AI Can’t Read Your Mind

When Product Behaviour Lives in People's Heads

Most software development teams treat product behaviour as something that "lives in people's heads," scattered across meetings, Slack threads, Figma comments, and half-formed assumptions. This means developers begin every feature by reconstructing missing knowledge, often guessing at how the product should behave. Instead of moving forward, they burn time digging backward through ambiguity, trying to discover what the system is actually supposed to do. The absence of a structured behavioural definition turns every task into an archaeological expedition.

This is not a minor inefficiency but the central constraint on developer capability. When the product behavioural knowledge is unstructured, developers cannot maintain flow, cannot predict outcomes, and cannot rely on past artefacts to guide future work. Every feature becomes a fresh instance of the same knowledge discovery struggle. That struggle is invisible in traditional metrics, but painfully visible in slow starts, inconsistent interpretations, and constant clarification loops. Without a consistent way to capture and express what the system should do, the knowledge gaps widens with every added feature and every new team member.

And no! This perspective does not deny the creativity of software development or the value of exploration. It simply recognizes that creativity thrives when the essentials are stable. Most product behaviour is "table stakes": routine, predictable, necessary features that every system must get right before innovation can happen on top. Treating everything as chaotic or purely creative forces developers to reinvent the basics repeatedly instead of focusing their ingenuity where it actually matters. Formalizing behavioural knowledge doesn't constrain creativity but protects it.

The result is a system that behaves like a black box: knowledge goes in, but nobody can see how it is transformed or whether it is complete. This undermines alignment across roles, and it makes discovery unpredictable and emotionally taxing for developers who must continuously infer behaviour from incomplete clues. Unless behavioural knowledge is formalized, the team will always operate in a reactive mode, working around the absence of clarity rather than building on it.

When product behavioural knowledge isn't made explicit, teams face widening knowledge gaps because your organization does not formalize the behavioural knowledge developers need, leaving the Knowledge Discovery Process chaotic, slow, and largely invisible.

Why Ambiguity Multiplies Cost in the AI Era

The lack of formalized product behavioural knowledge drives costly rework and prevents GenAI tools from generating reliable tests and code.

When developers build against assumptions instead of precise behavioural descriptions, defects become a structural inevitability. Teams ship features that appear correct in isolation but break user flows, violate edge conditions, or contradict rules hidden in someone's head. Industry studies routinely show that rework consumes 30–50% of engineering capacity and most of it stems from misunderstandings, not technical difficulty. Without a shared behavioural source of truth, every team member interprets the product differently, and those divergent mental models surface later as defects, inconsistencies, or late-cycle "fixes" that rewrite (throw away) earlier work.

This ambiguity becomes even more expensive when AI enters the workflow. GenAI tools depend entirely on structured knowledge: if the product behavioural expectations are unclear, incomplete, or spread across documents, the model cannot produce coherent tests or trustworthy code. Instead of accelerating the team, AI creates more noise by generating tests that miss critical behaviours or code that passes the wrong scenarios because those scenarios were never formalized. What should be a productivity multiplier turns into an amplification of waste. Teams end up reviewing, repairing, and rewriting AI output because the model was never given the behavioural clarity it needed to succeed.

Over time, these compounding failures erode predictability across the entire system. Delivery slows because defects ripple through the pipeline. Cognitive load increases as developers work harder to reconstruct missing intent. AI becomes unreliable because the upstream knowledge isn't expressed in a format it can reason about. The organization pays for the same knowledge gaps twice — first in human rework, then again in AI inefficiency.

Without precise behavioural knowledge, you pay for every feature multiple times — once to guess it, once to fix it, and again when AI repeats the same mistakes.

Turning Intent Into Explicit Behaviour

You must introduce a knowledge-centric Product Specification that breaks the product into a set of features, and describes how the system should behave through executable Behavior-Driven Development (BDD) scenarios.

BDD scenarios are structured behavioural descriptions written in the Given/When/Then format. Given sets the initial context, When defines the action or trigger, and Then specifies the expected outcome. This simple structure forces clarity around roles, data, UI state, and system responses, ensuring that every behaviour is described in a way that both humans and AI tools can interpret unambiguously. Because they express behaviour rather than implementation, BDD scenarios become the ideal bridge between discovery, design, testing, and code generation.

This approach recognizes that behavioural clarity is not a documentation exercise but a systemic capability that determines how efficiently developers discover, understand, and apply knowledge. By capturing product behaviour in structured, executable BDD scenarios, you transform invisible assumptions into visible operational knowledge, reducing the cognitive load that developers must carry and eliminating the need for interpretive guesswork.

A rigorous Product Specification process becomes the first tangible artefact that developers, QAs, and AI agents can build upon. It breaks each feature into its own behavioural module and defines the full scenario space: happy paths, edge conditions, validation rules, permission logic, and system safeguards. This ensures that both humans and AI tools work from the same authoritative behavioural contract. Meanwhile, this knowledge-centric operating model ensures that this specification is not a static deliverable but a living part of the development system — continuously updated, traceable across phases, and directly embedded into Iterative TDD development workflows.

This solution reduces the knowledge gaps by making product behavioural knowledge explicit, structured, and executable. When knowledge moves predictably from discovery → specification → design → tests → code, the entire software process stabilizes. Developers operate with clarity. AI generates consistent, scenario-aligned outputs. Teams maintain flow because ambiguity disappears upstream rather than being corrected downstream.

Formalize product behavioural knowledge through a structured Product Specification, and you convert the knowledge gap from a hidden constraint into a manageable, predictable part of the engineering system.

A Predictable, AI-Ready Engineering System

When you formalize behavioural knowledge through a Product Specification process, the entire development system evolves — from chaotic knowledge discovery to predictable, aligned, AI-ready execution.

Once the behavioural knowledge gaps are systematically closed upfront, downstream work stops behaving like a gamble. Developers begin features with a clear map of expected behaviour, which reduces ramp-up time, unlocks flow, and lowers cognitive load. QAs gain a stable foundation to design tests without reverse-engineering intent. Architects trace dependencies cleanly because every executable BDD scenario expresses precise conditions and outcomes. The system becomes more predictable because people no longer reconstruct intent; they build from shared, explicit knowledge. This stability compounds over time: each new feature enriches the organization's behavioural knowledge base rather than resetting the discovery struggle.

The impact is even more profound when AI enters the picture. With a structured Product Specification, GenAI tools finally receive the behavioural clarity they need to generate consistent tests and scenario-aligned code. Instead of amplifying uncertainty, AI amplifies precision. Prompt-driven Iterative TDD becomes feasible: tests flow directly from scenarios, code flows from tests, and developers spend more time reviewing and designing rather than debugging misunderstandings. AI becomes a force multiplier, not a source of waste, because the behavioural contract anchoring its output is explicit, complete, and unambiguous.