From Hype to Habit: Why AI Adoption Still Stalls—and What Actually Works
In my last post, I shared a reflection that resonated: AI is still sitting at the kids’ table.
After a dinner conversation with marketing and IT leaders from some of Chicago’s largest companies, it was clear: everyone was experimenting with AI -- but few were moving beyond isolated pilots. The tech was present, but trust, integration, and impact were still lagging.
So the next question is: Why?
And what separates companies that dabble in AI from those that actually scale it?
The Real Problem: It’s Not the Tech—It’s the Trust
Most employees don't adopt what they don't understand. If AI feels like a bolt-on tool -- or worse, a threat -- it won't gain traction, no matter how good the tech is. I've seen firsthand that the bottleneck isn’t capability, it's comfort. Most of your workforce doesn’t need more demos -- they need clarity, context, and confidence.
That’s where the idea of an AI Coach comes in.
What Is an AI Coach?
I’m not talking about someone who trains the models.
I’m talking about someone who helps the humans:
- Guides employees in how to use AI in their daily workflows
- Translates complex concepts into actionable tips
- Builds bridges between experimentation and execution
And no, your AI Coach doesn’t need to be a developer -- or someone obsessed with terms like Vertex AI, Snowflake, or orchestration pipelines. They need to be business-oriented, relatable, and grounded in the reality of how people actually work.
Early Adopters Still Matter -- Just Don’t Overcomplicate It
One of the more interesting comments I received on my last post suggested that internal AI adoption could be dramatically improved by applying psychographic segmentation -- essentially segmenting employees the same way we segment customers.
The strategy isn’t wrong. In fact, it sounds a lot like Crossing the Chasm -- start with the right early adopters, build momentum, and let influence drive the early majority.
I’ve seen this work over and over: with franchise owners, car dealers, insurance agents, even in the early BEV Model e pilot at Ford. The key wasn’t a fancy segmentation model. It was picking stakeholders who were open to experimentation -- and respected by their peers.
So yes, start with the right people. Just don’t over-engineer it.
What About the Agentic AI Hype?
We can’t ignore the noise coming from vendors. Salesforce’s Marc Benioff recently said he might be the last CEO to manage only humans. He cited their new Agentforce product as having autonomously resolved 380,000 support cases with an 84% resolution rate.
Impressive? Absolutely. But let’s be clear: Agentforce is operating inside a highly structured, narrow use case.
It’s not orchestrating multi-touch marketing journeys or running GTM strategy. It’s automating help desk flows -- and doing it well.
Qualtrics is making similar claims, but so far, most examples remain confined to polished demos and pilot environments. The leap from structured support automation to full-scale AI-led customer experience? We’re not there yet.
The Real Work Ahead
- AI adoption will continue to stall if it’s treated as separate from day-to-day work
- Employees need enablement, not just access
- Early wins come from clear value, not just cool tech
- Start with people who are motivated and credible—not just available
Whether you call them “AI coaches,” “Swiss Army Knife talent,” or just smart operators, the people who bridge tech and workflow are the ones who’ll make AI adoption stick.
What’s Next
In my next post, I’ll dig into the idea of agentic AI vs. structured automation, with examples of where AI is gaining real traction today -- and where the hype still outpaces the results.
In the meantime, I’d love to hear how your org is navigating this shift. Are you embedding AI into everyday work? Or are your teams still stuck in pilot mode?
Let’s keep the conversation going.
Stay tuned at blog.mikehotz.com, or follow me on LinkedIn to get it first.
