Why This Matters to Distributors: The companies gaining ground with AI are not chasing broad transformation. They are targeting specific workflows, improving data discipline, and building internal trust before scaling, which directly drives sales productivity and faster response times.
Dwayne Roberts, president of Summit Electric Supply, has a clear message for wholesale distributors navigating AI adoption: start narrow, validate with data, and build organizational confidence before expanding.
Roberts will share that approach June 25 at Distribution Strategy Group’s Applied AI for Distributors conference in Chicago, where he will join Natasha Broxton, founder, owner and chief executive officer of Alitura Group and Select Auto Parts and Sales, and Stu Tisdale, senior vice president and chief experience officer at ADI Global Distribution, on a panel examining how distributors of different sizes are making real investment decisions and setting AI priorities.
For Summit, a Sonepar operating company, the strategy is tightly focused: deploy AI in narrow use cases that remove friction from daily workflows, test results against internal assumptions and expand only after earning employee trust.
Using AI to Challenge Sales Assumptions
One of Summit’s first AI applications targets sales performance, a function historically driven by manager intuition and rep experience rather than structured data analysis.
Roberts said the company is using AI to evaluate account manager portfolio performance, identifying where customer penetration is underperforming and where coverage is oversaturated or losing market share, to identify targeted sales representative opportunities for growth, then comparing those findings against internal assumptions.
“You’re taking something you would normally give to an individual to ferret out opportunities or issues, and you’re validating it with data,” Roberts said.
Summit categorizes accounts by lifecycle, growing, declining or lost, and overlays those designations with revenue potential. The goal is to align sales effort with opportunity size rather than habit or history.
“Quality of service should be consistent across the organization, however, resource allocation may differ quite a bit based on the size and scale of the customer and opportunity,” Roberts said.
By reallocating accounts and directing reps toward higher-value opportunities, Summit expects measurable gains in productivity by increasing the number of salespeople across the organization and creating account packages that drive customer engagement and penetration.
Compressing Quote Cycles from Days to Minutes
Quoting is among the most labor-intensive workflows in distribution and one of the highest stakes. Summit developed an AI-enabled tool that ingests bills of materials, identifies SKUs, checks inventory availability, and flags missing data. The system can generate preliminary quotes in minutes, compared to a process that previously required hours or days.
Early testing exposed a design flaw: the tool searched for inventory across the entire company rather than filtering by location, adding complexity for sales teams rather than reducing it.
“We made it too wide open,” Roberts said, noting that refinements are underway to align outputs with how sales teams actually work.
Despite those adjustments, early results showed a 76% match rate when processing bill-of-material inputs, a figure Roberts said is sufficient to significantly accelerate the quoting process.
Speed, he said, frequently determines who wins the order.
“In our industry, the first person to respond with a quality quotation that includes accurate information has the lead in getting the order,” Roberts said.
Scaling Growth Without Adding Headcount
Summit’s AI push reflects a structural challenge common to growing distributors: the company has expanded 120% in recent years while keeping its outside sales headcount flat. Sustaining that trajectory without adding proportional staff requires productivity gains that traditional training and management cannot deliver alone.
AI is one answer, reducing time spent on administrative tasks such as data entry and product search heavy quoting while enabling faster onboarding for new hires who may lack deep industry experience.
“We need tools that make them more efficient so they can spend more time calling on customers and solving problems,” Roberts said.
Roberts also directed his financial planning and analysis team to run company financials through an AI model to generate executive-level summaries across revenue, gross margin, operating expenses, and earnings, enabling leadership to focus on corrective action rather than on identifying where problems exist.
“I want an executive summary that tells me where we should be looking and identifies both positive and negative trends,” Roberts said.
Culture as the Adoption Engine
Roberts said the most significant barrier to AI adoption at Summit is not technology. It is clearly articulating the organizational impact. To address that, the company’s leadership team is actively promoting AI use, engaging employees directly and incorporating frontline feedback into tool development.
“You have to champion it as a leader,” Roberts said. “People need to see that you’re using it.”
Summit has taken a disciplined approach to roll out, limiting concurrent initiatives to a small set of strategic priorities and resisting the temptation to introduce too many tools simultaneously.
“If I send out five or 10 new things, they won’t stick,” Roberts said. “We’re going to get one or two things done before we layer on additional priorities or tools that may dilute our message.”
The company is also prioritizing internal adoption before deploying AI in customer-facing processes. Roberts said sales teams must trust the tools themselves before presenting them to customers in a service-driven industry where reliability and relationship quality are decisive.
“If you don’t believe in what you’re selling, it’s not going to work,” he said.
Data Quality and the Limits of Prompting
Like many distributors, Summit’s most persistent technical challenge is data quality. Inconsistent, incomplete, or unstructured products and customer data limits what AI systems can reliably produce.
“Bad data in, bad data out,” Roberts said.
Equally important, he said, is the discipline of prompting, learning to ask AI systems the right questions to generate actionable output rather than generic responses.
“If you don’t know the right question to ask, it’s only going to answer what you ask which limits the quality and impact of the output,” Roberts said.
As part of Sonepar, Summit has access to a growing suite of enterprise-level digital tools. But integrating those tools with its SAP-based systems requires additional development work, particularly around API connections, a dynamic that has forced Summit to balance the advantages of scale against the need for speed and operational flexibility.
A Long-Term Priority, Executed in Small Steps
Roberts views AI as essential to Summit’s long-term competitive position, particularly as labor markets tighten and customer expectations for response time continue to rise. In the near term, the focus remains on targeted, incremental adoption, introducing AI in ways that are immediately useful and manageable for employees.
For distributors watching the AI adoption curve, Summit’s approach suggests that the path to scale runs through a series of narrow, well-validated wins while also reaping the benefits of Sonepar’s broad transformation initiatives. Distributors that build data discipline and organizational trust around a handful of use cases will be better positioned to expand AI deployment as the technology matures and the internal resistance that has slowed adoption at many companies begins to erode.
The Applied AI for Distributors Conference will be held in Chicago from June 23 to 25. Learn more at appliedaifordistributors.com.
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