Many companies have experimented with generative AI, but pilot projects often failed to deliver the expected value. To achieve measurable results, Mistral AI proposes an approach based on identifying an "iconic" use case.

Criteria for an Iconic Use Case

An ideal use case must meet four fundamental criteria:

  • Strategic: It must concern a key business process or a transformative new capability, capable of interesting top management.
  • Urgent: It must solve a critical and current problem, justifying the investment of time and resources.
  • Impactful: It must be pragmatic and lead to a production release to test the solution with real users and gather feedback.
  • Feasible: It must be able to be implemented quickly, with a working prototype in a few weeks and a production launch within three months, to ensure a quick return on investment.

What to Avoid

Mistral AI organizes workshops with customers to identify the most suitable use case, discarding projects such as:

  • Moonshots: Ambitious projects without a clear path to a quick ROI.
  • Future investments: Long-term projects that can wait.
  • Tactical fixes: Projects that solve immediate problems but do not lead to significant improvements.
  • Quick wins: Useful for building momentum, but not transformative.
  • Blue sky ideas: Revolutionary projects, but not yet mature.
  • Hero projects: High-pressure initiatives without executive support or realistic deadlines.

From Validation to Deployment

Once the use case has been identified, the validation phase begins, which includes data analysis, defining the pilot infrastructure, and choosing the deployment environment. Subsequently, the development phase proceeds, in which the Mistral AI and customer teams collaborate to create and implement the solution.

The success of an AI project depends on the choice of the initial use case, which must be ambitious enough to inspire, urgent enough to require immediate action, and pragmatic enough to produce concrete results. For those evaluating on-premise deployments, there are trade-offs to consider; AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.