Exploring AI Opportunities

Why exploring AI opportunities matters?

When AI enters a product, you need to frame decisions fast: what to automate, what to leave to the user, and what must stay under control. We study real usage, identify intents and edge cases, then prioritise the high-value AI moments. You leave with clear choices - what to build, in what order, with which safeguards - and inputs ready for design and specification.
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Nous vous aidons à miser sur les bonnes opportunités. En observant les usages et en cartographiant intentions et cas limites, la recherche révèle ce que les hypothèses ne voient pas. Vous repartez avec une vision priorisée des opportunités — celles à saisir avec l'IA, celles qui appellent une autre réponse. Quoi construire, dans quel ordre, avec quels garde-fous. Directement exploitable pour concevoir ou spécifier.

Focus on the right AI use cases

Research shows where AI truly saves time and reduces errors - and where it only adds noise.

Secure edge cases early

Most issues come from ambiguity, missing data, and outputs that look “right” at first glance. We map these early so you can prioritise what must be hardened.

Align on the expected level of quality

We align stakeholders on observable criteria: quality standards, error tolerance, and level of user control. Fewer back-and-forths, clearer trade-offs.

When should you focus on research & AI?

You’re preparing a copilot, chatbot, or generative feature and don’t know which use cases to prioritise.

Field feedback is vague: “it works sometimes”, “I’m not sure I can trust it”, “I don’t know what to check”.

You’re unsure about the right autonomy level: suggestion, assisted execution, or full automation.

Business, product, and data teams don’t share the same definition of value and risk.

You’re launching predictive scores and nobody knows how to interpret them - or what actions to take.

Intents and scenarios keep piling up without structure, making quality impossible to manage.

What we do in practice

AI usage research

Observing users in real contexts helps you understand their trade-offs: what they’re happy to delegate, what they refuse to delegate, and why. From there, we map the high-value AI moments with clarity.

AI UX risk analysis

Critical scenarios are clearly identified: where mistakes are costly, ambiguity is common, or explicit control is required. We deliver a prioritised list of edge cases to address in the experience.

Quality and verification criteria assessment

In your context, we define what a “good result” looks like: the expected level of accuracy, when sources are required, validation rules, and alert thresholds. We formalise these benchmarks to guide design and testing—so decisions are based on shared criteria, not opinion.

Decision-oriented user testing

On realistic tasks, we check that users understand what’s happening, know what to verify, and know what to do next. We deliberately include “hard” scenarios—when the AI hesitates, gets it wrong, or can’t answer.

How we work with you

Here are the main steps of the engagement. They adapt to your context, constraints, and the decisions to be made.

Together, we frame the AI question: your objectives, target users, critical tasks, the decisions to support, and what success looks like. The goal is simple: be crystal clear on what we need to learn - and what we need to be able to prove.

Next, field observation brings real usage back to the centre. We document intent, journeys, and trust signals, and we map the edge cases that can derail the experience.

From there, decision-oriented testing helps you make the call. Scenarios stay realistic, including moments when the AI hesitates, gets it wrong, or can’t answer - so uncertainty is reduced before you build.

Finally, the debrief turns findings into action. You leave with prioritised recommendations, risks to address, and inputs ready for design and specification - to speed up delivery in-house or with our AI Design team.