Busting the Top AI Myths Holding Distributors Back
Hesitation is understandable, but here’s the reality: waiting is riskier than starting.
Artificial intelligence has been one of the buzziest topics in distribution this year, but for many mid-market distributors I talk to, it feels less like a question of “if” and more a question of “how.” They know AI is here to stay, and they know they need to embrace it – but it can feel like a mountain to climb. The challenge isn’t belief in the technology, it’s figuring out where to start without getting overwhelmed.
That hesitation is understandable. Distributors already juggle thin margins, staffing shortages, and rising customer expectations. Adding another layer of technology on top of all that can feel overwhelming. But here’s the reality: waiting is riskier than starting. Every month you delay is time your competitors may be experimenting, learning, and building an advantage.
At White Cup, we work with distributors every day who are in the same boat: curious about AI, but cautious because of a handful of myths. Let’s bust those myths one by one.
Myth #1: “We need to clean up our data before we can start applying AI.”
For years, the mantra in analytics was “garbage in, garbage out.” And while that still has some truth, modern AI tools are far more forgiving.
Today’s models are built to handle messy, incomplete, or outlier data. In fact, AI can often help identify those inconsistencies and flag them for you. That means you don’t need to wait for a mythical moment when all your customer, product, and transaction data is pristine.
The bigger risk is time. Every year you wait for “perfect data” is a year of missed insights.
Takeaway: Don’t let the pursuit of perfection slow you down. Start with one dataset, one product category, or one region. “Good enough” is often more than enough to create value.
Myth #2: “AI is only for the big players.”
It’s easy to assume AI is reserved for Fortune 500 companies with data science teams and multimillion-dollar budgets. But that’s no longer the case.
With distributors we work with, I’ve seen success come from starting small: a handful of curious team members building a prompt library for common tasks, or a sales manager experimenting with AI-enabled CRM features. You don’t have to build a large language model yourself to reap the benefits.
As Gartner noted in its 2024 Market Guide for AI in CRM, “The democratization of AI is well underway,” with mid-market firms among the fastest adopters. And according to NAW’s Facing the Forces of Change report, technology adoption in distribution is less about company size and more about leadership willingness to experiment.
Takeaway: You don’t need a Chief AI Officer to start. Ask your technology partners what AI capabilities are already embedded in the systems you use every day – you may already be paying for them.
Myth #3: “My people aren’t going to adopt this.”
This is one I hear most often from executives: “Our people won’t use AI.” But here’s the truth – many of your people already are.
Research from Salesforce found that nearly half of workers are already using generative AI tools in some capacity. Sometimes that’s ChatGPT for writing help, sometimes it’s autocomplete in an email program, sometimes it’s a research summary in Google.
In other words, adoption is already happening – just without visibility or guidance. That creates risk. Wouldn’t you rather know how your employees are using AI and provide direction?
On the flip side, there are employees who aren’t comfortable yet. For them, the best path forward is role-specific use cases that feel safe: a rep shaving time off quote prep, a marketer summarizing long spec sheets, or a manager using AI to draft agenda notes.
Takeaway: Don’t frame adoption as a battle. Frame it as an opportunity to give employees tools that make them better at their jobs.
Myth #4: “It doesn’t matter which AI tool we choose.”
Not all AI tools are created equal. The wrong choice can lead to wasted spend, low adoption, or even data risk.
Distributors should ask tough questions:
- Does the tool train on your proprietary data?
- Can you control access at the user level?
- Does it solve a business problem that matters, or is it just “cool tech”?
Purpose-built AI tools, especially those designed with distributors in mind, tend to deliver value faster and with less lift from your IT team. Successful AI projects are the ones tied directly to business outcomes like sales productivity and margin improvement – not technology for technology’s sake.
Takeaway: Getting started matters, but choosing the right tool matters just as much. Look for partners who understand your business model and provide transparency on how their AI tooling works – so your team can adopt it confidently, build expertise, and realize value without putting your data at risk.
Myth #5: “AI will replace the human touch in sales.”
This one gets to the heart of what makes distribution unique. Relationships are everything. And when relationships are at the center of your business model, it’s natural to be wary of anything that sounds like “automation.”
But in reality, AI is most powerful when it removes the noise that gets in the way of relationships.
Here’s what that looks like in practice:
- Freeing up time: Instead of spending hours logging notes or updating CRM, AI can capture the details of a customer meeting and automatically generate follow-ups. Reps can focus on the customer instead of their keyboard.
- Making insights actionable: AI can flag customers who haven’t purchased in a while, highlight product lines in decline with a specific customer, or identify complementary products worth suggesting. These are things a busy rep might miss—but they can be the spark for a better conversation.
- Strengthening – not replacing – judgment: AI doesn’t know your customer the way your best salesperson does. But it can arm that salesperson with the context, history, and opportunities that make every conversation sharper.
What distributors should watch for is how AI tools are framed and introduced. If you roll them out as a replacement for human decision-making, adoption will stall. But if you present them as an assistant – a form of selling support, a digital coworker that helps reps do more of what they already do best—they’ll quickly become part of the fabric of sales.
Takeaway: AI isn’t here to erode your customer relationships. It’s here to amplify them—making sure every rep shows up more prepared, more responsive, and more focused on the customer.
Final Thoughts
Distributors don’t need perfect data, massive budgets, or a workforce of data scientists to get started with AI. What you need is a willingness to experiment, to start small, and to focus on business outcomes that matter – whether that’s margin improvement, productivity gains, or deeper customer engagement.
But one point bears repeating: choosing the right partners is essential. Not every AI solution is built with distribution in mind. The best outcomes come when you work with technology providers who understand the intricacies of your business – your data structures, your sales workflows, your customer relationships – and can translate AI into tools that serve those realities.
The myths are real, but so are the opportunities. The distributors who will thrive in the next decade are the ones who move beyond hesitation, partner wisely, and start learning today.
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Read the original article in Industrial Distribution, or find it in print from the September/October 2025 edition.
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