According to the Massachusetts Institute of Technology (MIT), 95% of generative artificial intelligence (AI) pilots at companies fail to deliver revenue impact (https://yhoo.it/4nHjebz). 

It’s like spending $5,000 on top-of-the-line Titleist drivers and irons and then wondering why you’re still shooting 95. The problem isn’t the clubs; it’s your swing.

If you’re a distribution executive dealing with margin pressure, supply chain challenges and digital transformation, you probably take this MIT statistic to further support your belief that AI is just another overhyped technology. 

However, the missing piece of the puzzle is that the 5% who do succeed with AI aren’t getting incremental gains; they are changing the game entirely. So really, the only question is: How are they doing it?

We recently worked with a PHCP distributor to implement an AI-powered quoting system, freeing up 70% of its administrative time. That’s hundreds of hours per week now spent building relationships with customers, rather than doing manual quote inputs. However, the company didn’t only buy new clubs; it rebuilt its entire swing.

Stop shopping for another set of clubs: Fix your swing

The 95% who fail AI see it as a trip to the pro shop. They will demo the latest technology, buy the most expensive option, perhaps even invest in a fitting session. Then they come straight onto the course with the same slice they’ve had for the last 20 years.

AI is not only a better set; it is a new way of playing the game. Your teams are being set up to use tools they do not trust and that they feel could ultimately replace them. You are working with a player who has played bogey golf for 15 years and, worse, thinks that is success. Even a two-week hyper-care period is like expecting someone to fix their slice after a single lesson.

The curse of being a club champion with old clubs

Why does success actually slow you down?

Interestingly, your current success is likely your biggest roadblock to AI transformation. We call this the local-optimum trap, and it is destroying AI adoption among the best distributors.

A $300 million PVF distributor we worked with appeared to be operating at peak performance. Its inside sales team had perfected its focus on the top 20% of its customers, who accounted for 80% of revenue. Every metric suggested efficiency. However, it posted par on its home track with persimmon woods and a set of 1992 blade irons.

The company was so good with its old clubs that any change felt like going backward.

However, our analysis unraveled the trap. Those 6,400 bottom-tier accounts it had written off were not all low-value; they were simply unserved. Many were large companies that purchased a narrow selection of SKUs only because they weren’t aware of the distributor’s expanded catalog. The sales force had optimized itself to win the senior flight at the cost of missing the championship altogether.

We were going to need to do what, for all practical purposes, looked like a regression. Rather than optimizing service to score for top accounts, we used AI to blanket the unserved customer base. Our AI identified 1,200 accounts whose purchase history and company data suggested significant buying potential. Many of these companies didn’t realize the recent growth in our distributor’s catalog. 

At first, reps felt as if they were wasting time and were less likely to make big sales. However, it soon became clear that these accounts generated $800,000 in new revenue in the first quarter without losing any of the larger accounts.

Sometimes being great with old clubs prevents you from seeing how the game has changed. Modern drivers are not slightly better than persimmon; they play a different game altogether. Sometimes, optimization is the enemy of opportunity.

Playing through fear as the member-guest

At an HVAC distributor, we developed AI for automated customer communications: following up on dormant accounts, sending technical bulletins about efficiency regulations and tracking contractor engagement. The branch manager was enthusiastic during demos but delayed the launch for months.

Turns out, his fear was visceral and specific: “If this thing sends the wrong message about refrigerant regulations to my biggest contractor during bid season, I’m done.”

This is accountability paralysis — the career equivalent of being too afraid to try your new swing during the member-guest tournament. The downside of shanking one in front of important clients feels catastrophic, even if your current game guarantees mediocrity.

We rewrote the risk equation by starting at the driving range, not the championship tee. The pilot targeted only accounts that hadn’t ordered in more than 12 months — the equivalent of practicing new techniques when no one’s watching. We agreed to walk away, no questions asked and no charge if the return on investment wasn’t clear. However, when those results showed 23% reactivation rates with zero relationship damage, we instead expanded to active accounts.

Building confidence through low-stakes practice proved more effective than demanding faith in high-pressure situations.

Learning from a pro, not YouTube

Most AI implementations treat training like showing someone golf videos. Let me show you the grip, here’s the stance, now go play 18 holes. However, you wouldn’t expect to double your yardage by watching YouTube; you need a coach studying your specific swing.

With one client, we achieved 90% adoption by treating our role as a coach, tailoring our recommendations to the team we were working with. It’s now our go-to methodology.

Week one was pure observation: watching the current processes, identifying inefficiencies and flagging important idiosyncrasies. No swings, just understanding the mechanics.

Week two and three involved side-by-side execution with experienced team members showing how the AI worked and how we can fix their problems. We gathered suggestions each day, implemented them overnight and came back for more feedback. At the same time, we advised on the best ways to use the new order-entry system. It’s like having a coach guide your hands through the proper motion.

Only in week four did users work independently. This apprenticeship model took longer and was far more involved than traditional software training, but each rep felt genuinely invested in the software because they had a hand in making it. From there, adoption is trivial. We created something that genuinely delighted them.

The competitive reality: They’re already playing a different game

Here’s what should keep you up at night: while you’re debating whether to upgrade from your trusty old irons, your competitors aren’t only using new clubs — they’re playing an entirely different game. They have GPS rangefinders showing wind conditions and elevation changes. They have launch monitors analyzing every swing. They have caddies (AI agents) who know every break on every green.

A distributor successfully using AI for 18 months isn’t only ahead in technology. It has:

Two years of clean, structured data that trains increasingly accurate models;

Teams that trust AI recommendations because they’ve seen thousands of successful outcomes;

Processes rebuilt around AI capabilities rather than AI forced into old workflows;

Compound advantages that multiply daily.

The gap isn’t linear — it’s exponential. Every month you wait, your competitors are not only hitting better shots; they’re playing courses you can’t even access yet. At some point, it becomes impossible to catch up. 

If the U.S. industry cannot adapt to this new reality quickly, this will be the biggest lost opportunity America has seen this century. Other countries are already seizing this advantage.

Your AI investment isn’t gathering dust because the technology failed. It’s waiting for you to stop shopping for clubs and start rebuilding your swing. The distributors who figure this out first aren’t only improving their scores, they’re changing what par means.

The technology is ready. The range is open. The only question is: Are you willing to rebuild your swing while there’s still time to make the cut?  

Ishir Vaidyanath is cofounder of Paragon, specializing in custom enterprise AI recommendations and deployments for manufacturers and distributors. He’s driven AI at Fortune 50 companies, scaled a global nonprofit to 43 chapters in 15 countries, and spent time on Splunk’s security team

Kasyap Chakra is cofounder of Paragon, where he designs and implements custom AI solutions for manufacturers and distributors — on-site and end-to-end — to ensure real adoption. A former AI researcher, he sold his last AI startup to Scale AI and spearheaded its enterprise AI team efforts.