Start from pain points, not technology
Successful AI projects do not start with choosing a model, but with a simple question: what wastes your teams’ time every week? Here are five use cases we see working from the first weeks, in organisations of all sizes.
1. Finding information in internal documentation
Procedures, contracts, notes, reference documents: the information exists, but nobody knows where. An assistant connected to your document spaces answers while citing its sources, and respects everyone’s access rights.
2. Writing meeting minutes
Transcription, structured summary, list of decisions and actions: a thankless task that often takes longer than the meeting itself, and is perfectly suited to AI.
3. Answering recurring HR questions
Leave, remote work, health cover, expenses: a dedicated assistant, fed with your agreements and internal rules, frees HR teams from repetitive questions.
4. Analysing spreadsheets without macros
Cross-referencing two files, spotting discrepancies, producing a chart: an agent able to work with Excel makes these analyses accessible to people who are not experts.
5. Preparing tender responses
Reusing past references, adapting technical proposals, checking compliance with the specifications: AI speeds up preparation without replacing the expertise of those who respond.
What these five cases have in common
These use cases share three characteristics that explain their success. They are frequent: the gain repeats every week. They are verifiable: an employee immediately judges whether the result is right. And they rely on your own documents: that is where an AI connected to your document base makes the difference compared with a general-purpose tool.
That is also what makes them sensitive. Internal procedures, management minutes, HR data, commercial offers: these are precisely the kinds of information a company does not want passing through external services. AI deployed on your infrastructure lets you launch these use cases without trading efficiency for confidentiality.
Mistakes to avoid
- Trying to cover everything at once: ten use cases launched together are ten use cases poorly followed up.
- Forgetting source quality: an AI answering from outdated documents gives outdated answers. A little document tidying often comes before deployment.
- Neglecting buy-in: involve future users from the start; they are the ones who will identify the best uses.
- Not setting a success criterion: without measurement, it is impossible to decide what comes next.
How to go about it
- Pick two or three cases with high volume and low risk to start.
- Measure the time saved from the first month, to decide the next steps on facts.
- Train the teams: a misunderstood tool is an abandoned tool. Our training courses are designed for exactly that.
To go further, the use case library details what MIA and the API do day to day.
