Too many AI implementations fail. Not because the technology was wrong. Not because the configuration was bad. Because nobody actually used it.
Here's a story that happens more often than I'd like to admit:
Company invests $50,000 in AI implementation. Consultants do their work, everything is configured correctly. The system launches. Marketing leadership sends an enthusiastic email. And then... nobody changes how they work.
The sales team keeps qualifying leads the old way because "that's what I know." The content team ignores the AI writing assistant because "it doesn't sound like us." The customer service team routes around the chatbot because "customers prefer talking to humans."
Six months later, leadership looks at the numbers and sees no improvement. The AI project is declared a failure. But the technology was never the problem—adoption was.
This is why I spend as much time thinking about the human side of AI implementation as the technical side. How do we make people feel safe trying something new? How do we demonstrate early wins so skeptics become believers? How do we identify the champions who can influence their peers?
Here's what actually works:
Start with volunteers. Don't force AI on reluctant teams. Find the early adopters who are genuinely curious and let them prove value first.
Make it safe to fail. If people are terrified of making mistakes with AI, they won't experiment. Create environments where trying and failing is acceptable.
Show, don't tell. Nobody cares about theoretical benefits. They care about "this saved me two hours yesterday." Find quick wins and make them visible.
Address fears directly. If someone's worried about AI replacing their job, saying "that's not going to happen" isn't enough. Have real conversations about how their role will evolve.
The technical work of AI implementation typically takes weeks. The adoption work takes months. But if you skip the adoption work, the technical work is wasted.