95% of AI pilots fail. The Massachusetts Institute of Technology (MIT) released a study that said 95% of pilots fail. That statistic stops most distribution executives cold. You’re considering testing AI in your operation, you see this number, and suddenly the whole idea feels risky. Why invest time and resources if the odds are stacked against you?

Here’s what that number means: experimentation has become cheap enough that failure costs almost nothing.

The Economics of Testing Have Changed

20 years ago, pilot programs required significant investment. You needed buy-in from multiple departments, dedicated resources, months of testing. Each experiment consumed substantial resources, which meant pilots were rare.

At Grainger, I watched the company test automated lockers for will-call pickup. The concept made sense. Customers could grab orders 24/7 without waiting for counter staff. The pilot consumed resources, required custom integration, involved facility modifications. It didn’t work. The technology wasn’t ready, customer adoption stayed low, operational complexity exceeded the benefit.

Years later, Amazon figured out the same concept and made it commonplace. They succeeded where we failed, but they also had something we didn’t: the ability to test cheaply and chang rapidly. Their failure cost was low enough that they could afford to keep experimenting until they got it right.

That’s the fundamental shift with AI. Testing no longer requires the kind of investment that made traditional pilot processes so selective.

What Cheap Experimentation Actually Means

AI pilots cost a fraction of what technology testing used to require. You can test a specific use case with a small team in 30 days. No major capital investment. No extensive system integration. No facility modifications.

When testing is this inexpensive, discovering what doesn’t work provides as much value as finding what does. Smart companies run multiple small experiments precisely because individual pilots cost so little. The 95% failure rate exists because businesses can afford to test rapidly and eliminate poor approaches quickly.

Those failures let you redirect resources to the 5% of pilots that deliver results. You learn what doesn’t work without betting significant capital or political capital on a single approach.

What Separates Success from Failure

The successful 5% of AI pilots get two things right.

First, they target specific solutions. Vague objectives fail. “Improve customer service” fails. “Reduce quote response time for standard product configurations by 50%” succeeds. The winning pilots focus on one clearly defined task rather than treating AI as a universal solution.

Second, they solve behavioral changes. Technology must help people do their jobs better. Tools that force staff to adapt to poorly designed systems fail regardless of the underlying AI capability. Success hinges on human adoption.

Distribution Strategy Group research emphasizes this point repeatedly: the people factor remains the single biggest determinant of whether AI projects deliver value. You can have the most sophisticated AI system available, but if your team won’t use it or use it incorrectly, the pilot fails.

This hasn’t changed since the old pilot days. The locker system at Grainger failed partly because customer behavior didn’t match our assumptions. The technology worked mechanically, but we couldn’t drive adoption. AI faces the same challenge.

How to Structure Your First Pilot

Pick one specific problem. Not “customer service needs help” but “customers ask the same five order status questions 200 times per day.” Not “inventory management could improve” but “staff spend 45 minutes daily searching for misplaced items in the warehouse.”

Choose a problem your team complains about regularly. The repetitive tasks that frustrate people make the best pilot candidates. Your staff will engage more readily when testing addresses something that genuinely bothers them.

Set a 30-day timeline. If you can’t see measurable improvement in 30 days, the pilot isn’t working. Kill it and move to the next test. Distribution Strategy Group research shows that successful implementations demonstrate clear value quickly. Projects that need extensive explanation or complicated metrics to show progress usually fail.

This timeline represents another massive shift from traditional pilot processes. Companies couldn’t afford to run 30-day tests and kill them when each pilot required significant investment and longer evaluation periods to justify the resources. AI testing works differently because the cost structure allows rapid iteration.

Start with a small team. Three to five people who handle the specific task you’re testing. Get their input on how the tool should work. Design around their actual workflow, not around how you think the workflow should operate.

Measure one clear metric. Time saved per transaction. Error reduction percentage. Customer inquiries handled per hour. Pick the single number that matters most and track it daily.

What Success Actually Looks Like

A leading electrical distributor tested AI for quote processing on standard product configurations. They started with three inside sales representatives handling routine quotes for common items. The pilot ran for 30 days.

Results showed 90% faster quote processing times. More importantly, the sales team wanted to expand the system. They saw immediate value because the AI eliminated work, they found it tedious while leaving them to handle the complex quotes that required expertise.

The company didn’t try to revolutionize their entire sales process. They didn’t attempt to automate relationship management or strategic account development. They picked one specific, repetitive task and tested whether AI could handle it better than humans. It could, so they expanded.

That’s what successful pilots look like. Small scope. Clear metrics. Fast timeline. Measurable improvement.

Why Most Pilots Fail

Pilots fail when objectives stay fuzzy. “See what AI can do for customer service” produces no actionable insights. You’ll spend 90 days testing various approaches, generate lots of discussion, and end up with no clear path forward.

Pilots fail when they take too long. The longer a test runs, the more variables change. Staff turnover happens. Business conditions shift. By the time you evaluate results, you can’t tell whether the AI worked or external factors influenced outcomes.

Pilots fail when the technology dictates the process instead of enhancing existing workflows. Your team has developed methods that work. AI should make those methods faster or more accurate, not force people to abandon approaches they trust.

The locker pilot at Grainger taught this lesson clearly. The technology was built around what seemed logical from an operational perspective. The design didn’t fully account for how customers wanted to interact with will-call pickup. The disconnect between design assumptions and real behavior killed the pilot.

Pilots fail when success requires elaborate explanation. If you need three slides to show value, the value isn’t there. Good pilots produce results simple enough that frontline staff can describe them in one sentence.

The Real Risk

The risk isn’t that your pilot might fail. The risk is that you don’t run enough pilots.

Distribution Strategy Group research shows 93% of distributors expect increased AI usage in the next year, but only 16% have moved beyond exploration to actual implementation. The gap between companies testing now and companies still planning widens every month.

Your competitors see the same statistics. They face the same challenges. The difference between leaders and laggards comes down to who starts experimenting versus who keeps analyzing.

When Grainger’s locker pilot failed, the company couldn’t immediately run another test on a different approach. The investment was too high, the organizational commitment too significant. There had to be time to wait, regroup, justify the next experiment. Amazon didn’t have that constraint. They could test, fail, adjust, and test again rapidly.

You now have the same advantage. The economic model for AI experimentation allows the kind of rapid iteration that used to be available only to the largest tech companies.

Your Next Step

You don’t need a comprehensive AI strategy. You don’t need board approval. You don’t need six months of planning.

You need one specific problem, one small team, and 30 days.

Pick the repetitive task that frustrates your staff most. Test whether AI can reduce time or errors. Measure results. Kill the pilot if it doesn’t show clear improvement. Start the next test.

Expect most pilots to fail. Plan for it. Budget for it. The cheap failures teach you what doesn’t work so you can focus resources on the approaches that deliver actual value.

The companies that figure this out first won’t just have better AI implementations. They’ll have organizational muscle memory for rapid experimentation that compounds over time. That capability matters more than any single pilot’s success.

Register for the Applied AI for Distribution Conference at https://appliedaifordistributors.com/. Chicago, June 23-25, 2026. Learn from distribution executives who’ve moved beyond planning to actual testing.​​​​​​​​​​​​​​​​