Finding our AI cases

Big players push us to go always faster. It seems on the contrary we have to think strategically beforehand.

What we see in our organizations:

  • AI divides, some people use it without a legal framework, others are affraid of it.
  • We are pushed to “be competitive”.
  • Technologies are developping very very fast, at the cost of huge investments and cash burning.
  • Global warming asks us to be extra careful with social responsability.
  • The legal framework helps, a bit, with the AI Act.

Let’s see how we can handle this.

1. Set up a framework

This is the first step to avoid any annoying effects of the most adventurous colleagues. It is important to minimize beyond limits uses, with:

  • A selection of authorized European AI, for data soverainity
  • Clear GDPR instructions, for legal risk management and data breach cybersecurity
  • Environmental impacts counciousness, for social responsability
  • IA limits and errors counciousness, for quality management

This framework will secure teams’ uses and prepare your mind for the next step.

2. What: get inspired with existing cases

Second, scroll what others have already done in your field or in similar roles.

For example:

  • saving time in:
    • Steering committees preparation
    • construction site planification
  • helping out with:
    • searching for products references
    • creating an offer
    • dispatching leads

Gatherings and podcasts are the best to learn from others, better than the news too much sponsored by big players.

3. How: build up your first demand

Third, define one experimental cases, its KPIs, and its testing frame:

  • team involved
  • time budget accepted risks
  • accepted output quality
  • accepted bias
  • governance checkups
  • ROI targets

Then, you can look up at your selection of authorized European AI, for a short list of offers that could fit your needs.

4. Why: test, learn, and decide

Finally, this first experiment will bring you all you need to know about what’s next. Stop or more?

What are the impacts on:

  • team’s productivity
  • team’s health (i.e. risk of AI fatigue)

Risk mitigation:

  • if AI replaces the youngest ones, what happens when the experts will retire?
  • what happens if token’s price rises ?

This new path reminds me of all the questions we had on the impacts of digital transformation. It was a new world, there we have a new one again!