The promise of artificial intelligence transforming business operations has collided with harsh reality in the distribution industry. While 73% of companies expected AI to deliver at least 2% pricing and margin improvements, only 16% actually achieved those results. The gap isn’t just disappointing—it’s a textbook example of why throwing technology at broken processes creates automated chaos instead of operational excellence.
The Automation Trap: When AI Amplifies Problems
Jenni Detert, vice president of information technology at Endries International, learned this lesson the hard way. Her company implemented AI to automate purchase order tracking, replacing human follow-ups with automated emails and AI-powered response processing. The result? “We had just kind of automated chaos,” she admitted during the Shift | The Future of Distribution conference in Denver.
This mirrors a pattern seen throughout industrial history. During the early days of assembly line manufacturing in the 1910s, companies that simply mechanized existing inefficient processes often made things worse. Henry Ford’s success came not from automating existing car-building methods, but from completely reimagining the process first. The same principle applies to AI implementation today.
“weekend reflections ✍️ it’s been 2 weeks since I started building x402Books AI. tbh, it has been one of the best and hardest weeks of my journey so far. building the product is easy. distribution is the real challenge.” — @danbuildss
This developer’s insight captures a fundamental truth: building AI solutions is straightforward, but effective implementation and distribution remain the real challenges.
The Root Cause Problem: Process Before Technology
Detert’s experience highlights a critical mistake: automating existing workflows without understanding why those workflows exist. Instead of simply digitizing purchase order chaos, Endries should have asked deeper questions:
- Why are purchase orders getting lost in the first place?
- Should order control processes be fundamentally different?
- How can customer forecasting be improved upstream?
This mirrors the lean manufacturing revolution of the 1980s, when companies discovered that automating wasteful processes simply created faster waste. Toyota’s success came from eliminating waste first, then applying technology to optimized processes.
Patti Rausch, vice president of research and innovation at the National Association of Wholesaler-Distributors (NAW), emphasizes that successful AI adoption requires treating change management as seriously as vendor selection. The 16% of companies hitting their AI targets didn’t just buy tools—they built organizational capabilities around those tools.
Smart Applications vs. Shiny Objects
Not all AI applications are created equal. Rausch points out that flashy features like real-time route optimization miss the mark for distribution companies. You can’t instruct a driver to skip stop two to reach stop seven just because an algorithm detects better traffic—delivery trucks are loaded based on stop sequence, and asking drivers to dig through cargo mid-route creates safety hazards.
Instead, successful AI applications focus on upstream intelligence:
- Predictive maintenance: AI tracks machinery like forklift brakes before they fail
- Safety monitoring: Wearable technology in hard hats analyzes real-time data to prevent accidents
- Warehouse automation: Robots retrieve products so humans don’t have to navigate dangerous environments
- Route planning: Using weather patterns, traffic data, and historical delivery times to optimize before trucks leave
These applications work because they enhance human decision-making rather than trying to replace human judgment in complex situations.
The Economic Reality Behind AI Investment
Despite implementation challenges, the distribution industry continues growing, reaching an estimated $8.7 trillion market in 2024, up from $8 trillion the previous year. Eric Hoplin, NAW president, credits recent tax legislation that incentivized business investment in research and development. Between July and November 2024, corporate income tax revenue dropped by one-third or $52 billion, according to U.S. Treasury data reported by The New York Times.
This economic environment has created a paradox: companies have more capital to invest in AI, but many lack the organizational maturity to deploy it effectively. It’s reminiscent of the dot-com boom, when abundant capital led to rushed technology deployments without proper strategic planning.
“One question though: what’s the real entry point into these markets before OpenAI/Google absorb them? For example with AI employees what’s the actual moat: data, distribution, workflow lock-in, or community?” — @zoiroff77
This question reveals another layer of complexity: companies aren’t just competing with implementation challenges, but also with the risk that large tech companies will commoditize AI capabilities before smaller players can establish competitive advantages.
Building AI Capabilities, Not Just Buying Tools
The 54% of distribution companies without AI on their roadmap aren’t necessarily making a mistake. Rausch argues that the expectation-reality gap represents timing rather than value. Companies that started earlier, built organizational habits around technology, and focused on change management are seeing results.
Successful AI implementation requires:
- Process analysis first: Understanding why current systems fail before automating them
- Change management: Training staff and adjusting workflows around new capabilities
- Incremental deployment: Starting with proven applications rather than experimental features
- Upstream thinking: Solving problems before they cascade downstream
- Safety integration: Using AI to enhance worker protection rather than replacing human judgment
This methodical approach mirrors how Southwest Airlines built competitive advantages through operational excellence rather than flashy technology. They automated and optimized ruthlessly, but only after perfecting their underlying processes.
The Path Forward: Learning from Early Adopters
The distribution industry’s AI journey is still in early stages, but patterns are emerging. Companies succeeding with AI share common characteristics: they treat technology as a capability-building exercise rather than a vendor relationship, they prioritize process understanding over automation speed, and they focus on enhancing human decision-making rather than replacing it.
The real lesson from Denver’s distribution conference isn’t that AI fails—it’s that successful AI requires the same disciplined approach that built great companies before artificial intelligence existed. Understanding your processes, investing in your people, and solving real problems will always trump automating chaos, regardless of how sophisticated the technology becomes.
Published in Stream · Dispatch #338 · May 16, 2026 · 5 min read.
Reply to paolo@mont3.ch - every email gets a human answer within 24h.