In episode 4 of his mini-series, “AI Success Starts with Leadership,” Shawn Dorward, COO Innovia Consulting, UG Expert and Microsoft MVP, tackles one of the most misunderstood obstacles in AI adoption: resistance. He makes the case that resistance isn’t a people problem, but it’s a leadership clarity problem, and the organizations that treat it that way are the ones that scale AI successfully.

The Podcast is hosted by Dynamic Communities, the home of Summit NA!

Click here for the full video or the audio file from Spotify.

Key Takeaways

• AI resistance usually isn’t loud. Most employees aren’t going to stand up in a meeting and announce that they’re resisting AI. It shows up much more quietly. People fall back into old habits. They experiment privately but don’t tell anyone what they’re learning. Leaders launch another AI initiative and the room just doesn’t respond. If you’ve invested in AI but aren’t seeing momentum, don’t assume people simply don’t care. That lack of movement is telling you something.
• More pressure won’t fix a lack of clarity. When AI adoption isn’t moving fast enough, the natural leadership response is often another deadline, another mandate, another training session, or another reminder to use the tools. But if employees don’t understand where AI is expected, where it’s optional, where they have freedom to experiment, and where human judgment still has to remain, then you don’t have a compliance problem. You have a clarity problem. Adding pressure to confusion usually just creates more resistance.
• Not all resistance is bad. Sometimes the person pushing back is actually protecting something important. They may see a quality issue you don’t. They may understand a customer risk you haven’t considered. They may be protecting the team from a process that doesn’t make sense yet. Of course, sometimes resistance really is about protecting the comfort of the status quo. Leadership’s job is to understand the difference. And you’re probably not going to discover that distinction on an adoption dashboard. It requires conversation.
• Make uncertainty something people are allowed to talk about. Ask employees what feels unclear, uncomfortable, or uncertain about AI. Ask what they’re worried about. Ask what they don’t trust yet. Ask where they’re afraid of getting something wrong. When people can say those things out loud, leaders can actually do something with the answers. Resistance spoken out loud becomes data. Resistance left unspoken eventually becomes culture. And culture is a whole lot harder to change.
• Psychological safety is part of AI readiness. AI adoption isn’t purely a technology change. For a lot of people, it challenges how they’ve worked for years, what they’re good at, and sometimes even how they think about their own value. Organizations need to create room for people to learn publicly, get things wrong, try again, and admit when they don’t understand something. When employees stop feeling like they have to protect themselves from AI, they can start figuring out how to use it.
• Build readiness intentionally. You don’t create AI readiness by simply buying licenses and scheduling training. You create it through clear expectations, safe experimentation, visible leadership support, honest conversations, and permission to learn. When those things exist, readiness starts to spread—and adoption follows.

The big takeaway for me: Don’t immediately try to eliminate AI resistance. Listen to it. It may be telling you exactly what’s missing from your AI strategy. And when people feel safe enough to tell you what’s holding them back, you’ve got something you can actually fix.

Click below to listen to the Spotify audio version

Thanks for reading!

Shawn Dorward

Microsoft MVP, Business Applications

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