AI should not be pursued out of enthusiasm for technology alone. It becomes valuable when it clarifies a decision, removes friction, speeds up repetitive work or improves an identifiable client experience.
Start with real work, not the tool
The first instinct is often to find an AI solution before naming the problem. That makes projects vague. A good use case starts with something concrete: a team wasting time, unused data, client replies depending on one person, or a check still performed manually.
Before discussing models, assistants or automation, observe everyday work: repetition, delays, decisions made without reliable information and unnecessary mental load.
Distinguish simple automation from strategic AI
Not every opportunity needs AI. Some need a better-connected workflow, a cleaner form or a properly configured CRM. AI makes sense when work involves classifying, summarising, interpreting, recommending or supporting a decision.
This distinction avoids premature investment and creates a more credible roadmap: automate what can be automated, then add AI where it provides a genuinely new capability.
Assess impact before complexity
Judge an AI use case on potential value, operational feasibility and risk. A well-scoped, low-risk project with visible time savings can create more value than an ambitious initiative poorly integrated into operations.
The best first projects are often modest: qualifying enquiries, summarising meeting notes, drafting, analysing client feedback, detecting anomalies, preparing dashboards or supporting internal searches.
Prioritise with your teams
AI cannot last if it works against daily practices. The teams using, checking and improving it need to understand it. End users should therefore be involved from the start.
Good prioritisation considers more than technical interest. It identifies the most costly frustrations, available data, adoption risks and indicators for measuring outcomes.
Put it into practice
Compare three ideas before choosing a pilot
This grid does not calculate a universal score. It makes the questions visible so ideas can be compared with the people involved. A promising case still needs testing with authorised data and defined criteria.
| Starting point | What to examine | Decision or check |
|---|---|---|
| Summarising notes | Data: approved notes, an output template and business vocabulary. Check decisions, dates and actions rather than fluency alone. | A pilot may fit if sources remain accessible and someone reviews every summary before sharing. |
| Document search | Data: a bounded collection, dated documents and known permissions. Include questions with missing or conflicting answers. | Defer the project if sources are unreliable or permissions cannot be respected. |
| Assigning a request | Data: categories, rules and exceptions. If a fixed condition can select the recipient, start with conventional automation. | Consider AI only if interpreting content is necessary and a review queue can handle uncertain cases. |
For each idea, document current work, the proposed measurement and risks. Rejecting or delaying a case is useful when data, controls or ownership are not ready.
Further reading on the principles: CNIL ↗
Key takeaways
Investing in AI rarely starts with a technology decision. It starts with an honest view of the organisation: where work stalls, data lies unused, tasks repeat and decisions lack support. That is where AI can become useful.
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