How to Prioritize AI Efforts

AI adoption has almost nothing to do with fancy technology. That’s the part most companies get wrong.

The organizations that struggle with AI aren’t lacking platforms, data scientists, or ambition. They’re struggling because they treat AI as a technical initiative when it’s really a people and prioritization problem.

You don’t win with AI by choosing the most impressive use case. You win by choosing the right one first.

Why Most AI Roadmaps Collapse Under Their Own Weight

Many companies start their AI journey by brainstorming everything AI could do.

Ideas pile up quickly:

• Automate this workflow
• Add intelligence to that system
• Build a predictive model over here

Before long, there are dozens of potential initiatives, but no clear way to decide what actually matters.

This is where momentum dies.

Without a prioritization framework grounded in reality, AI becomes a collection of good ideas instead of a sequence of executable wins.

Start With Three Simple Questions

The most effective AI prioritization doesn’t start with models or vendors. It starts with questions.

As Bob Marsh puts it:

1. Where’s the money?
2. Where’s the data?
3. Where will adoption come from?

These questions cut through noise immediately.

If an initiative can’t clearly connect to financial impact, usable data, and willing users, it’s not a starting point—it’s a future consideration at best.

“Where’s the Money?” Forces Real Business Thinking

This isn’t about vague ROI projections or “strategic value.”

It’s about identifying pain you can measure:

• Time wasted
• Revenue delayed
• Errors corrected manually
• Opportunities missed because processes are too slow

If you can’t articulate how a problem affects money, prioritizing it will always be subjective—and fragile.

“Where’s the Data?” Keeps AI Grounded

Many AI ideas sound great until you ask where the data actually lives.

Is it accessible?
Is it reasonably clean?
Does it reflect reality—or workarounds?

You don’t need perfect data to start. But you do need data that exists and can be improved through use. Otherwise, initiatives stall while teams debate architecture instead of shipping solutions.

“Where Will Adoption Come From?” Is the Most Overlooked Question

This is where most AI efforts fail quietly.

Adoption doesn’t come from leadership enthusiasm or mandates. It comes from people believing a solution helps them.

Teams adopt AI when:

• It removes frustration
• It saves time they actually feel
• It solves a problem they already complain about

If the users don’t care, the initiative won’t scale—no matter how elegant the technology is.

Look for Pain Points You Can Measure

Strong early AI candidates share one trait: measurability.

That’s why high-performing organizations look for:

• Manual processes with known cycle times
• Tasks repeated daily or weekly
• Errors that require rework
• Bottlenecks everyone recognizes

Clear measurement allows teams to prove value quickly—and proof builds trust.

As Dee-Dee Boykin has shared, focusing on clear business problems and aiming for measurable 90-day wins creates early results that build momentum instead of skepticism.

Build From the Bottom Up, Not the Boardroom Down

Another common mistake is prioritizing AI top-down.

Leadership defines a transformation goal, then asks teams to support it. That often produces resistance—or silence.

Teams on the ground already know where the friction is. When those insights feed into strategy, something powerful happens: small projects turn into bigger business cases that share data, infrastructure, and momentum.

Bottom-up doesn’t mean uncoordinated. It means informed.

Why People Matter More Than Technology

AI adoption accelerates when people feel involved—not replaced.

Change happens when teams:

• See the value
• Understand what’s being built
• Help shape the solution

That’s how AI moves from an experiment to part of how the business actually runs.

When people work with AI instead of having it imposed on them, prioritization becomes easier. The best ideas surface naturally.

From Prioritization to Momentum

Effective AI prioritization isn’t about choosing the “best” idea.

It’s about choosing the idea that:

• Solves a real, visible problem
• Has data you can work with
• Has users who want it to succeed

Those early wins create credibility. Credibility creates adoption. Adoption creates room for bigger, more transformative initiatives.

At OntracAI, this people-first, problem-driven approach is how we help organizations move from scattered ideas to focused execution.

Explore AI solutions designed to drive adoption and measurable outcomes.

Why Adoption Gaps Stall ROI

Even well-prioritized AI initiatives can stall if adoption gaps aren’t addressed deliberately.

When people don’t trust, understand, or see themselves in the solution, ROI never materializes, no matter how strong the use case looked on paper.

This dynamic is explored further in Ajay’s piece on why adoption gaps quietly derail automation ROI, and how organizations can close them before scaling.

The Bottom Line

Prioritizing AI efforts isn’t a technical exercise. It’s a human one.

Start with simple questions. Look for measurable pain. Listen to the people doing the work.

When you do, AI stops feeling overwhelming—and starts feeling obvious. Because the right AI initiative doesn’t need convincing. It solves a problem people already want fixed.