The Best AI Use Case Might Not Be the One Your CEO/Owner/Execs Think It Is

Leadership within a company is usually placed into leadership positions for a purpose, and they definitely should help determine where a company is going.

However, this does not mean leadership can see every problem standing in the way, and unfortunately they miss a lot of things from being where they are.

This distinction becomes particularly important when companies start looking for AI opportunities.

Ask an owner or CEO where AI might help the company, and you will probably get several ideas. Some may be excellent. Leadership understands the company’s strategy, economics, customers, risks, and priorities in ways that individual employees may not.

But leadership sees the business from one vantage point.

The people doing the work see it from another.

If an AI strategy is built entirely from the top down, a business can easily end up solving the problem leadership can see instead of the problem employees experience every day.

Different Levels of the Business See Different Truths

One of the most useful findings in current workplace AI research is that leaders and employees don’t experience AI the same way.

Gallup found that within organizations making AI available to employees, 67% of leaders reported using AI frequently, compared with 46% of individual contributors. Managers and project managers fell between the two groups.

Microsoft’s 2026 Work Trend Index identified another form of organizational disconnect. Among the AI users it surveyed, only 26% said their leadership was clearly and consistently aligned on AI. The research also found cases where employee AI capability was advancing faster than the organizational systems surrounding it.

None of this means leaders are incapable of identifying good AI opportunities.

It means perspective matters.

A CEO experiences the business through strategy, financial performance, management reporting, customer outcomes, and organizational priorities.

A frontline employee experiences the business through tasks, handoffs, systems, exceptions, workarounds, interruptions, and the hundred small decisions required to make the official process actually work.

Both views are absolutely real, and neither view is complete by itself.

The Most Visible Problem Is Not Always the Most Valuable Problem

AI discussions often begin with highly visible opportunities.

“We need an AI chatbot.”

“We should automate customer support.”

“We need AI for sales.”

“We should create an internal AI assistant.”

“We need an agent.”

Those ideas may eventually prove worthwhile.

But naming a technology application is not the same thing as identifying a use case.

A real use case should connect a specific business problem to a specific workflow, group of users, expected outcome, and economic impact.

Consider a hypothetical professional-services company receiving a growing number of customer inquiries.

Leadership might reasonably conclude:

We need an AI chatbot to reduce the number of questions employees answer.

Employee interviews might reveal something different.

• Perhaps customers are contacting the company because information is inconsistent across the website, proposals, onboarding materials, and account communications.
• Perhaps employees themselves spend ten minutes answering a customer question because they have to search three systems for the answer.
• Perhaps the real bottleneck is not answering questions at all. It is retrieving accurate information.

If those are the facts, a chatbot will only mask the symptom while leaving the underlying knowledge problem untouched.

The better investment would be knowledge consolidation, system integration, process redesign, or an internal retrieval system that helps employees find accurate information faster.

AI may still be involved.

But it may be solving an entirely different problem than leadership originally imagined.

Employees See the Work Between the Boxes

Most organizations have an official version of how work gets done.

Then there is the actual version.

The official version might say:

Sales closes opportunity → Operations receives information → Customer is onboarded.

The real version might look more like this:

1. The salesperson closes the opportunity.
2. Someone messages operations.
3. Operations notices three pieces of information are missing.
4. They email sales.
5. Sales asks the customer.
6. The customer sends a PDF.
7. Operations copies information from the PDF into a spreadsheet.
8. Someone else copies part of the spreadsheet into another system.
9. A manager reviews it.
10. An exception appears.
11. The employee who knows how to resolve that exception is out today.
12. Work stops.

None of those small pieces will appear on an executive dashboard.

Together they can represent hundreds of hours of friction every year.

That is why employees are essential during AI discovery.

They often know where work repeatedly slows down, where information goes missing, which systems do not talk to each other, where customers become confused, what employees have built manually to compensate, and which rules make sense on paper but fail in practice.

This kind of operational knowledge is difficult to discover from a leadership meeting alone.

The Goal Is Not to Ask Employees, “What AI Do You Want?”

Employee involvement does not mean asking everyone to brainstorm AI products.

That can create the same technology-first problem from a different direction.

A better interview focuses on the work.

• What takes too long?
• What do you repeatedly copy or re-enter?
• Where do you wait for someone else?
• What information is hardest to find?
• What work do you dread because it is unnecessarily complicated?
• Where do mistakes tend to happen?
• What requires the most judgment?
• What exceptions constantly break the normal process?
• What do customers repeatedly ask for?
• If you could remove one administrative burden from your week, what would it be?

These types of questions identify real day-to-day friction.

Only after the friction is understood should the company decide whether AI belongs in the solution.

Leadership, Managers, and Employees Each Contribute Different Evidence

We believe good AI discovery requires at least three views of the business.

The strategic view

Leadership can answer:

• What matters economically?
• Where is the company trying to go?
• What risks are unacceptable?
• Which capabilities could create competitive advantage?
• Where are customers being won or lost?
• What constraints are preventing growth?

These questions ensure that an AI opportunity matters to the business rather than simply making an annoying task slightly easier.

The operational view

Managers and employees can explain:

• How does the work actually happen?
• Where are the handoffs?
• Where does the documented process differ from reality?
• Which steps depend on institutional knowledge?
• Where are exceptions common?
• Which systems cause friction?
• What happens when something goes wrong?

These questions reveal whether leadership’s understanding of the problem matches operational reality.

The implementation view

Then the company has to evaluate feasibility.

• Does the required data exist?
• Can necessary systems be integrated?
• Is the information trustworthy?
• What level of accuracy is required?
• Where must humans remain involved?
• Who owns the workflow?
• What would adoption require?
• What will implementation and maintenance cost?

A good AI use case sits at the intersection of all three views:

It matters strategically.

It solves real operational friction.

And it can be implemented responsibly and economically.

Do Not Shy Away From Contradictions; They Are Useful

One of the most valuable things that can happen during discovery is disagreement.

• Leadership says a process takes one day.
• Employees say it takes three.
• A manager says a system is working well.
• Employees have built an unofficial spreadsheet because it is not.
• Leadership thinks a task is standardized.
• Every employee performs it differently.
• Employees think one problem is costing the company enormous amounts of time.
• The data shows it happens only twice a month.

None of these contradictions should be treated as someone being dishonest.

These are valuable signals.

They tell us where the organization’s understanding of itself may be incomplete.

This is valuable because AI magnifies assumptions.

If we automate a process based on the wrong assumption, we can make the wrong process faster.

If we build an AI tool around incomplete requirements, we can create something technically impressive that employees avoid using.

If we solve a highly visible problem that has little economic value while ignoring a less visible bottleneck that affects every customer transaction, we have optimized the wrong thing.

Discovery is supposed to find those contradictions before implementation begins.

This Is Also a Risk Management Principle

The idea that AI decisions should include the people affected by the system is not unique to our methodology.

NIST’s AI Risk Management Framework encourages organizations to engage system end users and relevant stakeholders to understand potential benefits, limitations, efficacy, and impacts. It also emphasizes human-centered design and incorporating diverse perspectives throughout AI risk management.

This matters because implementation risk is not limited to cybersecurity, hallucinations, or data privacy.

• There is also the risk that the technology does not fit the work.
• The risk that employees develop workarounds around the new system.
• The risk that managers do not support it.
• The risk that an AI system removes information employees need to make good decisions.
• The risk that a project optimizes one department while creating more work for another.

Those risks often become obvious when you talk to the people doing the work.

The CEO’s Idea Might Still Be the Best One

Sometimes leadership identifies the right opportunity immediately.

The point of broader discovery is not to prove executives wrong.

It is to test the hypothesis.

A CEO might believe AI could significantly reduce the effort required to prepare proposals.

Interviews may confirm that employees spend hundreds of hours assembling the same categories of information, much of the work is repeatable, the required source material is reliable, human review can remain in the workflow, and faster proposal generation would materially improve capacity.

Excellent.

Now the organization has more than an executive idea.

It has evidence.

That distinction matters.

A promising idea becomes much more valuable when it survives contact with the people, processes, systems, data, economics, and risks surrounding it.

Find the Problem Worth Solving

AI strategy should not be an executive brainstorming exercise.

It should be an investigation where,

• Leadership provides direction.
• Managers provide operational context.
• Employees reveal the lived reality of the work.
• Systems and data provide another layer of truth.
• Economics determine whether solving the problem is worth the investment.

And only then should technology enter the decision.

That is the philosophy behind how we approach the Business AI Readiness Assessment at The Scale Crew.

We are not looking for somewhere to put AI.

We are looking for the problem worth solving, the evidence that explains it, and the solution that creates the strongest business case.

Sometimes that solution will be exactly what the CEO expected.

Sometimes the best opportunity will be hiding three levels deeper in the organization.

You do not know until you ask.