How continuous reflection can help turn AI adoption into new ways of leading and working?
Many organisations have already deployed AI, yet the business impact often remains limited. The challenge is increasingly less about access to technology and more about what happens in everyday work: how people make decisions, handle uncertainty, collaborate and change established habits.
At a recent Gofore event, Dr. Olivier Malafronte shared insights from his research on AI coaching and leadership development. His core message was simple: organisational transformation succeeds only when new knowledge becomes new behaviour. That “last mile” is human. Gofore’s AI advisor and coach Terhi Aho sees the same gap in her work with leaders: knowing and doing are two different things. Deloitte’s trend report shows that 66% of leaders say human-AI interaction design matters, but only 6% are making real progress on it.
Reflection is what closes that gap: McKinsey’s 2026 survey of over 10,000 executives found that reflective leaders are far more likely to champion AI adoption (61% vs. 43%) and to believe their organization can adapt quickly (30% vs. non-reflective peers). Reflection turns exposure to AI into changed behaviour — and changed behaviour into real transformation.
The last mile is human
The diagnosis is clear and rigorous: Transformation doesn’t fail at the strategy or technology level — it fails at the last mile, where knowledge becomes behavior, where understanding becomes action, where commitment becomes a new way of working.
This is what researchers call the knowing–doing gap. Organizations know what matters. But they cannot translate that knowledge into everyday thinking and behavior.
Every leader operates through their own mental patterns, habits of thinking and reacting that shape how they read a situation, handle emotion, make decisions, and deal with other people. These patterns are invisible. You won’t find them in a slide deck. But they’re what actually drives behavior. And until they shift, behavior doesn’t shift either. No matter how many tools or workshops you throw at it. —This isn’t a criticism of organizations. It’s just how people work.
This is why reflection matters. It helps people notice assumptions, emotional reactions and habitual responses before choosing how to act. In leadership, that can mean reframing a difficult situation, considering another person’s perspective or preparing for a challenging conversation with greater clarity.
Use AI as a thinking partner
AI coaching can make structured reflection available more continuously. The important distinction is how AI is used. Used only for quick answers, AI improves efficiency without developing the person. Used as a reflective partner, it asks questions and surfaces patterns instead of solving the problem outright.
Malafronte’s research describes this role as a “reflective mirror”. Short AI coaching conversations of 15–30 minutes were associated with changes in how leaders reframed situations, regulated emotions and considered other perspectives. It doesn’t replace human coaching. It extends reflective practice into moments when a coach isn’t there.
Build development into real work
Leadership development is usually episodic, a workshop, a session, an annual review, but behavior is tested in between. AI coaching can support small and repeated reflection in the flow of work, for example before a difficult discussion, after an important meeting or during a weekly review of progress as Malafronte’s reseach on AI Coaching and leaderhips shows.
This is what scaling transformation actually means. Not deploying more tools. Not running more training. But creating a system where leaders are continuously supported to pause, reflect, learn, and act differently — in the real situations that constitute their daily work.
This also connects with Gofore’s AI Beyond Tomorrow transition model. To model emphasizes that AI adoption needs to be built as a broader organizational transition, not as isolated tool deployment, but as a structured shift in capabilities, roles, leadership and everyday practices.
Rethinking learning, from the ground up
In the event, Aho put it simply: the organizations that win aren’t the ones with the best AI — they’re the ones that learn faster than everyone else. Aho’s own experience working with leaders through change — later distilled into her book on experimentation culture — points to a clear pattern: the organizations that learn fastest aren’t running the biggest transformation programs. They’re running many small, low-threshold experiments, learning quickly, and adjusting before scaling. The same applies to reflection — it works best small and frequent, fitted into real work, not saved for the next offsite.
Three shifts matter here:
1. From training to developmental infrastructure — not which module to run, but what system sustains ongoing learning and reflection.
2. From tool deployment to methodology design — not which AI platform to adopt, but what developmental thinking is built into it, and who designed it.
3. From individual coaching to systemic reflection — not who gets a coach, but how reflection becomes accessible at scale, embedded in everyday work.
AI coaching, done well, makes all three possible — not because the technology is magic, but because it brings real developmental methodology to people who never had access to it, continuously and consistently.
Keep the human at the centre
The goal of AI coaching is not more AI. It is better human capability: clearer decisions, stronger collaboration and greater ability to adapt. Technology can help scale the conditions for learning, but people still do the learning and changing.
That is a useful principle for AI-enabled transformation more broadly. Successful adoption is not measured only by how widely a tool is deployed. It is measured by whether people can use it to think, decide and work better in the situations that matter.
Ready to turn AI opportunities into real organisational change?
Sources:
Olivier Malafronte’s articles in Management & Avenir (2024) and the Journal of Applied Behavioral Science (2026).
Deloitte. (2026). 2026 Global Human Capital Trends. Deloitte Insights.
McKinsey & Company. (2026). The State of Organizations 2026.
