In today's rapidly evolving AI landscape, effective training isn't just about delivering information—it's about creating lasting behavioral change and practical skills that professionals can apply immediately in their work.
How do you know if a training is right for your goals? And how do you know if that training will be effective? Choosing an educational path can feel confusing, especially in a hyped industry like AI, and most people end up guessing.
At AI Academy, we've developed a scientifically-grounded approach to AI training that guarantees you can reach the outcomes you want. The results speak for themselves: we have successfully trained over 12,000 individuals and teams, maintaining an impressive 4.83/5 average course rating. Some of our students have even transformed their careers to become AI consultants.
There are many different kinds of AI training on the market, from free 30-minute videos to 5-year university-level master's degrees. How do you pick what's right for you? It all comes down to the training outcome you want.
From a scientific perspective, the possible outcomes of training were codified by Benjamin Bloom in 1956. The core idea Bloom introduced is that there are different levels of skills, and each one allows learners to reach a specific outcome.
At AI Academy, we've built upon Bloom's taxonomy and used our 10 years of experience in AI to adapt it to this specific industry.The result is our AI skills pyramid.
We identify 4 possible AI skill levels:
1. Knowledge
At this foundational stage, you are aware of core concepts and basic principles of AI but can't really use it. An example might be someone who has heard about ChatGPT on the news or social media but doesn't have an account.
2. Understanding
Here, learners begin actively engaging with tools and techniques, though they may not always achieve optimal results. An example is someone who has successfully used AI for basic tasks like summarizing text but wouldn't be comfortable relying on it heavily for their work.
3. Application
At this level, professionals can confidently and reliably apply their skills in their daily work. This is when someone can consistently use AI effectively in their work.
4. Mastery
The pinnacle of the learning journey, where practitioners can not only apply their knowledge effectively but also innovate new solutions, teach others, and drive organizational change. Someone who has reached mastery can build automations that go beyond using generic tools like ChatGPT and has the potential to support others in using AI too.
Knowing what skill level someone wants to reach allows us to design AI training programs that work. You see, deciding whether a course should be on-demand, with live sessions, or whether it should last 2 hours or 2 months, shouldn't be guesswork. It's a decision that should be rooted in scientific evidence, and this is what we do.
Let's use an example. Suppose you want to teach someone how to drive a car. Would you be comfortable being in the passenger's seat if all this person did was read a book about driving? We wouldn't. If the intended learning outcome is to enable someone to do something, then passive content consumption isn't enough.
This is why our knowledge and understanding-level training is on-demand, but our application and mastery-level training includes hands-on activities and experiential learning components. It's not just because we like having live workshops with people—it's because science (and the results we've seen) tells us it works.
Scientific references: Bloom's Taxonomy, spaced repetition (Bjork, 1994), deliberate practice (Ericsson & Pool, 2016), and interleaving (Bjork & Bjork, 2011) to deepen knowledge retention and skill mastery.
Knowledge alone isn't enough—success requires developing sustainable habits. It's like going to the gym: you won't see results if you just learn how to do a squat; you have to actually do it regularly. Similarly, you won't see any impact from AI in your work or company if you just know how to use it but never do. The habits progression occurs through four key stages:
1. Awareness
The initial exposure phase, where individuals recognize the potential but haven't yet incorporated new tools into their workflow. This would be like someone who knows what ChatGPT can do but doesn't even have an account.
2. Occasional Use
At this stage, practitioners experiment with new approaches for specific tasks but haven't yet transformed their regular work processes. You may have an account on some AI platform but use it once in a while for simple tasks while keeping your pre-AI workflow (and speed and quality).
3. Integration
Here, you have successfully adapted your workflows to incorporate new tools and techniques effectively on a regular basis. You're an AI-powered professional: your work speed and quality have greatly improved as AI has become an integral part of your processes and toolkit.
4. Advocacy
The highest level of adoption, where you not only use tools extensively but also guide and inspire others. This means you're constantly on top of the latest AI news, can confidently talk about it with other people, and are seen by your peers as a reference for AI.
Scientific references: Prosci's ADKAR model
You should progress up both the skills and habits pyramids. If you know how to use a technology (high skills) but haven't integrated that into your work (low habits), what's the point of these skills? Similarly, if you use AI all the time (high habits) but with basic, unreliable techniques (low skills), aren't you missing out? The intersection of skills and habits creates distinct practitioner profiles:
Novice: Beginning their journey with basic knowledge and awareness
Explorer: Building understanding while starting to apply new tools occasionally
Pro: Confidently applying skills and integrating them into daily work
Champion: Mastering advanced applications while advocating and teaching others
Not everyone needs to reach Champion level, but you (or your team if you're a manager) should definitely be at least at "Pro" level.
