Most AI projects crash because companies obsess over tech instead of understanding their teams. After 30+ years in the field, I’ve learned the secret isn’t better algorithms.
The story
Most AI implementations fail because companies focus on the technology instead of the people using it.
I learned this during my 30+ years in tech. You can build sophisticated automation, but if your team sees it as another hurdle instead of a helpful tool, you’ve wasted everyone’s time.
The breakthrough came when I started applying cognitive empathy to AI design. Instead of asking “what can this technology do,” I ask “what does this team actually need, and how do they naturally work?”
Take my recent client. They were drowning during peak season – response times were brutal, and their team was burning out on repetitive tasks. Rather than throwing complex AI at the problem, I designed workflows that felt like extensions of their existing processes.
Result? Response times dropped 60% and they saved 25 hours weekly. But here’s what really mattered – their team actually wanted to use the system.
This player-coach approach separates effective AI consulting from expensive tech demos. I don’t just build and disappear. I work alongside development teams, turning technical complexity into business results that stick.
After decades of writing code and training my own AI chatbot since 2018, I’ve learned that successful automation isn’t about replacing humans – it’s about amplifying what they do best.
The companies winning with AI aren’t the ones with the fanciest tools. They’re the ones that understand their people first, then build technology that serves them.
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