More intelligence.
A bigger leadership opportunity.
AI has made knowledge more accessible. The real opportunity is to rethink how leaders lead, how people grow and how the enterprise works together.
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Speaking at the 2026 INSEAD MBA Alumni anniversary event in Amsterdam, I explored what AI could mean for leadership. My view is optimistic: this could be an age of enlightenment for leaders.
As functional knowledge becomes more accessible, the opportunity is to raise our expectations of leadership, and of the way organisations build and apply capability.
Use the time dividend.
Raise the quality of leadership.
Leaders have often earned authority through what they know. AI challenges that model. Access to functional knowledge is widening; the value of leadership increasingly lies in what leaders do with it.
Framing the problem. Asking the difficult question. Bringing organisational context. Making a decision when the answer is uncomfortable. Telling a story that gives people direction and confidence.
The evidence demonstrates potential efficiency gains. How leaders reinvest that capacity is a choice. I would use it to deepen judgement, strengthen relationships and make space for the conversations that move an organisation forward.
An age of enlightenment for leadership starts with better questions.
Discernment becomes more valuable too: in the same study, AI users were 19 percentage points less likely to reach the correct answer on a task beyond the technology’s capabilities. [1]
Build an AI community.
Multiply capability.
HR has an opportunity to create an environment in which employees and leaders can develop and apply capability for themselves. A community built around shared experimentation, trusted tools and learning from each other can extend the reach of the People function.
Consider AI-enabled conversation practice: an employee rehearses a difficult discussion, receives contextual feedback and explores the learning with their line leader. Development becomes repeatable, personal and closer to the work.
The support-agent study offers evidence that AI can spread effective practices and accelerate learning. It does not establish that AI coaching replaces learning teams or that manager-led learning is always superior. Those are separate questions to test.
Move learning closer to the moment it creates value.
Learning specialists can shape the community, curate technology, establish quality standards and evaluate impact. Line leaders can make practice meaningful through context, feedback and application. The commercial test is whether capability improves at scale, rather than whether platform usage increases.
Open up expertise.
Work as one enterprise.
Too many organisations still approach business challenges from the comfort of their functional silos. AI creates an opportunity to make knowledge travel further and bring more perspectives into the work.
In the P&G experiment, individuals using AI matched the performance of teams without it. AI-assisted proposals also combined technical and commercial perspectives more evenly, reducing the tendency to stay within a participant’s functional background. [3]
My interpretation is that leaders can set a higher ambition for multidisciplinary working: organise around an enterprise problem, make expertise accessible and expect people to be curious beyond their remit.
Functional excellence should travel across the enterprise.
Specialists remain essential for depth, validation and accountability. Their contribution can become more influential as their knowledge reaches further. The opportunity is to combine specialist judgement with a wider field of ideas.
Find the AI that makes a difference.
Build the conditions for it to work.
Forthworks brings an outside-in perspective to identifying and assessing differentiating AI technology across talent, leadership and learning. Starting with the business challenge, we examine market options, test practical use cases and define how value will be measured.
For Boards, CEOs and Chief People Officers, the question is bigger than which technology to buy: what should people be able to do differently, and how will that improve performance?
Discuss your prioritiesEvidence & sources
Research findings support the opportunities explored here. The leadership and organisational recommendations are Stephen’s interpretation. Results from specific tasks and organisations are not forecasts of returns elsewhere.
- Dell’Acqua et al.: Navigating the Jagged Technological Frontier. Organization Science. Experiment with BCG consultants; benefits depended on the task and AI’s capabilities.
- Brynjolfsson, Li & Raymond: Generative AI at Work. Quarterly Journal of Economics. Customer-support setting; productivity measured as issues resolved per hour. Effects differed by experience and skill.
- Dell’Acqua et al.: The Cybernetic Teammate. Organization Science. P&G product-innovation experiment; findings concern this setting and task, rather than organisation-wide outcomes.