AI training and trainers have been on a tear lately. But how do you tell the really good ones that will bring you value from the "for effect" ones, full of tool demos and promotional promos? That's what we'll go over in this article.
Choosing the wrong training is not just a waste of money and time, it can cost you much more:
- low adoption and zero impact,
- demotivating the AI team to use it,
- vendor lock on the wrong tools,
- security risks,
- procedural chaos and technical debt.
Before you start choosing
It's always a good idea to be clear on a few points before you start the actual selection of training.
- Clarify your goals and expectations: What does the training change in practice? Name 2-3 metrics that you want to move forward (e.g., reducing lead time by 20%, reducing bug rate in by a third, faster code review, faster test coverage, standardized README in all repositories).
- Make sure AI is the right solution: It is possible that your goals can be achieved in ways other than using artificial intelligence. Before choosing training, consider whether it would be enough to train your team to work better with automation, for example.
- Map the baseline: Measure how you are doing before the training, that's the only way you can tell if it has brought you any results. A short before and after audit will save you the "did it help" debates later.
- Find out the readiness of the team: Map out roles, tools and constraints: who is training, what is the stack, what data can you use, what rules apply. It's especially important to appropriately select the trainees who will really benefit from the training. Focus on their seniority and previous experience with AI, so that the training brings them as much new information as possible, but at the same time is not too challenging for them.
Criteria for quality AI training
Relevance & personalization for your business: Only training tied to your goals, processes, and stack will deliver rapid, measurable improvement. Otherwise, you'll end up with generic tips that don't stick in practice.
- Training is based on your goals, processes and roles (not a one-size-fits-all).
- Use-times and tasks map the real work of your teams.
- Tools and procedures are selected for your stack and constraints (policy, data, access, working in existing tools).
