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Artificial Intelligence
The real AI challenge: Empowering people, not just platforms
For CEOs, the hardest part of AI transformation isn’t the technology – it’s reskilling at scale to ensure teams can apply what they’ve learnt in practice.
The AI imperative: reskilling at scale
AI is redefining the scope of business performance. No longer an experimental add-on, AI is capable of rapidly implementing operational efficiencies and enhancements such as hyper-personalization. CEOs know this. What remains unclear is how to harness the full potential of AI.
Here lies the paradox of AI implementation in a training context. Many organizations invest in digital learning platforms – but employees still struggle to apply new skills on the job. This isn’t a training failure; it’s a structural one. Just as the steam engine took decades to reshape factory productivity, AI’s transformative potential requires more than technical readiness – it demands workforce readiness.
The future belongs to organizations that ignite a sense of urgency around AI – while building the practical infrastructure to support experimentation and application.
In today’s AI-powered environment, technical ability alone won’t deliver transformation. The new advantage lies in problem framing, judgment, and the ability to work symbiotically with machines – skills that emerge not through passive instruction, but through embedded, real-world experience.
Companies that fall behind in terms of skills will fall behind in terms of performance. They also risk irrelevance if their workforce isn’t adapting to AI-enhanced work. To unlock that value, companies must rethink how people learn, practice, and adapt. Those that reskill successfully will capture the speed and scale advantages that companies like Nvidia now enjoy.
Without clear pathways to apply what they’ve learned in real situations, the training ‘rush’ fades fast.
When learning falls flat
Companies are pouring resources into upskilling and reskilling, but many still struggle to turn training into tangible change. Employees often emerge from training sessions energized – brimming with ideas and eager to put new skills to work. But back at their desks, that momentum fizzles out. Without clear pathways to apply what they’ve learned in real situations, the training ‘rush’ fades fast. The result? A frustrating gap between potential and progress.
Reskilling isn’t about ticking off e-learning modules or collecting certifications. It’s about enabling people to drive change, whether by identifying opportunities to use AI or experimenting with solutions in their day-to-day roles. The jobs of the future will reward not rote execution, but critical thinking and iterative problem-solving in partnership with machines. The real transformation comes not from competing with AI, but from learning to thrive alongside it.
Why do so many companies fall short? Because the barriers are cultural and structural. Hierarchies remain rigid. Siloed departments stifle experimentation. Leadership hesitates to decentralize decision-making. And in the absence of psychological safety, few employees will take the initiative to experiment with AI, regardless of training.
The chart below illustrates a simple truth: reskilling only succeeds when infrastructure and motivation align. Pouring resources into training without empowering people to act leads to frustration. Cultivating urgency without support leads to chaos. True transformation comes when employees are both equipped and energized to apply what they’ve learned.
What drives successful reskilling
A modular design for flexibility
In the context of AI-driven transformation, modular design offers “innovation without the chaos.” Take the case of Mastercard, which shows how modular systems can drive digital innovation in the global payments industry. At its heart,”modularity” means breaking down complex, monolithic systems into modules, which enables faster modification and more efficient scaling.
Few areas of financial services have seen as much technological disruption as payments. The rise of digital and mobile tools has triggered the most fundamental transformation since the introduction of plastic cards half a century ago. Once dominated by a few banking behemoths, payments can now be made using a range of devices and across multiple channels.
Rather than relying on its reputation and affluent customer base, Mastercard responded by repositioning itself as a technology partner with a central commitment to modularity. This approach reshaped both Mastercard’s internal systems and the way it engaged with partners.
That openness extended to the FinTech subsector. Mastercard has worked hard to position itself as a preferred collaborator for new entrants by sharing tools, offering access, and encouraging co-creation. Developers could plug into its network via application programming interfaces (APIs), underlining the strengths of a platform-based model. This model feeds a cycle of innovation that delivers new services to consumers and businesses alike.
By diligently measuring AI’s real-world impact on cycle times, output quality, or customer satisfaction, leaders can ensure they stay ahead of the game.
From adoption to absorption
All companies can access frontier tech. But not all can turn it into productivity. Seminal analysis by the OECD found a growing gap between top-performing firms and the rest even when access to frontier technologies is broadly the same.
The difference? Leading firms don’t just adopt tech – they absorb it. They have “complementary capabilities”: advanced skills and workforce training, strong management and leadership, modern organizational practices, and access to finance, scale, and networks. These aren’t nice-to-haves; they’re what make the tech work.
Laggard firms often miss those pieces. They may have the same access to technology, but without the capacity to reshape workflows or upskill teams, the benefits stall. Same tech, different outcomes.
Another trap is the absence of robust metrics. By diligently measuring AI’s real-world impact on cycle times, output quality, or customer satisfaction, leaders can ensure they stay ahead of the game. This means benchmarking AI-enabled workflows against traditional processes. Are customer service agents responding faster? Are supply chain forecasts more accurate? Is AI-enhanced personalization driving higher conversion rates?
At global learning company Pearson, CFO Sally Johnson isn’t just talking about AI experiments, she’s also seeing real results. GenAI tools built into the company’s higher education courseware are driving performance where it counts. “For products using those AI study tools, we’ve seen double-digit revenue growth,” says Johnson. “So, it’s making a difference from a commercial point of view, as well.”
A framework that CEOs can act on
To scale from isolated pilots to transformation, CEOs must provide an organizational environment that welcomes AI:
Step 1: Invest in hands-on re-skilling programs
Programs must go beyond passive learning. Employees should work on real projects that integrate AI tools, whether experimenting with predictive analytics or GenAI in content creation. The emphasis must be on “learning by doing” and business relevance.
Step 2: Create “sandboxes” in which employees can test AI innovations safely
Provide safe environments in which teams can test AI use cases without performance pressure. These sandboxes encourage experimentation and foster a culture of curiosity. Crucially, businesses must shield these sandboxes from bureaucratic drag to avoid smothering innovation.
A case in point is Coca-Cola’s approach to GenAI. As Pratik Thakar, their Head of Generative AI, explains: “Gen AI is already distributed, it’s democratized. Everyone has access to it, and people can come up with ideas. We have created an internal mechanism where everyone who wants to use gen AI fills out a very simple form and our small team looks at it and maybe we say, ‘That’s a perfect use of AI – maybe just not that particular tool.'”
Step 3: Establish metrics to track AI’s internal and external impacts
Track how AI affects both internal efficiencies and customer-facing outcomes. Measuring impact helps maintain focus, justify investments, and reinforce a results-oriented mindset.
In a world racing to harness intelligent machines, the differentiator won’t be access to technology; rather, it will be human adaptability.
The flywheel effect
Effective reskilling creates a virtuous cycle; the “flywheel effect.” Learning feeds application, which leads to the results that motivate endeavor and accelerate investment.
Organizations that fail to act decisively will soon find themselves at a disadvantage in terms of talent retention, while struggling to maintain market relevance. As AI evolves, so does the competition.
In a world racing to harness intelligent machines, the differentiator won’t be access to technology; rather, it will be human adaptability. CEOs who reskill their people and empower them to act will set a pace that others will struggle to match.
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