arlene Carsley explains, the biggest challenge isn’t learning the tools; it’s shifting mindsets.
“With only a surface-level understanding, there’s a limit to how much value organisations can derive from AI,” she says. “Long-term success requires deeper understanding, curiosity, and commitment to cultural change.”
AI transformation disrupts workflows and roles, which is why change management, transparency, and communication are non-negotiable. Arlene argues that every organisation needs a “common language” around AI: a shared fluency that empowers people to use it confidently, regardless of their technical background.
“When there’s understanding at every level,” she explains, “then you can start designing operating models and processes that actually work.”
Transformation doesn’t happen overnight, and success can’t be measured solely in dashboards or delivery milestones.
Claire advises organisations to focus on outcomes, not activities. “Everything you do should have business value attached to it,” she says. “Otherwise, three years later you’ll have a list of completed tasks, but no transformation.”
That means moving beyond adoption metrics to real impact: improved processes, better decisions, and tangible ROI.
Equally, organisations must celebrate small wins along the way. “Transformation takes time,” Claire notes. “But every step that moves you closer to a data-driven culture deserves recognition.”
A recurring theme throughout the conversation is trust, both in data and in people’s ability to use it.
For Mansukh, that starts with upskilling. Capgemini has invested in training over 350,000 employees globally in AI-related skills, ensuring the workforce grows alongside the technology. But she also highlights a softer skill: storytelling.
“It’s not just about building dashboards,” she explains. “It’s about being able to articulate their impact, to tell the story of how data and AI are creating value.”
That ability to connect the technical with the tangible is what turns analytics into influence.
It’s well documented that around 70% of digital transformations fail to deliver on their promises. For Claire, the reasons are familiar:
Success, she says, comes from aligning all three: strategy, measurement, and mindset. When data foundations are strong, governance is clear, and leadership is engaged, transformation stops being an abstract goal and becomes a lived reality.
As AI automates more tasks, the human side of work becomes even more valuable.
Arlene predicts a shift “from doing work to orchestrating work.” AI will handle the repetitive and routine, freeing people to focus on creativity, judgment, and relationships—the skills machines can’t replicate.
Every role, she argues, will need a level of data fluency, but not everyone needs to be a data scientist. What matters is confidence and the ability to understand, interpret, and apply AI insights meaningfully.
AI transformation isn’t a technology race; it’s a leadership challenge.
As Capgemini’s leaders show, success depends on vision, collaboration, and curiosity. It’s about equipping people to think differently, act boldly, and trust both the data and themselves.
At Cambridge Spark, we help organisations do just that by developing the technical and human capabilities needed to thrive in the age of AI. Learn more about how we can help your teams build AI fluency and confidence.