One of the most common miscalculations I see from subject matter experts is assuming that a good idea is enough. It is not. A point of view may be insightful, original and well supported. Yet it can still fail to resonate because it has not been connected to a specific audience facing a specific situation.
The question is rarely, “How do I get this in front of more people?” The better question is, “Who is this for, and what are they trying to do?” That sounds obvious. In practice, it is surprisingly difficult. Many experts define their audience in broad categories. They write for business leaders, data professionals, marketers or anyone interested in AI.
These labels are useful for conference registration forms. They are much less useful when trying to communicate a meaningful idea. Real audiences live inside real constraints. A chief data officer in a regulated bank is not simply interested in AI. They may be under pressure to demonstrate business value while satisfying governance, audit and risk requirements. A marketing leader may care about personalisation, but also worry about customer trust and consent. A CISO may welcome innovation while losing sleep over invisible risk. The job title matters. The situation matters more.
Relevance is contextual
This is why relevance is contextual. The same idea can mean very different things to different people. A practitioner may want to understand how something works. A department head may want to know whether it improves productivity. An executive may be interested only in organisational impact, risk or cost. When we try to speak to all of them at once, we usually end up speaking clearly to none of them. The discipline is to start with the audience’s problem rather than your solution.
Many experts begin with the thing they want to explain. A technology trend. A framework. A research finding. A product capability. Only afterwards do they look for an audience.
The strongest thought leadership works in the opposite direction. It starts with understanding the pressures people face. It understands the trade-offs, frustrations and constraints that shape decisions. It uses language that feels familiar because it reflects the reality of the audience’s working life.
Speaking the language of work
This is where subject matter experts have a natural advantage. They often see patterns that customers experience only once. They hear the same questions repeatedly. They observe where ambitious plans collide with operational reality. They notice recurring mistakes across different organisations. Those observations are valuable. But they only become useful when translated into the audience’s world.
I often think about the difference between category language and work language. Category language addresses AI transformation, digital acceleration, and customer intelligence. Work language talks about reducing false positives, shortening model validation cycles, improving campaign conversion without increasing risk, or helping frontline teams trust recommendations.
One sounds like a market narrative. The other sounds like Tuesday morning. Good thought leadership recognises that people do not live within categories. They live inside constraints e.g. too little time, too many stakeholders, too much technical debt, too many competing priorities.
Acknowledging those constraints does not make an expert sound cautious. It makes them sound credible.
Listening before writing
This is also why direct exposure to audiences matters so much. Customer conversations, workshops, advisory boards, community discussions, event questions and even LinkedIn comments. These are not simply channels for engagement; they are listening systems. They reveal how people describe their problems when no vendor is in the room. They expose the gap between public narratives and operational reality. They often reveal concerns that never appear in surveys or strategy presentations.
AI can help identify patterns across those conversations. It can surface themes and recurring questions. But judgment still matters. Scale can reveal patterns. Human interaction reveals meaning. The most useful audience insights usually come from combining both.
Use technology to detect recurring signals. Use conversations to understand why those signals matter.
The hidden side of decision-making
There is one final observation worth remembering. People are not motivated only by what they want to achieve. They are often equally motivated by what they want to avoid. Failed projects, regulatory exposure, wasted investment, internal embarrassment, or loss of credibility. Many business decisions are shaped as much by fear as ambition. The visible goal is only half the story. The strongest thought leaders understand both.
A chief data officer may want to scale AI, but worry more about scaling inconsistency. A marketing leader may want greater personalisation, but be more concerned about customer trust. A security leader may support innovation, but remain focused on avoiding unseen risk.
Understanding that tension often reveals what really drives decisions.
The real test
Audience definition is not a one-time exercise. It evolves. You learn from conversations. You notice which examples travel. You pay attention to the questions people ask after the presentation. That is usually where the real signal lives. A point of view becomes thought leadership only when it helps someone make sense of a challenge they recognise.
Not everyone, but someone specific enough to see themselves in the problem. That is the discipline. First decide what you believe, then decide who it helps. Everything else follows from there.