dooopSoftware · Process · 11 min

How to Use AI in Requirements Discovery

Use AI to organize evidence, reveal gaps, and transform scattered reports into testable hypotheses before specifying requirements.

Published on September 6, 2026

CENTRAL THESIS

AI accelerates organizing material. The risk is turning fluent synthesis into unvalidated requirements.

Use AI to find gaps and hypotheses. Only proceed when there is a clear origin, user, and possible validation.

Interviews, tickets, minutes, and scattered conversations rarely arrive as ready requirements. Artificial intelligence helps when it transforms this material into verifiable hypotheses. The gain is in organizing evidence, revealing gaps, comparing interpretations, and preparing short validation cycles—not in letting AI decide what the user needs.

A requirement should only advance to specification when it has a clear origin, an affected user, an initial acceptance criterion, and a confirmation question still possible to test.

When AI Helps in Requirements Discovery

Requirements discovery usually starts with imperfect material. A meeting generates loose notes. An internal channel accumulates complaints. Support logs tickets with different language for the same problem. A business area requests a feature but does not clarify which operational friction it wants to remove.

This is where AI in requirements discovery can be useful: not to replace conversations with users, but to shape the raw material. The team can ask a model to group similar reports, separate feature requests from operational pains, identify ambiguous terms, and suggest deepening questions.

The difference seems small but changes the process. One thing is to use AI to write a polished requirement. Another is to use AI to make clear what is still not understood.

The second option preserves validation before specification.

The quality of this analysis depends on the context provided to the model. Anthropic defines context engineering as the selection and maintenance of information available to the model during inference, including instructions, tools, external data, and history, within a limited window Anthropic. In requirements discovery, this means AI can only organize well what it receives: transcripts, notes, tickets, product goals, known constraints, domain glossary, and decisions already made.

If the input mixes fact, opinion, and desire without separation, the output tends to seem more resolved than it really is. Fluency is not validation.

The scope here is specific: software requirements.

Separate Evidence, Interpretation, and Decision

A common mistake in AI-assisted discovery is accepting a synthesis as if it were a conclusion. To avoid this, the team needs to classify each item raised into three layers.

Evidence: something observed, said, or recorded. It can come from interviews, tickets, internal metrics, support conversations, usage observation, or identified business rules.
Interpretation: the reading the team makes from the evidence. It may be correct, incomplete, or biased.
Decision: the commitment made in the product, system, or process.

AI can help suggest this classification, but the team must review the origin. A simple example: three users say "the order gets stuck." This is evidence, provided the source is traceable. The interpretation might be "the triage queue does not provide visibility of priority." The decision could be "create automatic ordering by priority."

Notice the leap. The decision can be wrong even when the evidence is real.

Therefore, the operational question is not "did AI summarize well?" The question is: "what in this summary do we know, what are we inferring, and what are we already deciding?"

This separation also prevents the team from turning internal vocabulary into user needs. When someone asks for "a dashboard," they might be asking for visibility. When they ask for "automation," they might want less rework. When they ask for "integration," they might want to eliminate double entry. AI can list these alternatives but should not choose alone which corresponds to the problem.

Use AI to Find Gaps Before Writing Requirements

The best question to ask AI in requirements discovery is often not "write a specification." It is "what do we still need to know to write a responsible specification?"

From notes and reports, the team can ask the model to look for gaps such as:

Who is the affected user or role?
In what situation does the problem appear?
How often does the situation occur, if that information is available?
What exceptions were mentioned?
What business rule limits the solution?
What decision depends on a process-responsible area?
What observable behavior would indicate the change worked?
What simpler alternative should be considered before a new feature?

This use changes AI’s role. Instead of accelerating the move to development, it slows down the right point: premature conversion of ambiguity into scope.

In practice, a useful output can be a list of questions for new conversations with users or process representatives. For example: "when you say the order gets stuck, is the problem not knowing who should act, not having enough information to act, or not receiving a notification when the step changes?"

This question preserves investigation before closing scope.

It is also worth using AI to compare understanding versions. The team can provide two syntheses made by different people and ask for differences, conflicting assumptions, and points requiring validation. This helps make divergences explicit before they appear as rework in development.

In the requirements scope, the logic is similar: AI without criteria produces artifacts before validation. AI with criteria makes gaps, hypotheses, and pending decisions explicit.

Turn Syntheses into Testable Hypotheses

An AI-generated synthesis should enter the flow as a hypothesis, not as a closed requirement.

A useful hypothesis formulation contains four elements:

Affected user or role.
Situation in which the problem appears.
Expected change in behavior, flow, or decision.
Form of verification.

Instead of writing "the system must have a single pending queue," the team can formulate: "if the operations analyst can review pending orders in a single queue, it might reduce back-and-forth in checking, because today they switch between different screens to understand what is missing in each order."

This sentence is not yet a requirement. It is a point of investigation.

The next action can be to observe the real flow, review related tickets, interview analysts who perform the task, or test a low-fidelity prototype. Only then does the team decide if the single queue is the way, if the problem is incomplete information, if responsibility definition is missing, or if there is a misunderstood business rule.

DORA recommends working with small, independent, and testable units to get feedback on changes and review hypotheses earlier. The same guidance warns about the difficulty of reviewing and integrating large AI-generated changes DORA. Although this reference addresses development flow, the principle helps in discovery: smaller hypotheses are easier to verify than large scope packages.

This does not mean artificially fragmenting everything. It means not letting a broad synthesis hide different decisions within a single story.

Validate with Users Before Converting into Specification

AI can prepare questions, scripts, formulation alternatives, and initial acceptance criteria. But confirmation must go through users, process representatives, or observable evidence.

In requirements discovery, "validate" does not mean asking "did you like this solution?" It means testing whether the team correctly understood the problem, context, and constraints.

Some questions help:

"Does this description represent a situation that happens in your work?"
"What is missing or exaggerated?"
"In which cases does this rule not apply?"
"How do you solve this today?"
"What information would make you decide with confidence?"
"If this change existed, what would you expect to do differently?"

Human review also matters when more autonomous tools enter the flow. GitHub’s documentation on Copilot agents describes features with distinct environments and permissions and emphasizes human supervision and output review GitHub. In discovery, the same care applies before code: the team must review AI output before it becomes a product commitment.

A requirement should only advance when the team can answer two things: what was validated and what remains assumed.

This distinction avoids a common trap. If everything becomes "validated," no one knows where the risk is. If everything remains "open," nothing moves forward. A good flow makes the difference explicit.

Fictional Example: Internal Ticket Triage

Imagine a team responsible for improving internal ticket triage in a company. The example is fictional.

The team gathers old tickets, user comments, and notes from a conversation with operations people. The material points to triage delays, rework, and difficulty knowing what to do with certain requests.

Analyzing this set, AI groups reports and suggests an interpretation: "the main problem is lack of automatic prioritization of tickets." The suggestion seems plausible. Several comments mention urgency, delay, and accumulated queue.

If the team converts this synthesis directly into a requirement, they might write something like: "the system must prioritize tickets automatically." It is a clear sentence but still risky.

Returning to users, the team discovers another possibility: many tickets are delayed because they arrive without mandatory information. The analyst needs to ask back, wait for a response, and only then classify the request. Priority is not the first bottleneck. The earlier problem is input quality.

In this fictional scenario, AI did not "err" alone. It made a possible reading of the received material. The team would be wrong if it treated the reading as confirmed truth.

From validation, smaller hypotheses arise:

If the form requires minimum information before submission, it might reduce the number of tickets returned for completion.
If the user sees filling examples by request type, they might describe the request better.
If the analyst can quickly mark "insufficient information," the team might identify which fields cause the most doubt.

None of these hypotheses should be presented as an achieved result. They are proposals to measure. The next decision is to choose which has the lowest risk, clearest validation, and best relation to the observed pain.

Minimum Criteria for AI Output to Become a Requirement

This checklist is a practical criterion to prevent a well-written response from replacing real validation.

Traceable Origin

Did the statement come from an interview, ticket, metric, usage observation, or identifiable business rule?

If the origin is unclear, treat it as a hypothesis, not a requirement.

Affected User or Role

Is it clear who suffers the problem or performs the task?

If the user is generic, return to discovery. "End user" is rarely enough to decide software behavior.

Problem Before Solution

Does the phrase describe a pain or already jump to a feature?

If AI output already comes as a solution, ask for alternative problem formulations and validate with users.

Validation Condition

Is there a question, test, observation, or conversation that can confirm or deny the hypothesis?

If there is no way to validate, do not convert into specification.

Initial Acceptance Criterion

Can the team say what observable behavior would indicate the requirement was met?

If acceptance depends only on subjective interpretation, refine before developing.

Reviewable Size

Can the change be divided into a small, independent, and testable unit?

If the change is too large to review early, divide discovery into smaller hypotheses. This criterion aligns with DORA’s recommendation on small, independent, and testable batches DORA.

Blocks Before Specification

AI-assisted discovery needs an explicit brake. Without it, the team may confuse writing speed with understanding progress.

Do not advance to specification when:

The requirement lacks an identified user, role, or process owner.
The statement’s origin is not traceable.
AI output mixes evidence, interpretation, and decision without separation.
The team cannot formulate a validation question.
Flow exceptions have not been investigated.
The initial acceptance criterion is still too subjective.
The proposed solution has too high an impact to arise only from assisted analysis.
The change combines different decisions in a large, hard-to-review block.

This brake does not exist to slow the process. It exists to protect the team from a new form of haste: well-written but poorly understood requirements.

The issue is not to use AI at every possible point. It is to choose where it improves decision quality without erasing human responsibility.

The operational limit is this: use AI to structure evidence and requirement hypotheses, but block passage to specification when there is no clear origin, validation criterion, and confirmation question with users or process owners. This is the difference between accelerating requirement writing and improving the decision that justifies developing it.

If you want to discuss this decision in your company’s context, talk to dooop.

Sources

DORA: small batches. <https://dora.dev/capabilities/working-in-small-batches/>
Anthropic: context engineering. <https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents>
GitHub: responsible use of Copilot agents. <https://docs.github.com/en/copilot/responsible-use/agents>

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