Problem framing is often treated as a prelude. Something to move through before the “real work” begins. In practice, it is the work. It determines what gets funded, what gets built, and what gets ignored. For subject matter experts supporting customers, it is one of the few levers that consistently shifts outcomes. When done well, it changes how a customer sees their situation. When done poorly, it reduces the conversation to features and price.
Why framing the problem is the work
This is not a new idea. Across disciplines, decisions are shaped upstream. If the problem is defined narrowly, solutions will optimise within that constraint. If it is defined accurately, the solution space expands. The implication is simple. The quality of your framing often matters more than the quality of your answer. That is why it remains one of the strongest signals of expertise .
Start with why this matters in your context. Most customers arrive with a stated problem. It is rarely neutral. It is shaped by internal pressures, prior investments, and partial understanding. “We need a dashboard.” “We need to deploy GenAI.” “We need to reduce cost.” These are not wrong. But they are often incomplete. If you accept them at face value, you inherit their limitations. You also inherit the politics behind them.
Where most experts go wrong
This is where many experts move too quickly. They respond with solutions because that is visible and rewarded. It feels productive. But it weakens credibility. Customers can sense when their underlying issue is not being addressed. The result is polite engagement, followed by slow decisions or stalled deals. The surface problem was solved. The real one remains.
Symptoms, causes, and the discipline of diagnosis
A more disciplined approach starts by separating symptoms from causes. This is widely accepted practice. Techniques such as iterative questioning are common across consulting, engineering, and medicine. The aim is not to challenge the customer for its own sake. It is to understand the chain of cause and effect that created the current situation. Without that, any recommendation is fragile.
In pre-sales, this becomes a diagnostic tool rather than a messaging exercise. You are shaping how the buyer understands their situation before they evaluate your product. If that understanding is narrow, the evaluation becomes a comparison of features. If it is broader and more accurate, you influence the criteria, the urgency, and often the budget.
Reframing in pre-sales: from request to reality
Consider a familiar example. A customer asks for better reporting. The immediate response is to demonstrate dashboards. This is expected. It is also limiting. A stronger framing asks what reporting is meant to achieve. In many organisations, reports exist. The issue is that they do not change their behaviour in time. Decisions are delayed. Accountability is unclear. Metrics are not trusted.
The reframed problem is the quality of reporting. It is decision latency. That shift matters. It moves the discussion from visualisation to decisioning, workflows, and governance. It also brings new stakeholders into the conversation. Operations, risk, and compliance now have a reason to engage. The scope of value expands without changing the underlying technology.
This is not manipulation. It is precision. You are aligning the problem with how the organisation actually functions. The mechanism matters here. Fragmented data pipelines lead to inconsistent metrics. Inconsistent metrics erode trust. Low trust slows decisions. Each step is observable. Each step can be tested. Without that chain, the framing becomes opinion rather than diagnosis.
A practical sequence that holds under pressure
There is a practical sequence that tends to hold across cases. First, surface the current frame. Let the customer describe the problem in their own terms. This reveals both the issue and the context around it. Second, introduce a tension. Use patterns you have seen elsewhere. Keep it modest. “In similar situations, reporting improves, but decisions do not change at the same rate.” This opens the door without dismissing their view.
Third, offer a reframed problem with a clear mechanism. Explain why the issue persists. Avoid abstract language. Show the links between systems, behaviour, and outcomes. Fourth, connect this to consequences the customer recognises. Delayed decisions, duplicated effort, regulatory exposure. If these consequences do not resonate, the framing will not hold. Fifth, and only then, map to your solution. At this stage, features become evidence of fit rather than the centre of the conversation.
The line between precision and overreach
This sequence is simple. It is also difficult to execute. Most teams fail in two ways. They overreach, or they stay too safe. Overreach looks like a grand narrative that does not match the customer’s reality. It relies on generic trends. It weakens trust. Staying too safe looks like accepting the stated problem and optimising within it. It limits impact. The discipline is in finding the level of abstraction that is both accurate and useful.
Thought leadership as reinforcement, not promotion
Thought leadership plays a role here. Not as promotion, but as reinforcement. Pre-sales conversations are rarely linear. Customers seek validation between meetings. They look for language they can use internally. Well-structured content that articulates the reframed problem gives them that language. It helps your internal champion build alignment when you are not present .
This is often misunderstood. Many teams use thought leadership as a disguised product pitch. Customers recognise this quickly. It shifts the interaction into procurement logic. The opportunity is lost. Effective thought leadership focuses on the problem. It draws on established ideas. It provides a credible external frame. It shows that the issue is recognised beyond a single vendor interaction.
Using established ideas without overclaiming
There are established frameworks you can use carefully. The adoption gap described in Crossing the Chasm is one example. Many organisations see early success with a tool that does not scale. The surface problem is often framed as training or usability. The deeper issue is adoption mechanics between early enthusiasts and the mainstream. Naming that gap changes the intervention.
Behavioural research offers another lens. Work such as Thinking, Fast and Slow shows that better information does not guarantee better decisions. Bias, incentives, and context shape outcomes. In a customer conversation, this allows you to position governance and guardrails as necessary. Not as optional features, but as responses to predictable human behaviour.
These are not tactics. They are ways to ground your framing in widely accepted knowledge. They reduce the risk of overclaiming. They also help you avoid a common trap. Mistaking correlation for causation. As data volumes grow, this risk increases. Pattern recognition can highlight issues. It cannot explain them. Interpretation remains the responsibility of the expert.
AI and the risk of false confidence
This becomes more important as AI is introduced into customer environments. AI can surface anomalies and recurring patterns at scale. It can accelerate discovery. But it does not remove the need for judgment. In some cases, it increases the risk of false confidence. The role of the thought leader is to validate and contextualise. To ensure that patterns are linked to plausible mechanisms.
So far, these examples sit close to your day-to-day work. It is worth stepping back. Strong problem framing has shaped entire fields. It is not confined to pre-sales or technology.
What enduring examples teach us about framing
In The Innovator’s Dilemma, Clayton Christensen reframed corporate failure. The common assumption was poor management. His argument was that successful companies fail because they listen too closely to their best customers. This inverted a deeply held belief. It provided a mechanism in disruptive innovation. It gave leaders a reason to invest in uncertain opportunities.
In The Lean Startup, Eric Ries reframed the concept of innovation risk. The issue was not a lack of planning. It was a wasted effort due to untested assumptions. The build–measure–learn loop offered a practical discipline. It reduced uncertainty into a repeatable practice. This changed how both startups and large firms approached product development.
In Blue Ocean Strategy, W. Chan Kim and Renée Mauborgne reframed competition. The problem was not beating rivals in crowded markets. It was competing in the wrong space. This encouraged leaders to question industry assumptions rather than optimise within them. It shifted attention from incremental gains to structural change.
In The Fifth Discipline, Peter Senge reframed organisational failure. The issue was not individual capability. It was the absence of systems thinking. This encouraged leaders to see interdependencies. It influenced how organisations approach learning and complexity.
In The Black Swan, Nassim Nicholas Taleb reframed uncertainty. The problem was not randomness itself. It was the tendency to underestimate rare, high-impact events. This challenged expert confidence. It changed how risk is discussed in finance and policy.
Outside business, the same pattern holds. Silent Spring by Rachel Carson reframed environmental risk. The issue was not isolated chemical use. It was systemic ecological harm. This shifted public understanding and led to regulatory change.
Across these cases, the pattern is consistent. Effective framing challenges a default assumption. It introduces a mechanism that explains persistence. It provides a path to action without claiming certainty. It does not rely on novelty for its own sake. It relies on clarity and evidence.
The implication for experts and their marketing partners
For subject matter experts, the implication is direct. Your credibility is not built on how much you know. It is built on how precisely you define the problem. If your audience does not change how they see their situation, they will not change what they do next. Your solution, however strong, will be evaluated within the wrong frame.
For marketing partners, the implication is equally clear. Content should not start with the answer. It should earn the right to present one. This means investing in the articulation of the problem. It means equipping experts with language that is specific, evidence-based, and transferable. It also means resisting the pressure to oversimplify for reach at the expense of accuracy.
There is also a discipline in restraint. Not every interaction requires a full reframing. Sometimes the stated problem is accurate. The role of the expert is to judge when to intervene and when to support. Overuse of reframing can feel contrarian. Underuse reduces you to a supplier.
A final test of your own practice
The motivation to improve here should not come solely from the process. It should come from observing the outcomes. When framing is done well, customers often recognise their own situation more clearly. They gain language to describe it internally. Decisions move faster. Stakeholder alignment improves. When it is done poorly, progress slows. Conversations revert to features. Value is harder to demonstrate.
The broader point is simple. Problem framing is not a step. It is a capability. It sits at the intersection of expertise, communication, and judgment. It is where thought leadership either earns trust or loses it. For those supporting customers to get more value from existing platforms, it is often the difference between incremental improvement and meaningful change.
If you want to examine your own approach, start with a practical question. In your last three customer interactions, did you accept the problem as stated, or did you reshape it with evidence? If the answer is the former, the opportunity is clear. The next step is not to learn a new technique. It is to apply discipline to one you already know.