In my conversations with distributor sales teams across the country, one theme comes up more consistently than any other: finding information takes too long. The right replacement for a discontinued valve, the spec sheet for a specific water heater model, last quarter’s quote for a returning contractor. Questions that should take 30 seconds are taking 10 minutes or longer.

The average PHCP distributor has been in business for decades. That represents decades of product specifications, pricing agreements, technical bulletins and institutional knowledge. Most of it lives in SharePoint folders that are difficult to navigate, in the minds of your most experienced employees, or nowhere at all.

We work with a distributoaore than 50 years of history. Their leadership identified what their sales floor had been feeling for years: the knowledge exists, but people cannot access it when they need it. That inefficiency was not just costing time. It translated directly into quotes that were never sent, upsell opportunities that were missed and new hires who struggled to ramp because the answer to most questions was “go ask the senior rep.”

Preserving institutional knowledge

This points to a larger issue facing the industry. The employees who carry the deepest product expertise are approaching retirement. The average age of a skilled worker in wholesale distribution is over 50. When these individuals leave, their knowledge leaves with them. No training manual can capture 30 years of knowing which valve crosses to which, or which fittings a particular contractor prefers on commercial jobs. And the efforts most distributors have made to address this, better file organization, shared drives, internal wikis, have rarely gained traction because the information remains scattered and inaccessible at the moment it is needed most.

A new approach to product information

This is where AI is beginning to make a meaningful difference: purpose-built agents trained on a distributor’s own catalog, ERP system, pricing and technical documentation. A counter rep can ask the system for the equivalent of a specific product from a competing manufacturer and receive an accurate, sourced answer in seconds, drawn entirely from the company’s own materials.

The results from that Dallas distributor illustrate the powerful impact. After loading more than 1,100 documents into an AI-powered knowledge engine and connecting it to their Prophet 21 ERP, knowledge retrieval times dropped by 80%. Product match accuracy reached 90%. The team has logged more than 4,200 conversations through the platform, with adoption spreading organically across every level of the organization.

What was most encouraging was where adoption took hold. Leadership anticipated the greatest resistance from tenured managers with 20 or more years of experience. Instead, those individuals became the platform’s strongest advocates. They recognized it as a way to extend their expertise across the entire organization rather than fielding the same questions repeatedly. Five words became part of the company’s daily vocabulary: “Did you ask the Distro Agent first?”

Turning knowledge into productivity

But the knowledge problem does not end once someone finds the right product. It shows up again the moment that information needs to move into a quote. A typical request for quote at a mid-sized PHCP distributor involves 50-to-100 line items. Each one requires matching to the correct catalog item, verifying availability and pulling current pricing from the ERP. Done manually, that process takes upwards of 30 minutes, and it depends on the same institutional knowledge that is already difficult to access. The rep building the quote needs to know product equivalencies, preferred substitutions and pricing structures that may not be documented anywhere.

This is where the same AI infrastructure that solved the knowledge retrieval problem extends naturally into quoting. At that Dallas distributor, the team began running incoming RFQs through AI that reads the request, matches line items to the catalog and pulls live ERP pricing. Quoting time dropped from 30 minutes to approximately five minutes, an 83% reduction. The system matches items correctly about 90% of the time on the first pass, and accuracy improves with continued use as the model learns from corrections and feedback.

The time saved matters. But the greater value is in what teams do with that capacity. More quotes reach customers faster. Complex jobs that were previously passed over due to bandwidth constraints are now being pursued. Reps who used to spend their mornings buried in spreadsheets are back on the phone with customers. And the entire process is supported by a dedicated team with sub-10-minute response times that stays with the distributor from discovery through go-live and well beyond. Implementation takes weeks, not months.

I believe the next 12 to 18 months will be a defining period for this industry. The distributors who invest in capturing and scaling their institutional knowledge will be better positioned to grow, serve their customers and develop their workforce. The technology is available today, already running in branches, used daily on real customer interactions. It is worth a serious look. 

JASON SULLIVAN is the founder and CEO of Distro, the AI Revenue Platform for distributor sales teams and manufacturers’ reps. Distro automates quoting, from one-offs to complex takeoffs, empowering sales teams to sell faster and more profitably while driving fast adoption with measurable impact. The company partners with leading distributors to help them thrive in the AI era.