Walk into any distribution conference these days, and you won’t go more than a minute before encountering the term “AI.” You’ll meet dozens of AI vendors, many of which may be brand new, and they’ll all claim to offer the perfect product to carry your company into the future.
When you combine that with the pressure you’re already feeling from your board to implement AI-powered solutions, it’s a little overwhelming. Where do you even start? Are you already behind?
For many distributors, the problem isn’t that they’re falling behind. It’s that they’re getting ahead of themselves. The allure of advanced automation can distract from a more fundamental reality: Their core operations still need work. Even the most sophisticated AI solution won’t deliver value unless it’s applied to a clear, strategic business problem.
That’s why leaders should think of AI not as a race to win, but as a puzzle to solve. There’s no prize for moving first if the pieces don’t fit together and forcing them often creates more problems than progress.
AI Is a Force Multiplier, for Better or Worse
There’s no doubt that AI tools can be useful. What concerns me is when distributors invest aggressively in AI before they can consistently execute the fundamentals.
Pricing is inconsistent. Margins are leaking. Inventory data is unreliable. Critical information lives across silos, limiting visibility into which products, customers, and decisions drive profitability. As a result, many distributors spend more time reacting to problems than preventing them.
As powerful as AI has become, it still can’t compensate for operational dysfunction. If the underlying data, processes, and decision-making frameworks are weak, AI will struggle to produce meaningful results.
That’s the real risk: AI acts as an accelerant for whatever environment you introduce it to, good or bad. When deployed within a healthy operation, it supercharges speed, insight, and execution. But when applied to fractured processes, it simply fast-tracks bad data, magnifies flawed assumptions and creates even more noise around decision-making.
In other words, AI is not a substitute for sound business strategy and operational discipline.
Lead with Strategy, Not Anxiety
The wave of AI hype has created real pressure for distributors. Many leaders feel like they need to respond quickly, which often leads to statements like:
“We need an AI initiative.”
“We need to keep up.”
But that’s not a strategy. It’s a reaction. Instead of starting with AI anxiety, start with the business problem that needs to be solved:
“We need to decrease pricing inconsistency.”
“We need to reduce reactive decision-making.”
“We need better visibility into margin leakage.”
“We need to understand product profitability.”
From there, a real strategy can start to take shape. If AI is the right tool for the job, pursue it. But if it doesn’t fit, don’t force it. Like any other business investment, AI solutions should be rigorously evaluated based on their ability to solve a specific operational problem and deliver measurable value, not simply because they include AI. Jigsaw puzzles don’t come together that way, and neither do complex businesses.
There’s another reason not to prematurely force AI into the business: Over time, it will become embedded in the software distributors already use. Just as the internet and ecommerce moved from “new initiatives” to basic business infrastructure, AI will become part of enterprise systems by default. Most companies won’t need to chase every standalone tool to benefit from it.
The most successful distributors won’t rebuild their operations around a vague idea of the future. They’ll focus on the problems directly in front of them: improving workflows, automating repetitive tasks, enhancing forecasting, strengthening pricing precision and identifying operational insights faster.
That means asking sharper questions:
Where are we losing margin?
Which workflows create the most friction?
Where are decisions being made too slowly?
What operational errors keep happening?
Where is labor being wasted?
What is preventing us from scaling today?
Once leaders have answered those questions, they can decide what role AI should play, if any. The biggest risk isn’t adopting AI late. It’s investing in an AI project that consumes time, budget, and attention without meaningfully improving the business.
Take your time and don’t sweat the hype cycle. Instead of rushing to buy an AI tool and hunting for a problem to solve, which might just create entirely new headaches, use this wave of AI anxiety as an opportunity to truly think about your business performance. Focus on the operational improvements you need first and then let AI be the engine that advances the initiative once you know exactly where it fits.
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