There are “fear of technology” conversations happening across organizations right now and they don’t always make it into the boardroom.
It's the line worker wondering if the machine learning system being installed means his role disappears. It's the experienced supervisor worried that AI can analyze a production floor faster than she can walk it. And the manager being asked to lead a transformation he doesn't fully understand. And a team watching his every signal to figure out how worried they too should be.
The biggest issue is what leadership in the age of AI means for teams and if organizations will address that honestly.
The Fear Beneath the Surface
A recent Pew Research Center survey found 71% of adults believe AI will result in fewer jobs over the next 20 years. Whether that figure turns out to be accurate is almost beside the point. Most people walking into work every day are carrying some version of that belief with them. And it shapes how they respond to everything else. When people are afraid, they don't engage openly with change. They protect themselves, withhold discretionary effort, and interpret every new announcement through the lens of what it might mean for their job security.
Leadership that ignores that fear, or who assume that explaining the technology will address it, are solving the wrong problem.
What people need first is not a tutorial on AI capabilities. They need honest, direct conversations. To know what is changing and why. How jobs and processes will evolve. What work AI will take off their plates, and how the organization will build the skills they'll need. This transparency is the foundation of psychological safety, and it is especially true in manufacturing.
Much of the work we do at Glass of Learning is focused on Industry 4.0 , the integration of new technologies in production environments and the influence of leadership. Bringing AI into manufacturing doesn't automatically make the transformation smooth or straightforward. The goal is never to make the frontline workforce obsolete, but rather to make it more effective. AI can help manufacturers predict problems before they occur. It can automate routine processes, improve maintenance and quality, and improve outcomes. But none of that happens if the people responsible for operating in that environment feel threatened rather than supported.
Rethinking Work, Not Just Automating It
One of the most important reframes available to leaders right now is this: AI gives us an opportunity to fix complexity, not just automate it.
Organizations have spent decades layering processes, systems, and bureaucracy on top of one another — often in response to real problems. Over time though, they’ve created operational weight that slows everything down. When the instinct in difficult moments has been to cut people while leaving the dysfunctional systems, the result is a leaner headcount doing the same broken work. Applying AI without clarity will produce the same outcome. It will make teams faster at doing the wrong things, rather than prompting them to ask whether the things themselves need to change.
The strategic leaders will use the arrival of AI as an invitation to go back to fundamentals:
- Set clear expectations.
- Coach people to standards.
- Hold people accountable.
- Have the difficult conversations that build rather than damage trust.
- Lead with emotional intelligence in a moment when people are genuinely uncertain about what the future holds for them.
These are not new leadership principles, but they become more important when the environment is shifting rapidly and people are looking to their leaders to make sense of what's happening.
Giving People Capacity for Better Work
AI can take on routine tasks and free up the employees around it for judgment, creativity, problem-solving, and collaboration. It can be especially significant in manufacturing where every minute of unplanned downtime can cost thousands of dollars. AI can analyze enormous volumes of equipment and maintenance data, detect early warning signs, predict failures, and optimize maintenance timing in ways that would be impossible for a person to do manually at that scale. But that doesn’t mean that the employee becomes unnecessary, it's that their role evolves. So, instead of spending the majority of their time reacting to problems, they can focus on preventing them, solving higher-value issues, and improving the operation over time.
It's not unlike what we teach leaders about delegation. You delegate the lower-value work so you can focus on the work where your judgment and leadership matter most. AI can become another form of delegation.
AI Has its Limits
It's worth being clear-eyed about the limits. While AI is powerful, it still gets things wrong.
I experienced a memorable example of this recently with an AI-generated meeting summary from Zoom. In a personal call, I mentioned to a colleague that my cat had chewed his way out of his carrier during a car ride. The next day, on a completely separate call with a senior client - a conversation in which my cat was never mentioned - the AI summary somehow referenced someone "chewing their way out of a box." It's a funny story, but it makes an important point: AI does not replace human judgment.
We still need people who can question, interpret, and validate what the technology produces. Knowing when to trust it and when to push back is itself a skill and developing that skill across a workforce is part of what leadership development needs to address right now.
The Conversation Leadership Needs to Be Having
The most important conversations leader should be having right now should be focused on the people – not the technology.
It’s where a leader sits down with a team member and says: what could your role look like if AI took away the most repetitive parts of your job? What would you do with that capacity? What skills are you going to need to develop, and how can we support you in building them?
Leaders also need to become myth busters. When information is absent, people fill the gap on their own and the stories they tell themselves in the absence of clear communication are almost always more alarming than the reality. A message like "we're using AI to improve efficiency" lands very differently when someone's primary fear is job loss. It sounds, to that person, like a polite way of saying headcount is coming down.
Instead, the conversation that builds understanding and trust goes deeper:
What are we trying to accomplish?
Why does it matter?
And how can this technology help us invest more in our people?
Explaining the what and the why before the how makes people engage with change rather than resist it.
Explore more on how Glass of Learning supports leadership through change: The Risks of Not Investing in Virtual Training and Development
