If you've read my last article on the return of requirements, you already know that for many organizations, detailed requirements slowly faded out over the last decade.

Not completely. But enough that many teams lost the habit of writing with operational precision.

The industry moved toward:

  • lighter documentation,
  • rapid iteration,
  • user stories,
  • collaborative refinement,
  • and working software over comprehensive documentation.

Again, this shift was not wrong.

It solved real problems caused by over engineered requirement processes that created slow delivery cycles and disconnected teams from users.

But now AI is changing the equation.

As organizations increasingly rely on AI assisted delivery, they are rediscovering something uncomfortable: many teams are out of practice when it comes to writing strong requirements.

And the gap is becoming visible very quickly.

The Problem Is Not That Teams Cannot Write

The problem is that many teams stopped needing to write with precision.

For years, ambiguity was absorbed through conversation:

  • developers interpreted intent,
  • product managers clarified during standups,
  • QA filled gaps during testing,
  • analysts connected missing pieces,
  • and institutional knowledge lived inside peoples heads.

Modern AI workflows reduce that safety net dramatically.

AI executes exactly what is communicated and invents the rest.

That means vague thinking becomes visible almost immediately.

You start seeing:

  • incomplete workflows,
  • hallucinated assumptions,
  • inconsistent business rules,
  • edge cases ignored,
  • conflicting logic,
  • and outputs that look right but fail operationally.

The issue is often not the AI.

It is the missing precision upstream.

Yes, Teams Will Use AI to Generate Requirements, And That Is Fine

Let's also be realistic.

Most organizations are absolutely going to use AI to help generate requirements.

And honestly, they should.

AI can dramatically accelerate:

  • first drafts,
  • acceptance criteria,
  • workflow descriptions,
  • edge case brainstorming,
  • data mappings,
  • process documentation,
  • and technical summaries.

Used correctly, this is a massive productivity gain.

The problem is not AI generated requirements.

The problem is unreviewed AI generated requirements.

There is a dangerous tendency emerging where teams assume that because a requirement document is long, structured, and professionally written, it must also be correct.

But AI is exceptionally good at producing documentation that sounds authoritative while quietly introducing:

  • incorrect assumptions,
  • missing business rules,
  • invented logic,
  • contradictory flows,
  • or operational gaps.

This is where I think we are starting to see the emergence of what could be called requirements slop.

Just like AI generated content created the concept of content slop, requirements slop is the accumulation of:

  • bloated AI generated specifications,
  • repetitive acceptance criteria,
  • generic edge cases,
  • meaningless verbosity,
  • contradictory instructions,
  • and documentation nobody fully validates.

On the surface, it looks comprehensive.

Operationally, it is fragile.

And in some cases, worse than having less documentation, because teams assume the detail equals accuracy.

That is what organizations need to avoid.

The answer is not rejecting AI assisted requirement writing.

The answer is disciplined review, refinement, and ownership.

Human Revision Becomes the Critical Step

This is where revision and manual editing become critically important.

The role of analysts, product managers, architects, and operational experts is not disappearing.

In many ways, it is evolving into:

  • validating logic,
  • refining clarity,
  • removing ambiguity,
  • challenging assumptions,
  • stress testing workflows,
  • identifying operational gaps,
  • and cutting unnecessary noise.

Ironically, one of the most valuable skills in the AI era may become the ability to simplify AI generated requirements into something operationally usable.

Not longer.

Better.

Cleaner.

Sharper.

AI can help produce the draft.

Humans still need to own the thinking.

Most Teams Should Not Go Back to 200 Page Requirement Documents

One of the biggest mistakes organizations could make right now is overcorrecting.

This does not mean bringing back massive waterfall specification documents filled with screenshots and pages nobody maintains.

Modern requirements need to be:

  • lightweight enough to evolve,
  • but structured enough to execute reliably.

The goal is operational clarity, not documentation volume.

A good requirement today should answer:

  • What problem are we solving?
  • What should happen?
  • What should not happen?
  • What are the rules?
  • What are the exceptions?
  • What systems are involved?
  • What assumptions exist?
  • What defines success?
  • What constraints matter?
  • What edge cases must be handled?

That is very different from producing documentation for compliance theater.

Start With Operational Thinking, Not Templates

Many organizations immediately look for a new template.

Templates help, but they are not the real issue.

The bigger challenge is rebuilding operational thinking.

Strong requirements come from people asking disciplined questions:

  • What happens if the customer already exists?
  • What if the sync fails?
  • What if permissions conflict?
  • What happens when inventory reaches zero mid checkout?
  • What if a user belongs to multiple business units?
  • What if data arrives incomplete?
  • What happens after retry attempts fail?

AI exposes weak operational thinking brutally fast because it accelerates implementation before teams fully think through the process.

That is why the best requirement writers are often people who deeply understand operations, workflows, customer behavior, and system interactions, not just documentation formatting.

Requirements Should Become More Modular

One positive outcome of AI adoption is that requirements no longer need to exist as giant monolithic documents.

The strongest teams are moving toward modular requirement structures.

For example:

  • Business Context
  • Workflow Logic
  • Rules and Constraints
  • Data Mapping
  • Exception Handling
  • Acceptance Criteria
  • AI Execution Prompting
  • UX Considerations
  • Technical Notes

This structure works far better for:

  • humans,
  • AI tools,
  • future maintenance,
  • and cross functional collaboration.

It also allows teams to update only the sections impacted by change instead of rewriting entire specifications.

Organizations Need to Rebuild This Capability Intentionally

Many senior analysts and product professionals developed strong requirement writing skills earlier in their careers because they had no choice.

You had to think through processes carefully before development began because iteration was slower and more expensive.

Some newer teams grew up entirely inside lightweight agile environments where much of that rigor became distributed across ongoing collaboration instead of written artifacts.

Now the pendulum is shifting again.

Organizations need to intentionally rebuild:

  • structured thinking,
  • operational analysis,
  • systems reasoning,
  • and precision writing.

Not because agility failed.

But because AI drastically increased the speed of execution.

And when execution accelerates, unclear thinking becomes exponentially more expensive.

The Future Is Probably Hybrid

The future likely is not:

  • pure agile storytelling,
  • nor rigid traditional specification culture.

It is a hybrid model.

User stories remain critical for understanding value and customer intent.

Requirements regain importance for execution reliability and AI assisted delivery.

AI generated drafts will become normal.

And honestly, that is not necessarily a bad thing.

The danger is not AI generated requirements.

The danger is organizations mistaking AI generated completeness for operational correctness.

That is where human expertise becomes essential again:

  • validating workflows,
  • identifying missing operational realities,
  • refining ambiguity,
  • removing contradictions,
  • and ensuring the requirements reflect how the business actually functions.

The organizations that adapt fastest will be the ones capable of combining:

  • AI acceleration,
  • strong operational thinking,
  • structured review,
  • and disciplined requirement refinement.

Because in the AI era, the ability to think clearly and specify accurately is becoming a multiplier across the entire organization.