Incorporating the many AI tools available in the marketplace is a hot topic in nearly every organization.
Leaders are faced with the challenge of persuading team members to learn about and embrace AI for its many obvious advantages.
In many workplaces, this advocacy and AI adoption aren’t going so well.
The pace of change and the nearly limitless possibilities of the new technology can be overwhelming. But as it turns out, leaders are making this harder by how they speak about AI.
Recent research from the Harvard Business School suggests a direct parallel between how leaders talk about AI and whether team members are receptive to integrating it into their daily tasks. Let’s explore why.
According to the study of over 2,300 workers, people resist AI for two types of reasons: performance-based and principle-based.
When it comes to performance, people worry AI will not actually do the job well enough, given potential errors, quality standards, and technical limits. People doubt the reliability and technical feasibility of using current AI models to perform essential tasks.
As AI develops and gets better, the thought is that many of these concerns will fade.
Principle-based objections are a whole other story.
Some team members have a “moral repugnance” toward AI, viewing the use of the tools in some contexts as inherently wrong, regardless of how capable or cost-effective the technology is.
This moral objection is deeply rooted in beliefs about human dignity.
For instance, allowing AI to replace humans in caregiving, child education, or intimate relationships was seen in the study as morally bankrupt.
When people object on principle, no amount of improved capability changes their mind.
Now let’s return to how leaders talk about AI integration. When leaders frame AI in terms of “efficiency gains and cost savings,” team members hear “replacement.”
It doesn’t matter if that’s not the intent. The language does the work.
And once people conclude that AI is being deployed against their interests rather than in support of them, resistance hardens.
The HBS study found 94 percent of the respondents support AI as a tool that helps humans do their jobs better. This shows that the appetite for augmentation is overwhelmingly positive, but that the appetite for replacement is just the opposite.
The practical advice for leaders is to examine their messages and strip out words like “efficiency,” “cost-savings,” “automation,” and “substitute” as lead messages.
Replace them with what the tools actually enable people to do: work they couldn’t do before, problems that can be solved faster, and decisions that can be made with better data.
The distinction between “AI will handle this for you” and “AI will help you do this better” may sound like semantics, but it is huge.
It’s the difference between support and resistance.
Leaders don’t just deploy technology. They shape how people experience it.
Get the framing wrong, and you’ll spend months managing resistance that better language would have prevented.
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