How to recognize quality AI training?

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.

1. 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 UAT by a third, faster code review, faster test coverage, standardized README in all repositories).
2. 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.
3. 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.
4. 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).
• Ask: How will you incorporate our goals and processes into the training process? What specific use-cases from our environment will you include?

Emphasis on practical use: Training in real situations increases the chances of a real adoption.

• Participants solve examples that they will use in their work.
• Clear benefit to the specific role (FE/BE/data/QA/PM) of what specifically will change in their work.
• Ask: What will participants take away from the training? How will we measure the impact of the training?

A balanced mix of theory and practice: Purely theoretical blocks end up with notes with no real impact. Make sure that during the training the concrete "how" will prevail over the "why".

• Short "why" (limits, risks, best practices), most of the time "how" (hands-on).
• Vendor-neutral approach across tools/models.
• Ask: What is the ratio of theory vs. practice and why?

Support during training: The active approach of trainers in small groups keeps the pace across seniority, when absent, weaker participants get lost and the rest stall.

• The instructor continuously checks the situation so that no one is left behind and gets lost.
• Small groups with a clear exercise structure.
• Clear materials with links to more detailed resources.
• Ask: How do you ensure that the less experienced keep up?

Choice of tools and safety: Tools aligned to your governance and secure data handling will enable a smooth and sustainable deployment of training into practice.

• The tools should be tailored to you, respecting your rules and financial possibilities.
• Working safely with data. The tools you choose should always match the confidentiality of the data you process. For example, the choice of tools for processing marketing content and for processing customer data will differ.
• Ask: How will you work with our data and approaches? Which tools do you recommend and why?

Follow-up and adoption: Materials, consultation and a clear follow-up plan make one-off training a real competitive advantage. Without follow-up support, new habits easily fall apart. Plus, the world of AI is changing so fast that a lot of information soon becomes irrelevant after training and you need to refresh your knowledge regularly.

• Availability of the trainer after the training.
• Summary, checklist and recommended next steps for teams.
• Possibility of follow-up cooperation
• Ask: What is included after the training? Do you offer follow-up consultation and materials?

Red Flags

If you come across any of the following points, beware. Keep your hands off such training.

• Lack of a clear agenda
• Vendor limitation
• Lack of references from a similar environment
• One-size-fits-all programme
• Unclear contribution to individual roles
• Does not address security and protection of sensitive data
• No materials or usable outputs
• Excess group capacity
• Outdated or superficial content
• Opaque pricing

Where to find quality training?

• AI practitioners who are not just trainers: people and teams who use AI in real projects (development, data, QA, marketing) on a daily basis and treat training as a by-product of their practice.
• Software studios that actually incorporate AI into delivery. They can show real workflows, not just demo tools.
• Communities and professional events. Meetups and conferences often reveal who has real experience and how they teach. You can kind of check out the trainers there.
• Referrals from partners and related companies in your ecosystem.

Conclusion

If you want your company's AI implementation to be successful, the right training is one of the first prerequisites for success. Don't be afraid to take your time in the selection process and do your due diligence on the vendor. Training is often the first impression of AI that your teams take away and should motivate them to stay interested in the topic.

Don't extinguish their motivation to innovate before it has even begun.

Written by Vaclav Vesely, Solution Specialist at DX Heroes