The Context Gap, Part 2: The Feature Problem

This article builds on Part 1: The Context Gap, but the argument stands on its own: context loss does not only make enterprise work less efficient. It can eventually become product complexity.

Technical debt does not always begin in the code. Sometimes it begins much earlier, when customer context is lost before development starts.

I have spent years around complex enterprise technology and customer environments, and I have seen a pattern repeat itself. Sales, Marketing, Product, and Development may all be working hard. Each function may be acting rationally. Each may be doing the job the organization asked it to do. Yet the final product can still miss the customer's underlying problem.

That is not always a failure of effort or competence. Often, it is context loss. Sales sees one part of the customer's world. Marketing sees categories, segments, and messaging. Product turns needs into requirements. Development sees specifications, architecture, tickets, and acceptance criteria. Each view can be accurate and still be incomplete.

The Requirement May Be Clear While the Problem Is Blurry

Companies often imagine product development as a clean sequence: customer need to requirement to development to feature to customer value. That sequence is comforting because it suggests the work becomes more precise as it moves forward.

In reality, the path can be more complicated: customer problem to customer interpretation to sales interpretation to product or marketing interpretation to requirement to developer interpretation to feature. Every transition creates an opportunity for context to disappear.

By the time a developer receives a requirement, the requirement may accurately describe what someone asked to build while poorly describing why the customer needed it in the first place. That distinction matters. A precise specification can still point at the wrong problem.

Customers Describe Experience, Not Always the Root Problem

Customers understand their experience. They know where the friction is, what is painful, where the current process breaks, and what they wish were easier. But customers do not always understand the underlying problem, and they certainly do not always know the best solution.

A customer might say, "We need a configurable dashboard." That sounds like a requirement. It may even sound like a product strategy if the customer is important enough. But it is still a requested solution, not necessarily the underlying problem.

The real issue might be that supervisors cannot respond quickly enough to changing production conditions because they depend on engineering to modify the system. That is a different problem. It could lead to configuration, but it could also lead to better workflows, permissions, alerts, guided actions, templates, data visibility, or a different implementation model.

The organization has to understand what the customer is trying to accomplish, what prevents them from accomplishing it today, who experiences the problem, under what conditions it occurs, what the current workaround is, why the problem matters, what happens economically or operationally if it is not solved, how often it occurs, whether other customers experience it, and whether the requested feature is actually the best solution.

The goal is not simply to preserve the voice of the customer. The goal is to preserve enough context to understand the reality of the customer.

Context Debt Comes Before Some Technical Debt

In the first Context Gap article, I described context decay: the way meaning gets thinner as information moves through meetings, notes, systems, handoffs, and interpretation. In product development, that decay creates another liability: context debt.

Context debt is the accumulated liability created when important reasoning, circumstances, assumptions, and meaning are not preserved as work moves through an organization. Like technical debt, it may not create an immediate failure. The organization pays for it later.

It pays through another discovery meeting, another requirements workshop, another engineering investigation, another customer escalation, another customization, another configuration option, another workaround, or another redesign. The company is not only doing new work. It is repeatedly reconstructing knowledge it previously possessed.

The Feature Problem

Enterprise software often accumulates complexity one rational decision at a time. One important customer asks for Feature A. The request is commercially important, so it gets built. Another customer has a similar but slightly different problem and asks for Feature A to behave differently. Configuration is added. A third customer needs an exception. Another option appears. A fourth customer requires a different workflow. Another abstraction is created.

Over time, the product becomes extremely capable and increasingly complicated. Eventually the organization says, "We have technical debt." That may be true. But a deeper question is worth asking: how much of that technical debt began as context debt?

Perhaps the company repeatedly encoded individual customer requests because it failed to identify the common problem underneath them. The issue was not that customers had no signal. The issue was that the signal was fragmented by the time it reached the roadmap.

Customer Specificity Is Not the Same as Market Evidence

Context debt causes companies to confuse customer specificity with market evidence. A large customer's request is important. It may deserve serious attention. It may deserve executive visibility. It may even deserve a near-term product response. But importance does not automatically make the request representative of the market.

There is a major difference between saying Customer A wants X, Customer B wants Y, and Customer C wants Z, and saying Customers A, B, and C are all struggling with the same underlying job but their existing processes cause them to describe the solution differently. The second level of understanding is where product strategy becomes possible.

Without that context, the roadmap can become an accumulation of requests rather than an expression of a coherent market problem. The product may look customer-driven while quietly becoming customer-specific.

The Frustration Is Rational on Every Side

Developers are often executing exactly what the organization asked them to build. They receive a requirement that says the customer needs X. They build X. Later they hear that X is not exactly what the customer needed. Then Customer B needs X to work differently. Then the product needs another configuration option. Then support needs a workaround because the configuration is hard to explain.

Development may conclude that Sales or Product does not understand the market. Sales may conclude that Development does not understand the customer. Product may feel trapped between both. Marketing may struggle to explain a product whose value story has become too fragmented. All of those perspectives can be rational.

The deeper problem is that each group is operating with different amounts and forms of context. When the original customer reality is not preserved, every function has to make decisions from the part of the truth it can see.

More Documentation Is Not Always More Context

Organizations often respond to this problem by increasing documentation. More CRM fields. More requirements. More tickets. More acceptance criteria. More product documentation. More status meetings.

Documentation matters, but more information is not necessarily more context. A perfectly documented requirement can still describe the wrong problem. The goal should not simply be to document more. The goal should be to preserve the reasoning connecting problem, evidence, context, decision, requirement, and solution.

That connects naturally to established product thinking. Melissa Perri's work on escaping the build trap is a useful reminder that producing features is not the same as producing customer and business outcomes. Jobs-to-be-Done thinking and Strategyzer's jobs, pains, and gains language help separate the customer's underlying objective from a requested feature. Product discovery practices reinforce the importance of validating problems and assumptions before committing development resources.

The context debt argument builds on those ideas by looking at what happens across organizational boundaries. The problem is not only whether the product team talks to customers. It is whether the organization preserves enough meaning as customer reality moves through Sales, Marketing, Product, Development, Implementation, and leadership.

The AI Opportunity Is Better Problem Understanding

AI should be introduced carefully here. It will not magically solve poor product judgment, weak strategy, or unclear ownership. But AI may change the economics of preserving context.

Historically, giving Development access to the full context behind hundreds or thousands of customer interactions was impractical. The information lived across meetings, CRM, email, support cases, sales conversations, product discussions, implementation experience, customer interviews, engineering discussions, and people's memories.

AI may make it possible to preserve, connect, and reason across more of that context without forcing every employee to manually document everything in perfect form. The opportunity is not simply that AI writes requirements faster. The more interesting opportunity is that AI helps organizations understand the problem better before they write the requirement.

Before we ask how quickly Development can build a feature, perhaps we should ask a more important question: how much of the customer's original problem survived the journey to the developer building it?

The next generation of enterprise systems should not merely move data between departments. They should help preserve why the data matters. Because when context is lost, organizations do not simply lose information. They eventually build the consequences into their products.