Editor’s note: This is part one of a two part series.
The distribution industry stands at an unprecedented crossroads. As artificial intelligence capabilities accelerate beyond what seemed possible even months ago, executives face a question that extends far beyond quarterly earnings and operational efficiency, what happens to the people who have built our companies?
The conversation about AI and employment in distribution has moved from philosophical speculation to urgent strategic imperative. The decisions leaders make today — not next year, not after the technology matures — will determine whether their organizations thrive through augmentation or stumble through disruption.
“Until October, I believe people could catch up,” said Brooks Hamilton, chief, founder and CEO at AI Strategy Advisors. “That window has closed. Organizations that take advantage of this are going to be at an advantage. It is truly game on.”
Why October Changed Everything
For nearly two years, large language models offered impressive assistance with emails, presentations, and ideation. They were helpful tools, but they couldn’t handle complete workflows. That limitation provided a buffer zone where human expertise remained irreplaceable.
In October, that barrier fell. Claude, the AI system from Anthropic, released tools capable of building Word documents, PowerPoint presentations, and complex Excel spreadsheets. Hamilton described using it to generate a ten-tab Excel document with RACI diagrams, color-coding, and thoughtful analysis — work that previously required hours of skilled labor. A RACI chart is a simple matrix that clarifies who is responsible, accountable, consulted, and informed for every task in a project.
“Much more knowledge work is now going to be possible via these language models,” Hamilton explained. “Don’t think of it as replacing a person with a language model. Instead, think of it as the ability to create meaningful knowledge worker outputs is now within the realm of language models as a tool.”
The implications ripple across distribution operations. An order investigation that once required checking multiple systems, writing follow-up emails, assembling results, creating presentations, tracking outcomes, modifying systems, and generating action item spreadsheets can now be automated end-to-end. The question is no longer whether AI can handle complex workflows—it’s how distribution companies will respond to this capability.
The Tortoise, The Hare, and The Beaver
In an episode of AI News & Gurus the discussion on the traditional innovation adoption narrative was reframed. Rather than positioning companies as either aggressive first movers or cautious followers, a third archetype was revealed: the beaver.
Leaders need to be building the ecosystem in which they are going to thrive the most. They are going to control how that ecosystem functions. Everyone around them is going to start to integrate and find their place in that ecosystem.
This perspective challenges two extremes. The “hare” companies racing to automate everything risk finding themselves alone, having sacrificed the relationships and knowledge that differentiate distributors. The “tortoise” organizations waiting to see what happens will fall irretrievably behind as competitors build compounding advantages through AI efficiency.
The beaver approach requires thoughtful construction of systems where AI handles tasks while humans focus on relationships, industry knowledge, and strategic decision-making. According to DSG research, 40% to 60% of distribution roles across various functions will place greater emphasis on relationships than task automation.
Instead of spending a day creating a quote and doing all that work and the data entry side of it, that can be done in five minutes to half an hour with AI. You’ve got that day back, where you’re going, okay, how do I serve that customer better? How do I look into what they are going to need six months from now?
Beyond the Platitude: What Is “Higher Level Work”?
The most common response to AI automation concerns sounds reassuring: employees will be “freed up for higher level work.” But this platitude crumbles under scrutiny. What exactly does higher level work mean for an inside sales representative whose quote generation now takes five minutes instead of a full day? What does it mean for a customer service agent when AI handles routine inquiries? What does it mean for operations managers when inventory optimization runs automatically?
The uncomfortable truth is that most distribution companies haven’t defined what this higher-level work looks like in practical terms. They haven’t redesigned roles, rewritten job descriptions, or created new performance metrics that reflect an AI-augmented workforce. The phrase becomes a convenient deflection rather than a strategic answer.
Industry leaders need to get specific. Instead of measuring an employee by how many orders did you fulfill, it will be what is your strongest relationship? How many returning customers do you have? You’re going to start to reassess where the emphasis is on your time.
This shift represents a fundamental reimagining of value creation in distribution. The customer-facing employee who once spent 80% of their time on transactional tasks and 20% on relationship building must flip that ratio. But this transition requires more than time reallocation.It demands different skills, training, management approaches, and ways of measuring success.
Hamilton framed the challenge in terms of strategic foresight: “Economic pressure almost always wins in the long run but getting ahead of the ‘what’ strategically will help you control how it wins. Those who wait will have it decided for them.”
Companies that proactively define higher level work create intentional pathways for their workforce. Those that don’t will find the market defining it for them—typically through layoffs and consolidation rather than thoughtful role evolution. The difference between these approaches isn’t just ethical — it’s strategic. Organizations that successfully redesign roles around human and AI collaboration will retain institutional knowledge, maintain customer relationships, and attract talent that competitors lose.
The Role Redesign Challenge: Will Versus Skill
Understanding roles must change is one thing. Actually, redesigning them is another. Distribution leaders face two distinct barriers: will and skill.
The will question asks whether companies possess the commitment to invest in role transformation when the easier path involves headcount reduction. Ian Heller, chief strategy officer for Distribution Strategy Group, highlighted this tension when he noted that companies claiming to manage AI adoption through attrition face mathematical impossibility if everyone pursues the same strategy. Someone will choose the path of workforce reduction, gaining immediate cost advantages that force competitors to follow.
“If I don’t do it and my competitor does, and he now has a 20-point cost advantage, customers are going to flock to that solution,” Heller observed. “Look how many people say they really don’t like Amazon, and they buy there all the time — me included. The value proposition is so compelling, it’s just irresistible sometimes.”
This race-to-the-bottom dynamic makes the will question even more critical. Companies must decide whether they believe sustainable competitive advantage lies in being the lowest-cost provider through maximum automation, or in being the highest-value provider through enhanced human capabilities augmented by AI.
The skill question addresses whether distribution leadership teams know how to redesign roles for an AI-augmented workforce. Most executives built their careers in an environment where operational efficiency meant process optimization and system integration. Redesigning roles for human-AI collaboration requires different expertise. This means understanding what AI does well versus what humans do well, identifying which tasks to automate versus which to enhance, and creating hybrid workflows that leverage both.
Managers and directors are going to be so much more important for the solvency of a company. The new generation coming in won’t have the opportunity to learn the industry on the job. It’s going to be a lot more one-to-one training, trying to pass that knowledge down.
This observation reveals a hidden dimension of role redesign. It’s not just individual contributor roles that must change—management roles must evolve to emphasize knowledge transfer, coaching, and relationship building in ways that traditional distribution management didn’t require. The inside sales manager who once focused on activity metrics and close rates must now develop their team’s consultative capabilities and industry expertise.
The tsunami of change coming for all industries from AI’s impact will be felt for generations. The distribution industry has an opportunity to brace for this wave and build a sustainable business ecosystem in its wake.
Check out part two of this article “The AI Employment Paradox: What Winning Looks Like” next week. And join us for the next AI News & Gurus to stay up to date on all things AI in distribution.
