You've probably launched an AI pilot by now. Maybe it showed promise in the demo. Maybe it even worked for a few weeks.

And then what? Did it scale? Did anyone outside the pilot team actually adopt it?

Most AI initiatives die in pilot purgatory. Not because the technology failed, but because they started with the wrong question. Companies ask "What can AI do?" when they should be asking "What problems are actually killing us?"

The Problem-First Approach

The highest ROI from AI doesn't come from implementing the coolest technology. It comes from fixing your most expensive problems.

So start there. Where are your actual bottlenecks?

Look for places where work is:

• Manual and repetitive (like triaging customer support tickets or routing invoice approvals)

• Data-heavy but done by gut feel (like sales forecasting)

• Slow and inconsistent even though the stakes are high (like predicting which customers will churn or identifying upsell opportunities)

Here's a real example: A B2B SaaS company had a sales forecasting problem. Their pipeline predictions were consistently off, which meant misallocated resources and missed targets. They built an AI model using their CRM data, pipeline history, and customer behavior patterns. Pipeline accuracy improved by more than 30%, and suddenly sales leaders could tell the difference between real opportunities and wishful thinking.

That's what happens when you solve an actual problem instead of shopping for technology.

Map What's Actually Happening

You can't fix what you can't see. Pull together people from different functions and diagram your key processes. Not the theoretical flowcharts from your documentation, the real ones, with all the workarounds and bottlenecks.

Ask uncomfortable questions:

• Where do things get stuck between teams?

• What tasks eat up time without creating real value?

• Where is there too much data for anyone to make sense of it manually?

The goal isn't to find AI opportunities. It's to make your hidden inefficiencies visible. The AI opportunities will become obvious once you do that.

Pick Your Battles

Not every problem is worth solving first. Prioritize based on two factors:

Impact: If you fix this, what actually changes? Will it cut costs? Generate revenue? Improve customer experience in a way that matters?

Feasibility: Do you have the data? Is the process clear enough to test quickly? Can you run a pilot without restructuring half the company?

The sweet spot is problems that matter and that you can tackle fast. Common examples:

• Automatically routing support tickets to the right team

• Building churn prediction models that trigger your retention team

• Personalizing onboarding based on customer type

Deloitte found that AI-driven sales forecasting doesn't just improve accuracy, it can cut forecasting cycles by more than 25%. That's time your team gets back to actually sell.

Build a Case That Gets Funded

Estimate what fixing this problem is worth:

• What do you save in time or cost?

• What revenue upside do you unlock? (Faster deals, better retention, higher conversion)

• What's the strategic value? (Customer trust, competitive advantage, speed to market)

Focus on pilots that can show results within a quarter. You need momentum, not perfection.

Run the Pilot Like You Mean It

Pick one clear success metric. Launch small. Track what matters, forecast accuracy, time to close, resolution time, whatever you said you'd improve.

When it works, document what you learned and share it. When it doesn't work, figure out why before you scale.

Gartner's advice here is straightforward: treat successful pilots as templates. If it worked for one team, you've got a blueprint for rolling it out to others.

Make This a Habit, Not a Project

Your business changes. AI capabilities change. What's a bottleneck today might not be next quarter, and new problems will surface.

Review your workflows regularly. Encourage every department, not just IT, to flag where they're stuck or where they're drowning in manual work. Those friction points are your roadmap.

What Actually Works

High-impact AI isn't about chasing trends. It's about knowing where your business hurts and fixing those problems first.

Map your workflows. Find the pain. Target quick wins in areas like sales forecasting, support routing, or onboarding. Pilot with a real metric. Measure the results. Scale what works. Then do it again.

That's how you turn AI from something you talk about in strategy meetings into something that actually moves the business forward.