When I was starting out in the military in the 1980s, we had something called “green machines” – very large computers in suitcase-sized cases that were tough enough to be parachuted out of a C-130. This was how we did logistics in the dirt. It was the cutting edge back then, and it got me thinking about technology and what it might be capable of in the future. I’ve now been in warehousing and distribution for decades, running after pallets, chasing down orders and learning how to turn overstock into success. I’ve seen how logistics technology has evolved. Innovations like advanced AI and robotics make those old green machines look like dinosaurs. This article is part 4 in a series about how distributors can get the most out of today’s tech. 

In our last article, we explored how AI is revolutionizing warehouse operations and inventory management: automated inventory tracking, AI-optimized picking and predictive demand planning that slashes overstock and stockouts. We saw how companies like Amarra cut overstock by 40% with AI-driven tools, proving that AI isn’t just for the big players — it’s a game-changer for distributors of all sizes when paired with a solid tech foundation.  

But implementing new technology like AI in wholesale distribution isn’t for the faint of heart. It’s a high-stakes move that can turbocharge your operation or leave you scrambling to recover. You’ve heard the saying: people don’t plan to fail, but they often fail to plan. That’s the crux of it. Even if you have the best intentions, you can still crash and burn without a rock-solid strategy, especially when bringing AI into the mix. 

The stakes for leaders eyeing this leap are clear: align it with your business goals, sidestep the pitfalls and unlock serious value. Botch it, and you’re staring at cost overruns, frustrated teams and a “go-live” that never quite arrives. Let’s break down why AI implementations stumble and how to get it right. 

Why AI Implementations Fail 

First off, picking the right tech isn’t something most distribution leaders do every day. You might be a wizard at optimizing warehouse flows or negotiating supplier deals, but selecting an ERP, WMS or AI solution? That’s a different beast. Even your IT crew, aces at keeping servers humming or pushing updates, rarely have the operational chops to match software to your floor’s realities. Too often, the decision hinges on a slick sales pitch or a gut feeling about the “nicest” vendor. Spoiler: that’s a lousy strategy. Sales reps might mean well, but their north star is their quota, not your long-term success. A misaligned choice here can saddle you with a system that looks great on paper but flops in your bins. 

That’s where a vendor-agnostic selection partner shines. These pros live and breathe tech evaluations. They’ll dissect your operation, noting inventory quirks, picking pain points and spotting data gaps. Then, they’ll match you with a solution that fits, not just what’s trending. I’ve seen companies dodge six-figure mistakes by leaning on this expertise instead of rolling the dice with a charismatic rep. Once you’ve got the right tool, the real work begins: prepping your business and people for the shift. 

The Planning Trap Leaders Fall Into 

Distribution leaders are doers and go-getters who thrive on action. That’s a strength, but it’s also a trap. Charging into an AI rollout without due diligence is like building a house with no blueprint and a plumber as your architect.  

Eric Kimberling nails this in The Final Countdown, where he frames digital transformation as a staged journey. You wouldn’t skip the foundation and start with the roof, so why rush an AI launch without a plan? Half-baked prep leads to delayed go-lives, ballooning costs and floundering teams. Kimberling’s analogy stuck with me: transformation needs structure, not chaos. Here are my top five takeaways from his book, tailored for your AI journey: 

1. Define success upfront. Before you start, nail down what “done” looks like—say, 20% faster picks or 15% less overstock. 
2. Map the process first. Document how work flows now, then design how AI will fit, not the other way around. 
3. Stage the rollout. Test AI in one area (like forecasting) before betting the farm. 
4. Engage the frontline early. Get buy-in from pickers and planners; they’ll spot flaws executives miss. 
5. Measure and adjust. Track ROI at every step, tweaking what needs fixing before it becomes entrenched. 

These aren’t just theory. They’re guardrails against failure. 

The Big Roadblocks — and How to Bust Them 

The toughest hurdles in AI implementation are cost, workforce training and system integration. Let’s tackle each. 

• Cost: AI comes with a hefty price tag. Software licenses, cloud fees and consulting hours add up quickly. To maximize ROI, focus on high-impact areas first, like demand forecasting, which offers quick wins in purchasing accuracy. If you’re on a cloud solution, check if your provider has already rolled out AI use cases in the product — typically, they’ve tested these with other companies before release, reducing your risk. Try those features first to gauge their value, then scale up. A phased approach keeps your budget in check while building momentum for broader adoption. 
• Workforce training: Your team isn’t obsolete. They’re the heart of your operation. Untrained staff, like pickers ignoring optimized routes or buyers dismissing forecasts, can derail AI’s potential, but ignoring their need for buy-in is even riskier — they’ll assume you’re planning to replace them with AI. Counter this by investing in hands-on training that shows how AI eliminates their grunt work, making their jobs easier and more effective. Foster curiosity: encourage them to test tools and ask, “What if?” Building trust and engagement ensures your team sees AI as a partner, not a threat, paving the way for smoother adoption and better results. 
• System integration: AI won’t deliver if it can’t connect seamlessly with your ERP or WMS. Legacy systems especially struggle to integrate with modern tech, creating bottlenecks. Start by auditing your tech stack, ensuring it’s cloud-ready (as discussed in article one!), and ensuring that APIs are accessible for smooth data flow. Software vendors want to work with you to help develop use cases that can be sold and replicated to others, so lean on them or third-party integrators to bridge gaps. Focus on creating clean, efficient connections. Seamless integration consistently outperforms a clunky, forced fit. 

Strategies for Success 

Overcoming these challenges isn’t luck; it’s strategy. Be sure to make the following moves: 

• Tie every decision to your business goals: growth, efficiency and customer wins. 
• Use a selection partner to dodge vendor bias and nail the right fit.  
• Build a phased plan: Pilot AI at a pain point, measure results and roll it wider.  
• Invest in your people: Training’s not a cost; it’s a multiplier.  
• Keep integration tight, such as testing connections early and often. 

Take Tom, the AR clerk we met in parts two and three. His company implemented AI to automate invoice matching, but it only worked because they took the time to train him properly on the new system. With the grunt work handled, Tom focused on resolving complex payment disputes, boosting customer satisfaction and cutting resolution times by 25%. Proper planning and training turned a potential headache into a win. Patience pays. 

The Bottom Line 

AI in distribution can transform how you operate, resulting in faster picks, leaner stock and happier customers. But it’s not plug-and-play. Fail to plan, and you’re courting disaster — the project could easily come in over budget, overdue and underdelivering. Plan smart, tackle the big three (cost, training, integration) and you’ll turn challenges into wins. This isn’t just about surviving AI; it’s about thriving with it.  

Where does your operation stand? Share your hurdles below, and let’s crack them together. 

Also in this series: 

• Part 1: How to Unlock the Full Potential of Your Existing Technology in Wholesale Distribution, which encourages distributors to properly leverage the tech they already have before looking to upgrade. 
• Part 2: Preparing Your Operation to Harness the Benefits of AI in Wholesale Distribution, which covers the basics of getting ready for AI, including digital strategy, cloud migration and data hygiene. 
• Part 3: AI is Actively Transforming Warehouse Operations and Inventory Management, which looks at analytics, automation and robotics, with real-world examples. 
• Part 5: The Future of AI in Wholesale Distribution: What’s Next and How to Stay Ahead, which explores emerging trends such as supply chain visibility and last-mile delivery. 

Will Quinn

With over 25 years of leadership in supply chain, logistics and global distribution strategy, Will Quinn is a recognized authority in warehousing and distribution operations. A U.S. Marine Corps veteran, he spent 12 years mastering discipline, adaptability and leadership — qualities that have fueled his success in managing high-impact distribution networks for companies like Grainger, Coca-Cola, MSC Industrial Supply, WEG Electric and Cintas. As a former global distribution strategist at Infor, he spent four years helping businesses bridge the gap between cutting-edge technology and real-world distribution challenges. Will holds a Master of Science in Supply Chain Management from Elmhurst University.