The conversation around artificial intelligence in wholesale distribution has shifted from “if” to “when”—and according to industry experts, that window is narrowing rapidly. In a recent panel discussion hosted by Distribution Strategy Group, three voices from the front lines of AI implementation delivered a clear message: distribution companies that haven’t started their AI journey are already behind.
Brian Hopkins, Chief Operations Officer at Distribution Strategy Group, moderated a discussion with Jason Sullivan, founder and CEO of Distro, and Brooks Hamilton, founder of AI Strategy Advisors. What emerged was a frank assessment of where the industry stands and where it must go to remain competitive.
Understanding Agentic AI: Beyond Traditional Automation
The term “agentic AI” has become ubiquitous in technology circles, but its meaning remains fuzzy for many distribution executives. Sullivan provided clarity by returning to first principles. Agency, he explained, fundamentally means autonomy, or the degree to which a system can operate independently without constant human steering. Rather than a binary state, agency exists on a spectrum, with varying levels of human intervention determining where any given system falls.
Hamilton expanded on this framework by identifying core components that define an agent. These systems possess objectives, develop plans to achieve them, sense their digital environment, and importantly, remember both long-term procedures and short-term context. They create workarounds when obstacles arise and continuously adapt based on what they learn. This combination of planning, sensing, acting, and remembering distinguishes true agentic systems from simpler automation tools.
The practical implications become clear when examining how these systems appear in distribution today. Sullivan outlined a progression of agency levels in quoting processes. The most basic level involves AI-generated product recommendations within quotes. Helpful, but still requiring significant human oversight. The next tier introduces sensors that monitor email inboxes, automatically processing quote requests when they arrive. More advanced systems enable real-time quote adjustments through conversational interfaces, handling dynamic cross-referencing and accessory bundling based on customer feedback. The highest levels autonomously update pricing based on market conditions or manage entire quote processes end-to-end.
Hamilton organized these applications into three categories that help executives understand where to focus initial efforts. Information routing and response encompasses customer service, knowledge-based inquiries, and quote preparation—areas where many distributors are seeing active implementation today. Data transformation and integration includes price file updates, invoice reconciliation, and product matching use cases currently in pilot stages across the industry. Decision support and optimization — covering inventory management and pricing strategies — represents the frontier where formal optimization techniques are being reimagined within agentic frameworks.
The Immediate Battleground: Speed and Service Quality
The competitive implications of these technologies are already materializing in ways that should concern any executive not yet engaged with AI strategy. Hamilton shared a telling example from a procurement team his firm advises. Previously, when this team sent a 200-item quote to suppliers requesting project-specific pricing and product matching, the standard response was “we’ll get back to you in a week.” Organizations equipped with AI-driven product matching and pricing tools now respond in under five minutes.
The implications extend beyond mere convenience. When one supplier takes a week and another takes five minutes to turn around the same request, buyer preferences shift decisively. This speed advantage translates directly to wallet share and customer loyalty — the metrics that determine long-term viability in distribution.
Sullivan emphasized that these improvements stem from eliminating what he termed “context switching”—the constant interruptions that prevent employees from focusing on their primary tasks. For counter personnel, the real job is developing customer relationships and serving effectively. Every moment spent flipping through catalogs or searching databases pulls attention away from that core mission. Hopkins recalled his early days working a Grainger counter, fumbling through 900-page catalogs while customers waited. Today’s AI systems allow employees to ask natural language questions and receive instant answers, transforming that experience entirely.
The shift doesn’t just benefit customers. Employees experience less stress, higher job satisfaction, and improved performance. Even inexperienced staff can project expertise and confidence when supported by systems that instantly surface the right information. Conversion rates improve because customers receive faster, more accurate service from representatives who appear more knowledgeable regardless of their actual tenure.
The Strategic Imperative: Acting Now
Hamilton made a striking declaration during the discussion: it was possible to sit on the sidelines until October 2024, but not anymore. Organizations that fail to engage with AI strategy immediately will find themselves struggling to catch up within 24 months. This assessment stems from recent technological breakthroughs that have crossed a critical threshold of capability and accessibility.
Sullivan reinforced this urgency with two principles: it’s never too early to start these conversations, and no organization is too small to benefit. Many smaller distributors assume these technologies are only for large players with extensive IT departments. This assumption is dangerously wrong. Modern AI solutions are increasingly accessible to single-location distributors and multi-billion-dollar operations alike. The technology vendors serving this space must achieve scale by serving the entire market spectrum, making sophisticated capabilities available at price points that work for smaller organizations.
Hamilton added a counterintuitive insight: smaller distributors may actually hold an advantage in AI adoption. Large organizations face complex coordination challenges, lengthy approval processes, and competing priorities that slow decision-making. Smaller companies can move faster, experiment more freely, and potentially run circles around larger, more bureaucratic competitors. This window represents a rare opportunity for nimble regional players to gain ground against national competitors.
Protecting Trust While Embracing Automation
Distribution’s fundamental nature as a relationship business raises legitimate concerns about introducing AI agents into customer interactions. Hopkins framed the central tension: distributors build trust over decades, knowing customers’ children and birthdays, becoming almost like friends. How do organizations protect this trust while deploying increasingly autonomous systems?
Sullivan’s answer centered on maintaining humans in the loop. The dystopian future where agents run wild and humans become subservient to machines isn’t inevitable—or desirable. The preferable path positions AI as a wingman or co-pilot that makes people more effective without replacing them. Trust and verification both matter. Organizations need sufficient confidence to deploy these technologies while maintaining safeguards that prevent autonomous failures from damaging customer relationships.
Hamilton quoted a distribution executive who articulated a compelling vision: “I want my organization to have AI all over it, making us extremely efficient and well-informed. And I do not want a single one of my customers interacting directly with AI.” This stance recognizes that AI should enhance rather than replace relationships. The technology excels at eliminating repetitive, energy-draining tasks while enabling new capabilities that were previously too expensive to consider.
One such capability involves using language models as translation tools—not between languages, but between the subtle dialects and communication styles of different customer personas. A facility manager at a Pacific Northwest tech company and a facility manager at a Louisiana industrial plant require different approaches, even when discussing identical products. AI can help sales representatives adapt their messaging, documentation style, and engagement approach based on customer profiles—a capability that previously required years of direct experience or extensive coaching from seasoned managers.
Preparing for Tomorrow’s Interactions
Looking 18 to 36 months ahead, both experts envision a bifurcation in how distributors and customers interact. Sullivan anticipates more face-to-face and phone-based conversations, but with a crucial difference: these interactions will focus on consultative discussions, forward-thinking project planning, and systems design rather than transactional exchanges. The purely transactional elements — quick recommendations, will-call pickups, routine reorders — will increasingly be handled through digital channels with AI support operating in the background.
Hamilton sees two parallel tracks emerging. Person-to-person discussions will continue for relationship building and strategic planning, with AI systems then executing on what was agreed during those conversations—generating agreements, creating purchase orders, assembling bills of materials, and solving implementation challenges. Simultaneously, the scope of automated system-to-system transactions will broaden beyond today’s EDI and ETL frameworks. More transaction types will be handled through agent-to-agent interactions, though this evolution requires both technological development and new organizational roles for testing, validation, and oversight.
The Path Forward
The experts offered clear guidance for executives wondering where to begin. Sullivan emphasized finding the right thought partners—vendors, consultants, and peers who can guide decision-making. The build-versus-buy question tilts heavily toward buy; attempting to build an internal tech startup within a distribution company diverts resources from core competencies.
Hamilton urged executives to invest in learning immediately. Attend conferences, meet with vendors, and gain exposure to other distribution organizations navigating these same challenges. The technology has crossed a threshold where AI applications can now create polished documents and perform complex tasks with legitimacy—capabilities that seemed out of reach mere weeks ago. This inflection point will drive increased activity and curiosity across the industry.
Both stressed that the strongest use cases appear where ROI is clearest: sales organizations, finance departments handling credit analysis and accounts payable, and procurement teams managing inventory with predictive analytics. These represent natural starting points for organizations beginning their AI journey.
The underlying message from all three participants was consistent: the competitive landscape is shifting beneath the industry’s feet. Distribution companies that engage with AI strategy now will build compounding advantages in speed, service quality, and operational efficiency. Those that wait will find themselves fighting an uphill battle against competitors who have already begun transforming their operations. The window for easy entry is closing, and the cost of delay grows steeper with each passing month.
As Chief Operations Officer of a Distribution Strategy Group, I'm in the unique position of having helped transform distribution companies and am now collaborating with AI vendors to understand their solutions. My background in industrial distribution operations, sales process management, and continuous improvement provides a different perspective on how distributors can leverage AI to transform margin and productivity challenges into competitive advantages.