A common frustration among senior leaders is that AI advice often sounds fluent but feels generic. You ask for strategic input, a sales angle or a growth recommendation, and what comes back is tidy, sensible and completely disconnected from the commercial reality of your business.
It is not always obviously wrong. In many cases, it is worse than that: it is plausible. It sounds like the sort of answer that could apply to almost any company in almost any sector. And because it is polished enough to appear useful, many leaders conclude that AI simply “doesn’t get” their business.
AI produces generic business advice because it has no access to your proprietary context — your ideal customer profile, commercial objectives, proof points, market positioning, or competitive dynamics. Without this, it defaults to generalities drawn from its training data. EY’s 2025 Work Reimagined Survey found 88% of employees use AI at work, but only 5% use it in advanced ways. The solution is systematic context injection, not a better model.
But AI is not a mind reader. When it misses the mark, it is rarely because the model lacks intelligence. More often, it lacks context.
“AI does not fail because it lacks intelligence. It fails because leaders expect it to understand a business they have not properly explained.”— Larysa Hale, Expert Circle
What context does AI need to understand your business?
A surprising number of professionals still approach AI as though it should be able to produce a sharp strategy, a persuasive sales message or a commercially relevant recommendation from three lines of input and a hopeful tone. That is fantasy.
AI cannot pull a nuanced business strategy out of thin air. It cannot infer your market dynamics, your internal constraints, your proof points, your customer objections or your commercial priorities unless you provide them. If the business context is missing, the output will miss the mark. That is not a flaw in the model. It is the natural result of poor input.
I have seen this clearly in practice. A client once asked me to prepare cold outreach and sales copy for a campaign. Sales copy is not my natural mode. I am a marketer, not a natural salesperson. But I had invested time in understanding the psychology of strong offers and learned from people with deep expertise in that discipline. So I did not simply ask AI to “write an email”. I gave it the market context, the technique I wanted to use, the objections we needed to address and the standard I was aiming for.
The result was an e-shot that sounded like something a seasoned copywriter would have written — and it generated leads almost immediately.
That is the point. AI did not magically become a brilliant salesperson. It performed well because it was given enough business reality to work with.
Why context matters more than clever prompting
This is where many senior leaders go wrong. They focus on the prompt itself rather than the strategic substance behind it. They ask for outputs before they have transferred the commercial DNA of the business.
That commercial DNA includes the target market, the buying psychology, the business model, the offer, the positioning, the objections, the internal evidence and the outcome being pursued. Without that, the model defaults to generalities because generalities are all it has.
You can see why this matters. If you ask AI to produce something in an area you know nothing about, you are not in a position to judge whether the answer is good, weak or commercially dangerous. That is why leaders do not need to be the strongest operator in every specialist discipline, but they do need enough understanding to direct the work and assess the response.
“High-value AI output requires high-value business context. If you want relevance, you have to feed it the market reality, not just the task.”— Larysa Hale, Expert Circle
AI is best used as a collaborator you lead, not a substitute adviser you blindly trust.
How to give AI the business context it needs: three steps
First, inject context before asking for deliverables. Do not ask for “strategy” in the abstract. Feed AI the reality of your market: your ideal customer profile, their frustrations, your offer, your proof points and the commercial objective you are trying to achieve.
Second, maintain director-level oversight. You do not need to personally draft every line, but you do need to know what good looks like. If you cannot judge the output, you should not be delegating that thinking to AI in the first place.
Third, stop expecting AI to solve the business for you. Use it to clarify, structure and accelerate your thinking — but keep responsibility for the direction yourself.
If AI advice still feels generic, the issue is usually not that AI “doesn’t get” your business. It is that the business has not been properly explained to the AI.
High-value output requires high-value context. The model cannot invent the commercial specificity that leaders fail to provide.
Our briefings and programme are designed for senior leaders who want to move beyond textbook answers and build a more disciplined, commercially grounded way of working with AI. Because the real value of AI does not come from asking it more questions. It comes from teaching it enough about your business to ask better ones.
Larysa Hale is the founder of Expert Circle and creator of the AI-Driven Marketing Growth Programme, a structured series of briefings and masterclasses for managing directors and senior leaders in professional services. She has spent over 15 years helping founders, marketing directors and business leaders build commercially grounded growth strategies.
