Winning organizations won't predict the future. They will stay curious enough to learn faster.

Anne-Laure Le Cunff's UNLEASH 2026 in Paris keynote will share an uncomfortable truth.

The KCL neuroscientist will argue that the organizations that lose in an AI-driven world won't be the ones with worse technology, but the ones that mistook optimization for good decision-making.

Ahead of her keynote, she sits down with UNLEASH’s Chief Reporter Allie Nawrat to discuss why successful HR leaders trade certainty for experimentation — and what that looks like in practice.

Allie Nawrat: Why is over-engineering for optimization a critical business challenge right now, and what are the signs that companies have fallen into that trap?

Anne-Laure Le Cunff: Optimization works when the environment is stable, and the desired outcome is already known.

Many organizations are now optimizing processes built for a world, I would say, that no longer exists. They’ve become very efficient at executing yesterday’s assumptions [and] in the process are losing the ability to question them.

I’d say some common signs are endless dashboards, having rigid annual plans, slow approvals, meeting overload and teams that are afraid to act without having complete data.

The risk here is not necessarily inefficiency or lack of productivity, it is becoming extremely efficient at doing the wrong thing.The risk here is not necessarily inefficiency or lack of productivity, it is becoming extremely efficient at doing the wrong thing.

Organizations in today’s world need to complement operational excellence with the capacity to notice change and to adapt and learn quickly.

AN: As you said, business processes have been built for a stable world that no longer exists – it now feels like uncertainty is the new normal. In this context, why do organizations need to shift from control and certainty to curiosity when making decisions?

ALC: Control creates comfort, but it can also create the illusion that uncertainty can be eliminated.

Curiosity offers a more productive response. Instead of asking ‘How do you guarantee success?’ ask ‘What can we learn next?’

This process of shifting from control and certainty to curiosity turns uncertainty from a threat into a source of useful information. Curiosity doesn’t mean indecision; it means acting without pretending that we already know the answer.

The idea is to replace large, irreversible bets with tiny experiments that generate evidence. The aim is to learn, to adapt, to iterate. We’re still progressing, [but] we’re not pretending that we have the answers.

AN: In a recent New York Times OpEd you argue that curiosity is a biological state — it opens a window between a question and an answer. How is AI threatening that window, and what does it mean for enterprises?

ALC: A window is a really good way to think about it. Curiosity lives in the gap between encountering a question and receiving an answer – this gap creates productive tension; we reflect, we search, we make connection, we generate our own hypotheses.

AI can close that gap almost instantly by giving us a polished answer before we have had time to think.

The danger is not simply that an answer may be wrong – AI models have actually become really good at giving us decent answers. The danger is that we outsource the cognitive process that helps us learn.

At an enterprise level, this could produce faster output but weaker judgement [and] more homogenous ideas because an AI model is going to predict the most likely answer.

Organizations should use AI to expand inquiry, not to end it as quickly as possible. This means using AI to ask for an alternative hypothesis, blind spots, possible experiments, rather than asking for a final answer.

AN: At UNLEASH 2026 in Paris, you’ll be speaking to a room of HR leaders. If you had to give them one action to take now, based on everything we've discussed about curiosity and decision-making, what would it be?

ALC: HR can lead by rewarding curiosity and experimentation, not just predictable outcomes.

Create protected space for running experiments. Ask every team to identify one assumption about how work should be done and test an alternative for a limited period.

You need to define the question, the experiment, and what the team hopes to learn, but, most importantly, do not require the experiment to ‘succeed’. Then you hold a short review focused on learnings: What surprised us? What changed? What should we try next?

This one action would make curiosity an operating practice, rather than a value written on a wall, which is very common in companies.

AN: A year from now, what will separate the organizations that thrive from the ones that don't?

ALC: Their speed of learning, not just their speed of execution. Thriving organizations will combine AI-enabled efficiency, which is great, with human judgement, curiosity and imagination. They will be able to change direction and iterate without treating every change as a failure.

Organizations who are leaders will be comfortable saying ‘I don’t know yet’ and who will also know how to turn that uncertainty into an experiment – their employees feel safe questioning assumptions and sharing unexpected results.

Thriving organizations will not be the ones that try to predict the future perfectly. They’ll be the ones that build the capacity to keep on experimenting, keep on learning, keep on iterating as the future unfolds.