Kolby Beck, Senior Solution Architect at Kimball Equipment Company, on why the rush to adopt AI often exposes significant structural issues for enterprises.
After decades of collecting poor-quality data, most systems are too bloated and unreliable to produce meaningful results.
He cautions against the “endless pursuit of useless information,” noting how often it leads to user burnout and tech fatigue, causing employees to disengage from the very systems meant to help them.
Without a “back-to-basics” focus on first principles and user adoption, Beck says organizations risk wasting significant resources on complex tools that no one uses.
Decades of over-collecting and under-cleaning data have left most organizations with a vast collection of dashboards and fields that nobody trusts. Until recently, most leaders incorrectly assumed that more data meant more visibility. But with the explosion of artificial intelligence, the truth is becoming increasingly impossible to ignore. Even as businesses continue to implement “AI for the sake of AI”, experts are discovering that success often depends more on reducing noise and fixing broken foundations than it does on shinier features.
One of those experts is Kolby Beck, a Senior Solution Architect at Kimball Equipment Company, North America’s largest aggregate equipment provider. Having spent years as a consultant helping countless organizations navigate complex data challenges, Beck now applies his learnings in a distinctly non-tech, industrial setting. But with experience that spans both the corporate hype cycle and on-the-ground reality, he offers a unique perspective. According to Beck, the industry’s AI obsession is a fundamental, first-principles problem.
The leaves from the trees: At some point, Beck says, enterprises made a collective, strategic decision to hoard information in the hope of finding gold. “For years, the mantra was analytics, analytics, analytics,” Beck explains. But the result was the exact opposite: systems so bloated and demanding that they created their own failure. From Beck’s perspective, AI is a first principles problem. “Everyone’s looking at the leaves, and they’re all shiny and cool, but they’re forgetting that the roots might be lacking.”
Without a solid understanding of their own foundations, Beck says, leaders often turn to AI as a magical solution for far simpler problems. “There’s a ton of opportunity to help businesses with their data. I think there will be plenty of job and career opportunities for non-experts, too. They might want to use AI, but the foundation must be built first.”
AI for the sake of AI: Beck shares a story about a client that perfectly illustrates this disconnect. “A client brought me in and said, ‘We want to use AI to answer these questions.’ As we were going through it, I asked, ‘Wait a second, what are you trying to ask?'” Beck recalls. “What they actually needed to know was, for this Head Start program, ‘How many children are enrolled?’ My rebuttal is typically, ‘Why do you need AI to tell you that? That’s right here in your CRM.’ The actual issue this company was struggling with was training on how to use their current software. AI wasn’t even the answer. So for me, the discussion always comes back to: what is the actual problem?”
But the cycle of adding new tools on top of broken processes isn’t always so benign. In some contexts, it can also have a profound human cost. The constant push for more data, more dashboards, and now more AI has led to widespread “tech fatigue,” where users are so overwhelmed they disengage. Beck points to another example that reveals the state of AI overload in the healthcare industry.
A crisis of data overload: At a recent tech conference Beck attended, he was struck by the amount of “dashboard fatigue” his peers are experiencing. “There’s fatigue from information and overload, and it’s driving a desire to go back to the basics, which is exactly why I started working for a company that makes gravel. That’s about as basic as you can get. Back to basics. First principles.”
Worth the AI squeeze: Beck offers a simple framework for making the go/no-go decision, grounding it in an honest assessment of an organization’s ability to handle change. “Is the juice worth the squeeze? Is it worth our time? If I spend $100,000 building this really cool thing, but I’ve already made the mistake of not getting my users to use something simple, are they going to use this big, new thing? I’ve built apps for companies that spent a lot of money on the tool itself, but didn’t do any change management or follow-up. Eventually, after they’ve wasted $50,000-100,000 on an app that no one uses, it just fizzles out.”
Ultimately, Beck says, even if an organization manages to clean its data and identify a legitimate use case for AI, the entire project can still fail at the final hurdle. The make-or-break factor, Beck warns, often has little to do with the technology itself and everything to do with people.
