This is the most common sequencing question we get, and the usual answer - "learn the fundamentals first" - quietly sends leaders down a technical path they do not need.
The honest sequence
A thin layer of AI basics, then strategy, then more basics only as needed.
You need roughly a week of foundations: what a model is, what training and inference mean, why outputs vary, what data quality does to results, and where models fail. That vocabulary is the entry ticket. Going deeper before you have a business frame just gives you facts with nowhere to put them.
The definitions themselves are covered in AI vs machine learning vs generative AI.
Why strategy carries more weight for a leader
Your job is not to build models. It is to choose which problems are worth pointing at AI, judge a vendor claim, set guardrails, and carry people through the change. None of that is answered by knowing how a transformer works. It is answered by knowing what a use case is worth and what it costs to run.
That is the substance of AI Strategy for Executives.
When to go back for more technical depth
Go deeper when you hit a specific wall: you cannot interrogate a vendor's accuracy claim, you cannot tell whether your data is fit for a use case, or you cannot follow your own team's technical debate. Learn the narrow thing that unblocks you, then stop.
A practical four-week plan
- Week one - vocabulary and limitations.
- Week two - map five candidate use cases in your own function.
- Week three - cost, risk and governance for the best two.
- Week four - write the business case for one.
That is essentially the shape of Leading AI Innovation & Organisational Transformation.
The bottom line
Basics are a prerequisite, not the subject. Buy a week of vocabulary, then spend your real learning time on strategy, risk and adoption. Related: the fastest way to learn AI for business.