Make AI Requirements Bigger Before You Make Them Real

Many AI projects start with requirements that are already too small.

A team asks for a chatbot because chatbots are familiar. A manager asks for an automated report because the current report is painful. A department asks for a document assistant because everyone has heard that retrieval-augmented generation can search internal knowledge. A product team asks for an agent because the market is full of agent demos.

Those requests are not wrong. They may be the right solution eventually. But if the first requirement is already shaped like a tool, the team may never discover the larger problem underneath it.

This is one of the quiet risks in modern AI work. Generative AI has made prototypes easier to build, so teams can move from idea to demo before they have understood the workflow. The screen fills with something impressive, the model gives fluent answers, and everyone feels closer to completion than they really are. Then the project reaches real users and the hidden requirements appear: permissions, exception handling, stale data, audit needs, latency, cost, approvals, escalation, and evaluation.

The better habit is to make the requirements bigger before making them real.

I do not mean adding scope for its own sake. I mean giving stakeholders enough room to describe the outcome they actually want before the team collapses the conversation into screens, fields, prompts, models, vendors, and tickets. Good discovery asks people to step outside the current workflow long enough to imagine a better one. Good engineering then brings that ambition back into constraints.

Both parts matter. Ambition without engineering becomes fantasy. Engineering without ambition becomes a polished version of the old problem.

The first request is often a disguised workaround

When users describe what they need, they usually describe it through the system they already know.

That is normal. People spend years adapting to existing tools, approval paths, spreadsheets, dashboards, ticket queues, and informal workarounds. By the time a project team arrives to gather requirements, the people closest to the work may have learned how to survive the process so well that their improvement ideas are limited by it.

In an AI project, this can sound like:

• “Can the assistant summarize these emails?”
• “Can we add a button that writes the status update?”
• “Can the dashboard explain the metric changes?”
• “Can the agent fill out this form for us?”
• “Can we search all policy documents through chat?”

Each request may be useful. But each one may also be a symptom.

The team should ask what job the request is doing. Is the email summary needed because customers write long messages, or because the CRM does not show the previous interaction clearly? Is the status update painful because writing is hard, or because six different stakeholders want six different formats? Is document search needed because people cannot find policies, or because policies contradict each other? Is the form painful because typing is slow, or because the underlying process asks for information nobody trusts?

AI can automate parts of a broken process. It can also hide the fact that the process is broken.

That is why requirements should begin with the work, not the model.

Big questions expose the real workflow

The useful discovery question is not, “Which AI feature should we build?”

It is closer to: “If this work became dramatically easier, what would be different?”

That question changes the conversation. It gives stakeholders permission to stop editing the current interface and start describing the outcome. They may still ask for automation, but they are more likely to reveal the real bottleneck: waiting for approvals, copying data between systems, reconciling conflicting definitions, searching for context, checking compliance, rewriting the same explanation, or deciding which exception needs human judgment.

For technical teams, this matters because AI is not a single capability. The same business pain can point to very different systems.

A support team that wants faster answers may need a better knowledge base before it needs a model. It may need retrieval with citations, a feedback loop for stale answers, and an escalation path for high-risk cases. A finance team that wants automated commentary may need metric definitions, variance thresholds, and approval rules before it needs text generation. A data team that wants a natural-language analytics assistant may need semantic layers, query safety, and permission-aware SQL before it needs an agent.

The bigger question helps the team avoid premature solution design.

It also makes current AI trends more useful. Tool calling, structured outputs, RAG, multimodal models, model routing, and agents are powerful only when connected to a clear workflow. Without that clarity, they become expensive vocabulary. The team can say “agentic workflow” while still automating the wrong step.

Current AI adoption makes this more urgent

The market context matters because many organizations are now past the stage of simple curiosity.

McKinsey’s 2025 State of AI survey reported that 88 percent of respondents said their organizations regularly used AI in at least one business function, but only about one-third said their companies had begun scaling AI programs. The same report found that workflow redesign is one of the strongest differences between high performers and other organizations.

That is the important lesson for requirements work. Broad usage does not automatically create business value. A company can have many pilots, many licenses, many internal demos, and still not redesign the work enough to matter.

AI agents show the same pattern. McKinsey reported that 23 percent of respondents were scaling agentic AI somewhere in the enterprise, while another 39 percent were experimenting. LangChain’s 2026 State of Agent Engineering found that teams moving agents toward production rely heavily on observability and evaluation because final outputs alone are not enough to understand failures.

This should change how teams gather requirements. For a normal software feature, a requirement may describe a screen, a role, a field, a workflow step, or a report. For an AI system, the requirement also needs to describe behavior under uncertainty.

What should the system do when retrieval finds weak evidence? When the user asks an ambiguous question? When the model returns invalid JSON? When a tool fails? When a prompt change improves one use case and breaks another? When the answer is probably right but the business risk is too high to act automatically?

These are not implementation details to postpone indefinitely. They are part of the product.

Ambition needs a reality boundary

There is a trap here. If a team asks people to imagine a much better workflow, it can accidentally create a promise.

That is especially dangerous with AI because many people already see the technology through a fog of exaggerated claims. They hear that models can write code, analyze documents, call tools, understand images, generate reports, and operate software. Some of that is true in narrow contexts. Much of it becomes unreliable when the data is messy, the task is ambiguous, the stakes are high, or the workflow depends on permissions and judgment.

So the discovery process needs two distinct modes.

First, open the space. Ask what would change if the painful parts of the process were no longer painful. Ask what decisions would become faster, what errors would disappear, what handoffs would go away, what information would be available at the right moment, and what users would stop doing manually.

Then close the space. Identify which parts are feasible now, which require better data, which require policy decisions, which need human review, which are too risky to automate, and which should remain ordinary deterministic software.

The second mode is not pessimism. It is professional honesty.

NIST’s AI Risk Management Framework describes AI risk management as something that belongs in the design, development, use, and evaluation of AI systems. That framing is useful because it prevents teams from treating trust, safety, and accountability as decorations added after a demo. They belong in the requirements conversation.

McKinsey’s survey also noted that high-performing organizations are more likely to define when model outputs need human validation. That is not a minor process detail. It is a requirement about authority. The system may draft, retrieve, classify, rank, or recommend, but someone has to decide where automation ends and responsibility begins.

A practical way to run AI discovery

A good requirements conversation for AI should move through four passes.

Start with the outcome. Ask what meaningful improvement would look like if the team did not have to preserve the current process. Do not start with model names or frameworks. Start with time saved, errors reduced, decisions improved, handoffs removed, risk lowered, or quality made more consistent.

Map the real work. Once the outcome is clear, describe how the work happens today. Include the unofficial steps. Where do people copy data? Where do they wait? Where do they ask another person for context? Where do they distrust the system and check manually? Where do exceptions appear?

Separate capability from control. Decide which parts could be handled by AI and which parts need normal software, rules, approvals, or human judgment. A model may extract fields from a contract, but business rules may decide whether the contract needs legal review. An agent may gather information from several systems, but deterministic code may enforce permissions and logging.

Define evidence before launch. Decide how the team will know whether the system works. For a RAG assistant, that may include retrieval tests, answer quality checks, citation accuracy, and unsupported-answer refusal. For an agent, it may include task completion rate, tool-call traces, step limits, cost per task, failure categories, and human review outcomes. For a structured-output workflow, it may include schema validation, regression tests, and exception queues.

This turns discovery into an engineering input, not a workshop artifact.

It also helps teams build smaller first versions without losing the bigger vision. The first release may not automate the whole workflow. It may only handle one document type, one queue, one internal team, or one low-risk decision. That is fine. The value of big requirements is not that everything must be built immediately. The value is that the first version points in the right direction.

The requirements document should include uncertainty

Traditional requirements often sound more certain than the project really is.

AI requirements should be more honest. They should name what the team knows, what it assumes, what must be tested, and what cannot be promised yet.

A useful AI requirements brief might include:

• The business outcome the system is meant to improve
• The workflow steps that should change if the system succeeds
• The data sources, owners, freshness expectations, and access rules
• The model tasks, such as classification, extraction, summarization, retrieval, routing, or tool use
• The parts that must remain deterministic software
• The output format and validation rules
• The evaluation set and the failure types it must cover
• The human review points and escalation rules
• The cost, latency, privacy, and security constraints
• The launch boundary: who uses it first, for what, and under which limits

This is not bureaucracy. It is how a team protects itself from a demo that looks finished while the hard parts are still undefined.

It is also a better way to communicate with nontechnical stakeholders. Instead of saying, “AI is unreliable,” the team can say, “Here are the cases where automation is acceptable, here are the cases where review is required, and here is the evidence we need before expanding use.” That is a much more useful conversation.

I made a related point in AI Project Planning Without Panic or Rework: many project surprises are really visible risks that were not named early enough. Requirements are one of the best places to name them.

The best AI requirement may be a non-AI change

One uncomfortable outcome of good discovery is that the best answer may not be an AI feature.

Sometimes the most valuable improvement is a cleaner knowledge base, a better approval rule, a simpler form, a shared metric definition, a well-designed API, a permissions cleanup, or a dashboard that answers the real question directly. AI may still help later, but it should not be used to compensate for avoidable confusion.

This is not anti-AI. It is how strong AI work becomes possible.

If documents are outdated, a RAG system will retrieve outdated documents faster. If no one owns the metric definition, a natural-language analytics assistant will make disagreement easier to access. If permissions are unclear, an agent can turn a governance problem into a security problem. If the workflow has no exception path, automation will push edge cases into private messages and manual cleanup.

Good requirements work should expose these foundations before the model is blamed for everything.

There is also a career lesson here for data, AI, and software professionals. The valuable person on an AI project is not only the person who knows the newest framework. It is the person who can ask what the business is trying to change, identify where AI fits, explain where it does not, and turn uncertainty into testable requirements. That skill ages better than any one tool.

Leaders should protect both imagination and discipline

Leaders have a specific role in this process.

If they ask only for quick AI wins, teams will produce quick AI-looking artifacts. If they punish uncertainty, teams will hide it. If they reward demos without asking about evaluation, they will get more demos. If they announce automation before the workflow is understood, requirements work becomes political theater.

The better leadership behavior is to create room for ambitious discovery while making it clear that no imagined capability becomes a commitment until the team has tested feasibility, risk, and value.

That means asking questions such as:

• What would the workflow look like if we redesigned it around the outcome?
• Which part of this idea is genuinely new, and which part is just the old process with a model attached?
• What data, access, policy, or operating constraint could block this?
• Where do we need human judgment?
• How will we know whether the system improved the work?
• What should we refuse to automate?

Those questions keep the team from shrinking the idea too early or overselling it too soon.

AI product work needs both movements. First, ask for a bigger version of the outcome than the current process suggests. Then translate that ambition into architecture, data work, tests, review paths, deployment boundaries, and operating discipline.

The first movement prevents small thinking. The second prevents irresponsible promises.

Build the possible version of the better idea

The point of ambitious requirements is not to pretend constraints do not exist. The point is to discover what the constraints are constraining.

If a team only asks users what screen they want changed, it may build a better screen for a process that should have been redesigned. If a team only asks which AI tool to buy, it may automate a workaround instead of solving the workflow problem. If a team only asks what can be delivered this quarter, it may never learn what would actually matter.

But once the bigger idea is visible, the team has to become very practical.

What can be built first? What should stay manual? What needs evaluation? What data has to be fixed? What risks are unacceptable? What evidence would justify expanding the system? What should be logged, reviewed, measured, and improved?

That is the mature version of AI requirements work. It invites people to imagine a better way to work, then refuses to confuse imagination with delivery.

AI is useful when it helps teams redesign real workflows, not when it decorates old ones. The requirements process should reflect that. Make the idea bigger first. Then make the first version smaller, testable, and honest enough to survive contact with real work.