Why This Matters to Distributors: Artificial intelligence has become a strategic priority across wholesale distribution, but few companies have moved beyond pilots and isolated applications. Distribution Strategy Group’s recently published AI Top 25 research shows that the distributors furthest along are separating themselves through executive accountability, stronger data, measurable business goals, and the ability to put AI into production across multiple functions.
Wholesale distributors have embraced artificial intelligence as a strategic priority, but most have yet to deploy the technology broadly across their businesses, creating a sizable divide between AI ambitions and execution.
Distribution Strategy Group’s recently published State of AI in Distribution research found that 93% of distributors consider AI a strategic priority, while just 16% have deployed it across multiple business functions. That represents a 77-percentage-point gap between strategic intent and operational execution.
The findings are based on a survey of 233 wholesale distribution executives, 57% of them C-suite executives. They show an industry in which interest in AI has become widespread while large-scale implementation remains concentrated among a small group of companies.
That divide is the focus of DSG’s AI Top 25, a benchmark developed to identify distributors that have moved AI beyond experimentation and into production with measurable business results.
DSG evaluated more than 300 North American distributors during a two-year research effort, reviewing public disclosures, earnings call transcripts, executive interviews, case studies, and vendor confirmation. Companies whose AI strategies consisted primarily of roadmaps, partnership announcements or pilot programs that had not reached daily operating use were excluded.
The benchmark was designed to recognize 25 companies. DSG included 26 after an additional distributor met all its criteria rather than eliminating a qualifying company to maintain the original cutoff.
Download the complete AI Top 25 report to see the distributors setting the pace for AI deployment and the strategies behind their progress. [Click here.]
Six Practices Separate AI Leaders
The companies in the AI Top 25 span industrial, electrical, healthcare, technology and foodservice distribution and include publicly traded, privately held and employee-owned businesses.
Despite those differences, DSG identified six practices that consistently separate companies with more mature AI operations: clear executive accountability, moving successful pilots into production, stronger data, deployment across multiple business functions, measurement against business results and sustained investment over several years.
The findings indicate that access to technology itself is becoming less of a competitive differentiator.
Most distributors can buy many of the same cloud platforms, generative AI tools, pricing applications, and forecasting systems. What varies is the ability to integrate those systems into daily operations and demonstrate that they are improving the business.
That shifts the AI discussion from what technology a distributor owns to how effectively it uses it.
Leadership Starts with Accountability
One of the clearest patterns DSG identified was executive accountability.
At companies with more mature AI deployments, responsibility typically does not sit with a broad committee or move among departments. One executive owns the strategy, controls the budget and is accountable for results.
That structure gives operating teams a clear decision-maker for prioritizing investments, resolving conflicts, and determining whether an application should move from testing into production.
Brian Hopkins, DSG chief operating officer, said the pattern appeared consistently among companies in the benchmark.
“All these folks named a single owner,” Hopkins said. “If you have budget authority, you’re the owner. You’re not a committee.”
The title varies by company. Responsibility may be with a chief information officer, chief digital officer, another senior executive or, at smaller organizations, the CEO.
The common factor is authority.
DSG’s research indicates that implementation can slow when responsibility is divided among steering committees, innovation teams, information technology departments, and operating units without one executive empowered to make enterprise-wide decisions.
Moving Beyond Pilot Mode
Another dividing line is whether companies treat pilots as a temporary stage or an accomplishment in themselves.
Distributors have launched chatbots, pricing experiments, forecasting tools, sales applications, and other AI projects. Many, however, remain isolated tests rather than part of everyday operations.
DSG’s methodology intentionally distinguished between experimentation and production deployment. Companies were not included based solely on AI announcements or pilots.
Top performers start with a defined business problem and measurable target. If an application demonstrates value, they decide about whether to expand it rather than allowing the project to remain indefinitely in testing.
Successful applications can then be extended across branches, regions, product categories, or business functions.
“They stopped piloting a couple years ago and started making sure it worked across the business,” Hopkins said.
The distinction matters because the number of AI projects underway is not necessarily a measure of maturity. A distributor operating two AI applications at scale and producing measurable results may be further along than a company running a dozen pilots.
Data Quality Emerges as a Critical Divide
DSG’s broader research also found that technology is not the biggest obstacle to adoption of AI.
People are.
Skills shortages and employee resistance accounted for 52% of the challenges distributors identified. Skills gaps alone were cited by one-third of respondents, making workforce readiness the largest individual barrier identified in the research.
Data was another major dividing line.
Among distributors reporting strong data quality, 81% expressed confidence in the return on their AI investments. Among companies reporting poor or inconsistent data, only 18% expressed confidence.
The findings reinforce a fundamental limitation of AI: It cannot correct unreliable underlying information simply because a company deploys more sophisticated software.
Poor product records, inconsistent customer information and incomplete pricing and transaction histories can weaken AI results. Companies that invested in standardized product information, cleaner customer records, and data governance before scaling AI entered deployment from a stronger position.
For distributors early in their AI strategies, the findings suggest improving underlying data may be more important than adding another application.
AI Spreads Across Business Functions
The AI Top 25 also shows AI moving beyond individual departments.
Pricing systems can support quoting. Forecasting can improve purchasing and inventory positioning. Digital commerce activity can identify sales opportunities. Better demand forecasts can improve warehouse planning and customer service.
“The companies that started this process are now taking multiple AI use cases and putting them together simultaneously,” Hopkins said. “That’s going to be a differentiator over the next couple years.”
Connecting those systems can create benefits beyond individual productivity gains.
Better forecasting can improve inventory decisions. More accurate inventory information gives customer service teams better information about availability and delivery. Customer interactions generate additional transactions and behavioral data that can improve future recommendations.
As more applications draw from common data, the value of individual deployments can increase.
That gives companies that started building interconnected systems several years ago an advantage that competitors may not be able to eliminate simply by purchasing similar software.
Business Results Become the Scorecard
Measurement is another distinction between AI experimentation and more mature deployment.
DSG found that 41% of distributors primarily measure AI through productivity and time savings, while 18% have no formal measurement process.
Companies in the AI Top 25 take a more structured approach. They establish performance measures before implementation and evaluate AI against business objectives such as revenue, costs, productivity, customer experience, gross margin, fill rates and days of inventory on hand.
That provides management with a clearer basis for determining which projects warrant additional investment and which should be discontinued.
It also shifts attention away from technology activity.
Launching an AI application is not a business result. Improving gross margin, reducing inventory, increasing sales productivity, or cutting customer response times is.
AI Leaders Take a Multiyear View
Many of the distributors furthest along with AI began investing well before generative AI became a mainstream business issue.
They spent years developing capabilities in automation, analytics, machine learning, and data management. Those investments created an operating and data foundation that could support newer AI technologies.
“They’re taking big swings,” Hopkins said. “It’s not a one-time investment this year. It’s investment over a number of years.”
The findings suggest AI leadership may depend less on being first to adopt each new application and more on developing an organization capable of repeatedly identifying useful technology, deploying it, measuring the results, and expanding what works.
A Benchmark for the Rest of Distribution
The AI Top 25 also shows that AI maturity is not limited to the largest distributors.
Large publicly traded companies appear alongside privately held and employee-owned distributors and companies serving specialized markets. Their resources and technology budgets vary, but DSG found similar management practices among those that reached higher levels of AI maturity.
Executive accountability, data quality, measurable objectives, production deployment, and expansion across functions recur throughout the benchmark.
For distributors outside the Top 25, those findings provide a practical benchmark.
Executives can assess who owns AI and controls its budget, whether product and customer data are ready to support AI on a scale, how many pilots have reached production, whether applications operate across multiple functions and whether results are measured against established business metrics.
The 77-percentage-point gap between strategic priority and broad deployment shows how far much of wholesale distribution must go.
But the AI Top 25 also provides evidence of what comes next.
The companies identified by DSG are moving AI out of isolated experiments and into operations, tying investments to measurable business results and connecting applications across the enterprise.
For distributors trying to make the same transition, the benchmark offers a view not of what AI may eventually do, but of how leading distributors are putting it to work today.
Download the complete Distribution Strategy Group AI Top 25 report for the rankings, company examples and lessons from distributors that have moved AI from experimentation to execution. [Click here.]
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