For years, the default startup advice was simple: build the MVP, ship it fast, and let the market teach you what matters.

That made sense when building software was the hard part. If you could get something into users’ hands quickly, you learned a lot.

AI changes that.

Now it is easier than ever to spin up a prototype, generate an interface, or stitch together a product demo in days. The bottleneck is no longer just building. It is knowing whether the workflow underneath the idea is actually worth turning into software.

That is why founders need a new validation habit: before you build the AI product, test the workflow.

What we’ll explore today

— Naseema Perveen

We’ve released a smart news platform that scores articles, research, and opinions in real time with relevance to your interests. You can get an overview, score rating, and a link to the full story with your interests and preferences at the centre of what you see.

Stop searching endless articles to find what you need. Let our smart news deliver to you automatically the stories you need to see for your career and to get more of your time back.

Sign up for a completely free account today!

Intrepid's co-founder and CEO don't do corporate gloss. Their opening letter in the Integrated Annual Report gets into what 2025 actually required: the hard calls, the strategy reset, and how a nearly 30% growth year still came with real challenges.

Read their story

AI investment is accelerating. But maturity is not.

That is the real signal hiding underneath the current wave of enthusiasm. McKinsey found that almost all companies are investing in AI, yet only 1% say they have reached maturity. In other words, the bottleneck is no longer access. It is application. Most companies are not struggling to find AI tools. They are struggling to turn those tools into workflows that actually work inside the business.

And the market is already moving past the experimentation phase. Microsoft’s 2025 Work Trend Index, based on 31,000 workers across 31 markets, found that 82% of leaders say this is a pivotal year to rethink strategy and operations. That matters because it suggests the next wave of AI advantage will not come from adding more AI features on top. It will come from redesigning how work moves underneath.

The workforce data points in the same direction. The World Economic Forum’s Future of Jobs Report 2025, based on input from more than 1,000 employers representing over 14 million workers, found that 50% of employers plan to reorient their business in response to AI, while 77% plan to upskill their workforce. That is not the language of side-tool adoption. It is the language of operating-model change.

That is exactly why workflow testing matters. Both Anthropic and OpenAI are pointing builders toward the same conclusion: early AI systems work best when they are narrow, structured, and composable. Not overly broad. Not unnecessarily complex. The strongest early products are often the ones built around a real workflow, a clear decision point, and a system people can actually trust.

The classic startup motion was straightforward.

Find a pain point. Build a lightweight version of the product. Put it in front of users. Watch what they do. Improve from there.

That playbook worked because software creation was expensive enough that even a simple product taught you a lot. If users signed up, returned, and tolerated the rough edges, you had signal. If they ignored it, you moved on.

But AI changes the economics of that process.

Now you can generate interfaces quickly. You can prototype agents, copilots, assistants, and workflow layers much faster than before. Which sounds like progress, and it is. But it also creates a new trap: founders can now build the wrong thing much faster.

That is the real shift.

The question used to be: Can we build this product?

Now the better question is: Should this workflow become a product at all?

Those are not the same question.

An MVP mostly tests whether users will interact with a product surface. A workflow test tells you whether the repeated job underneath that surface is real, frequent, painful, structured enough for AI to help, and valuable enough to justify software.

That is a much better place to start.

The idea is simple.

Instead of spending weeks or months building an AI product, you spend a small budget running the workflow manually, semi-manually, or with lightweight tools. The point is not polish. The point is learning.

You are trying to answer a different set of questions:

Does this task happen often enough to matter?
Is the pain sharp enough that people want relief?
What context is required to make the output useful?
Where does AI help?
Where does it fail?
What part still needs a human?
Which step creates the most value?
Would anyone trust this enough to use it repeatedly?

That is the test.

And the cost does not need to be high. In many cases, you can do this with prompts, spreadsheets, forms, templates, no-code tools, basic automations, and human review. You are not trying to impress anyone. You are trying to reduce uncertainty.

This is what makes the idea so powerful.

A workflow test lets you validate the job before you validate the product.

The mistake many founders make is assuming the visible feature is the opportunity.

They see a summarizer and think the opportunity is summary generation.
They see a copilot and think the opportunity is conversational UI.
They see an assistant and think the opportunity is smart responses.

But the real value often lives somewhere deeper.

It lives in the workflow.

What triggers the work?
What information needs to be pulled in?
What judgment needs to happen?
What action follows?
Where does trust break?
Where do edge cases appear?
When does a human still need to step in?

These are workflow questions, not feature questions.

And they matter because AI products usually fail for workflow reasons, not interface reasons.

The output is wrong because context is missing.
The recommendation is ignored because users do not trust it.
The agent breaks because the workflow is less structured than expected.
The system creates more work because review and escalation were never designed properly.

You do not need a finished product to learn any of that.

A well-run workflow test can reveal it in days.

If you run this test, do not just ask whether the output looks good. That is too shallow. A useful test should measure five things.

Does this workflow happen often enough to matter?

A clever solution for a rare workflow is usually not a business. You want repeated work. Weekly is a good starting point. Daily is even better.

The more frequently the workflow occurs, the more chances you have to improve it, learn from it, and justify product investment later.

Is the workflow painful enough that people will feel the improvement?

This is critical. AI does not need to improve everything. It needs to improve something people already dislike.

Look for work that is:
slow
inconsistent
mentally draining
full of handoffs
dependent on messy context
easy to delay
annoying enough that people complain about it

Good workflow ideas usually hide inside recurring irritation.

Is there enough pattern here for AI to actually help?

Some workflows feel painful but are still too open-ended early on. Others look messy but actually contain clear repeated patterns once you break them down.

That is what you are looking for.

A strong candidate usually has:
predictable inputs
repeatable decision points
recognizable output formats
some stable rules or thresholds
a bounded outcome

The more structure you find, the easier it becomes to build useful assistance around it.

Will users rely on the output, or quietly redo the work themselves?

This is one of the most underrated parts of AI validation.

A workflow can look good in a demo and still fail in practice because users do not trust the result enough to act on it. They read the output, then rewrite it. They review the recommendation, then make the call themselves. They ask for the summary, then go back to the original source anyway.

That is not product-market fit. That is theater.

A workflow test helps you see whether the output is genuinely usable or just superficially impressive.

If this workflow improves, what gets better downstream?

Time saved matters. But it is rarely the whole story.

The stronger signals are often:
faster cycles
better decisions
fewer dropped tasks
more consistent quality
cleaner handoffs
higher conversion
lower error rates
fewer escalations
better customer outcomes

You are not just testing whether AI can do something. You are testing whether improving this job creates meaningful business value.

The best first workflow is usually not glamorous.

It is not “replace the whole support team.”
It is not “build a universal research agent.”
It is not “automate the entire sales process.”

It is smaller.

A good first candidate is something your team already does repeatedly and already dislikes.

For example:

inbound lead triage
support request qualification
turning sales calls into next steps
customer feedback clustering
drafting weekly client updates
vendor issue routing
meeting brief preparation
internal research summaries

These are strong starting points because they usually contain a repeatable loop:

Something comes in.
Context gets gathered.
A judgment gets made.
An action follows.
A result gets handed forward.

That is exactly the kind of loop you can test before you turn it into software.

Imagine a founder wants to build an AI product for inbound lead qualification.

At first, the product idea looks obvious: score the lead, summarize the company, recommend next steps, and push everything into the CRM. That sounds like a reasonable MVP.

But before building anything, the founder runs a simple workflow test for one week.

Every time a new lead comes in, they manually collect the same inputs a future product would need: company size, role, use case, source, urgency, and buying signals from the form or email. Then they use AI to classify the lead, draft the suggested next action, and compare that output with what the sales team would actually do.

By day three, the real problem becomes clearer.

The scoring is not the hard part. The real friction is missing context. Some leads look promising on paper but are outside the ideal customer profile. Others seem weak at first, but the wording in the inquiry signals urgency or strong intent. The team also notices that generic next-step suggestions are not very useful unless they match the lead source and deal stage.

That changes the product direction.

Instead of building a broad AI lead-scoring tool, the founder now sees a sharper opportunity: a workflow system that enriches lead context, identifies the likely buying signal, and recommends the right next step based on stage and fit, while still leaving edge cases for human review.

That is exactly what the workflow test is supposed to reveal.

In one week, the founder learns four important things:

That is a much better insight than building a flashy MVP and discovering later that the underlying job was misunderstood.

Before building an AI product, what is the one signal you trust most to tell you a workflow is actually worth turning into software?

We’d love to hear your perspective.

Email your thoughts to: [email protected]
Selected responses will be featured in next week’s edition.

AI automation is opening up a practical new path for people who want to earn online without learning to code. Tasks that once took hours, like lead sorting, content repurposing, follow-ups, research, and client reporting, can now be streamlined into repeatable workflows using AI tools and no-code platforms.

That means individuals can build simple service offers around automation and charge businesses for saving them time, reducing manual work, and improving consistency. The real opportunity is not just using AI for yourself, but packaging these workflows into useful solutions other people are willing to pay for.

Here is how I would run the $1,000 Workflow Test over one week.

Choose something real. Not an aspiration. Not a cool demo idea. Not a future platform vision.

You want something that already exists, already consumes team time, and is painful enough that improvement will be obvious.

A simple filter helps here: Would someone feel real pain if this workflow disappeared tomorrow?

If yes, you probably have something worth testing.

Write down the flow in plain language.

What triggers the work?
What inputs come in?
What context is needed?
What decisions are made?
What actions follow?
What does the final output look like?
Where does a human review or approve?
How would you know the workflow improved?

This step matters more than most founders think.

Because once you map the workflow, you often realize the problem is not slow execution. It is broken context, unclear standards, inconsistent decisions, or unnecessary handoffs.

Do not automate confusion.

Now simulate it.

Use prompts, templates, spreadsheets, forms, or lightweight tools to run the workflow manually. Keep the human in the loop. The goal is not to eliminate labor yet. The goal is to learn how the workflow behaves.

This is where you start seeing what the product would actually need to do.

This is the most valuable day.

Notice:
where context is missing
where prompts fail
where humans override the result
where outputs feel weak
where handoffs create friction
where exceptions keep showing up
where trust drops

This is your raw product insight.

Most founders discover the product shape is not what they first imagined. That is good. That is exactly what this test is supposed to reveal.

Tighten the context. Clarify the decision criteria. Narrow the task. Define what the system should do and what it should not do.

You are not trying to make it magical. You are trying to make it reliable.

This is where many strong AI products begin. Not with bigger models, but with better workflow design.

No polished samples. No idealized examples. No cherry-picked inputs.

Use real work.

Run actual support tickets, real lead forms, live meeting notes, current client updates, real sales call transcripts. That is where the truth shows up.

If the workflow only works on clean examples, it is not ready.

Now ask the real questions:

Did this materially improve the workflow?
Which step created the most value?
Which step still needed human judgment?
What broke most often?
What context turned out to be essential?
Would users repeat this?
Did trust improve or decline?
Is the workflow valuable enough to justify building around it?

That is enough to make a serious decision.

Not a perfect decision. But a much better one than building blindly.

This is where workflow tests get interesting.

They rarely confirm the original idea exactly as imagined.

Instead, they usually reveal one of five things.

Founders often assume they need a full product when they really need a narrow workflow tool, internal layer, or assisted decision engine.

The pain may not sit across the whole workflow. It may sit in one specific part, like context gathering, classification, or drafting.

That is useful. The best product is not always the broadest one.

Sometimes the biggest lesson is that the team itself has not standardized the process enough. Rules vary. Inputs vary. decisions vary. Success is undefined.

In that case, productizing too early creates noise.

The workflow may technically work, but if users still feel the need to redo or review everything, the real work is not output generation. It is confidence design.

Some workflows turn out to be far more universal than they first seemed. Others are too narrow to support a business. Both are good outcomes to learn early.

That is why this method is so useful.

You are not just testing a tool idea. You are testing whether the repeated work underneath it deserves a product at all.

This is really about where validation is moving.

In the old software world, building the MVP was often the fastest route to truth.

In the AI world, the truth often sits one layer lower.

Not in the interface.
In the workflow.
Not in the feature.
In the repeated job.
Not in whether users click.
In whether the system helps real work move forward.

That is why small-budget experiments matter so much now.

They force founders to learn before they polish. They surface context, trust, exceptions, and human review earlier. They make product judgment better.

And in this cycle, better product judgment may matter more than faster execution.

Because faster execution is becoming available to everyone.

If I were building in AI today, I would treat every early idea like this:

Do not ask first, “What should we build?”

Ask:
What repeated workflow are we trying to improve?
What part of it is painful?
What part is structured?
What part is trust-sensitive?
What can be tested manually before software exists?

That shift sounds small. It is not.

It moves you from product-first thinking to workflow-first thinking. And that usually leads to sharper products, better timing, and much less wasted effort.

They will be the ones who learn fastest.

That is an important difference.

For most of the software era, speed to build was a major advantage. If you could ship faster than everyone else, you could get into the market earlier, collect feedback sooner, and improve from there. Building was expensive enough that simply getting something live created leverage.

But AI changes the economics of that loop.

Now the cost of building has dropped so quickly that speed alone is becoming less differentiating. More teams can prototype quickly. More founders can launch assistants, copilots, and workflow layers. More products can look impressive in a week.

Which means the real advantage is starting to move.

Not toward who can generate the most product surface area.
Toward who can identify where the real value sits underneath it.

That is why validation matters more now than it used to.

The strongest founders in this cycle will not confuse motion with insight. They will not assume that because something can be turned into a feature, it should be turned into a company. They will spend less time building speculative interfaces and more time studying the repeated work underneath them: where the friction is real, where the context breaks, where trust matters, where decisions repeat, and where a system could genuinely improve the flow of work.

That is what the $1,000 Workflow Test is really about.

It is not just a way to save money.
It is a way to improve judgment.

It forces founders to learn earlier, while the cost of being wrong is still low. It helps them see whether the opportunity is broad or narrow, whether the real pain sits in the whole workflow or just one step, and whether the product should automate, assist, or simply make better decisions possible.

That kind of clarity is becoming more valuable than raw shipping speed.

Because in the AI era, the expensive mistake is no longer failing to launch.

It is launching too quickly around the wrong workflow.
It is polishing a product surface before understanding the job underneath it.
It is building software for a behavior that is too rare, too messy, too trust-sensitive, or too weak in value to support a real product.

The founders who win from here will be the ones who can tell the difference early.

They will know when a workflow is painful enough to matter.
Structured enough to improve.
Frequent enough to justify productization.
And valuable enough to compound into something bigger.

In other words, they will not just be fast builders.

They will be sharp validators.

And that may end up being one of the defining founder skills of the AI era.

—Naseema

Writer & editor, The AIJ Newsletter

Stay ahead of where AI and technology are actually heading, not just where headlines point:

→ Read more insights on The AI Journal and download our 2026 Media Kit.

→ See all our reports and guides, which you can download for free today.

→ Join Premium for exclusive takes on topics emerging and stories developing in AI.

→ Explore broader tech coverage on Silicon Valley Journal.

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

Join 130k+ AI and Data enthusiasts by subscribing to our LinkedIn page.

Become a sponsor of our next newsletter and connect with industry leaders and innovators.