Yes - and in most organisations the people setting AI strategy are not engineers. Strategy is about which problems to solve, what value to expect, and which risks to accept, not about building models. If you can read a business case, question an assumption and hold a supplier to account, you already have the core skills. What you need to add is enough fluency to ask the right questions and recognise a good answer.
What you actually need to understand
- What AI is good and bad at - pattern-finding and language tasks versus judgement, ethics and novel situations
- Where value comes from - saving time, improving decisions, or enabling something you could not do before
- What can go wrong - bias, hallucinated output, data leakage and over-reliance
- How to measure success - so a pilot proves value before you scale it
None of that requires mathematics. It requires clear commercial thinking applied to a new tool.
The skills that transfer straight across
Prioritisation, stakeholder management, budgeting and risk assessment are exactly the skills AI strategy depends on. A non-technical leader who understands the business often makes better AI decisions than a technical specialist who does not, because they start from the problem rather than the technology. For more on this gap, see what skills you need to understand business AI.
How to build fluency quickly
- Use the mainstream tools yourself for a week - fluency beats theory
- Learn the vocabulary so you are not sold to on jargon
- Study a few real cases of what worked and what failed
- Practise writing a one-page AI use case with a cost, benefit and risk
The bottom line
AI strategy is a business discipline first and a technical one second. Non-technical professionals who learn the fundamentals become the people who direct AI rather than being directed by it. That is exactly what the Strategic Application of AI in Business course at London School of Business UK is built for - no coding required. Enquire today.