Artificial Intelligence has moved from curiosity to boardroom agenda in record time. Senior leaders are being told it will transform productivity, accelerate decision-making and unlock faster growth. Yet many are discovering a more awkward reality: the output often feels bland, predictable and commercially weak.
The grammar is tidy. The structure is fine. The tone is polished enough. But the substance is missing. It has what many now recognise as the unmistakable “AI smell” — content that sounds competent at first glance, yet says very little of value.
The easy conclusion is that AI is overhyped or generic by nature, and that is the wrong conclusion.
AI-generated content sounds generic because most users provide generic inputs. When AI receives a vague request without audience context, commercial objectives, or quality standards, it defaults to the statistical average. Research from MIT Sloan (January 2026) found that roughly half the performance gains from a better AI model came not from the model itself, but from how users adapted their approach. The fix is not a better tool — it is a better briefing.
More often, generic AI output is the result of generic leadership. What looks like a technology failure is usually a management failure: weak briefing, vague direction, poor context and no real standard for what success should look like.
“AI does not produce generic work by default. It produces generic work when leaders give it generic thinking, generic briefing, and generic standards.”— Larysa Hale, Expert Circle
Why most AI content fails: the briefing problem
Too many professionals treat AI like a vending machine. A thin request goes in, a polished answer comes out, and they seem surprised when it lacks originality, depth or commercial relevance.
But even a vending machine requires precision. You do not walk up to it, press a random button and then complain because you did not get what you wanted. You choose the exact product. You know what outcome you expect before you press anything.
AI works in much the same way. If you want a specific result, you have to be specific about what you are asking for.
This is where many business users go wrong. They ask for “a blog post”, “a LinkedIn post”, or “an email draft” without defining the strategic intent behind it. They do not explain the audience, the commercial objective, the point of view, the tone, the constraints or the standard the work must meet. Then they blame the model when the result sounds like beige wallpaper in a suit.
You do not produce a strong painting with a single stroke, and you do not produce strong AI content with a single prompt. Good output requires intention. You need to know what you are trying to create, why it matters, what good looks like and how the final piece should move the reader.
Without that, you are not leading the process. You are guessing.
Why the user matters more than the model
The gap between mediocre AI use and high-performance AI use is not just about the model. It is about the person managing it.
Research from MIT Sloan, published in January 2026, found that in a large-scale experiment, only about half of the performance gains seen after switching to a more advanced AI model came from the model itself. The other half came from how users adapted their prompts. The researchers noted that the best prompters were not software engineers — the skill was clear communication.
That aligns with what many leaders are seeing in practice. The same AI tool can produce lifeless, forgettable content in one pair of hands and commercially sharp, insight-led work in another. The difference is rarely luck. It is usually direction.
Ethan Mollick, a professor at Wharton who studies AI’s effects on work, has argued that leaders should stop thinking about AI as a simple tool and start thinking about it more like an unfamiliar but capable co-worker. That is a useful shift. If you hired a bright senior assistant and gave them no context, no structure, no clear scope and no quality standard, their poor output would not be evidence that the person was useless. It would be evidence that you briefed badly.
AI is no different. It defaults to the average because average is what it has most readily available. If you want it to move towards your commercial edge, your perspective and your standards, you must supply what the model does not know.
“If your AI content feels bland, the problem is rarely the tool. More often, it is that nobody has properly directed it towards a specific commercial outcome.”— Larysa Hale, Expert Circle
From prompting to briefing: the shift senior leaders need to make
This is the real shift senior leaders need to make. Stop treating AI as something to “play around with” and start treating it as part of a professional workflow.
That means moving from prompting to briefing.
A proper AI brief should include the business objective, the audience, the context, the tone, the constraints and the desired outcome. It should be grounded in your market reality, not in generic internet fluff. It should reflect your point of view, not just the model’s default assumptions.
In other words, you need to onboard AI as you would a high-value team member: with direction, feedback, correction and clear expectations.
How to improve AI marketing output: three practical changes
First, inject real context. Do not ask for “content”. Explain the business goal, the customer’s frustrations and the commercial angle that matters.
Second, define what excellent looks like. Show the AI the kind of work you want it to emulate and the kind of lazy phrasing you want it to avoid.
Third, lead iteratively. The first draft is not the finish line. Push for sharper angles, stronger specificity and more useful insight.
AI can accelerate thinking, but it cannot replace it. The real return on investment is not found in the subscription fee. It is found in the quality of the briefing and the discipline of the workflow behind it.
If your AI content feels generic, the answer is not to abandon the technology. It is to lead it better.
Our briefings and programme are designed for managing directors and senior leaders who want to move beyond experimentation and build a more rigorous, commercially useful approach to AI. Because the leaders who get the most from AI will not be the ones who merely use it. They will be the ones who learn how to direct it.
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.
