The conversation around AI is often framed in terms of disruption. Increasingly, however, manufacturers are finding that its greatest value comes from helping people work smarter and make better use of data.
AI is becoming an extension of teams, allowing employees to make use of data in ways that weren't possible before. On the shop floor, in the office and across the supply chain, AI is becoming part of the way work gets done.
For business leaders, however, this shift brings a new set of questions. The more conversations I have with manufacturers, the clearer it becomes that the biggest challenge is no longer whether AI will become part of the workplace, but how organizations can effectively manage and govern it now that it's here.
As AI becomes more involved in daily workflows, leaders will need to balance automation with human judgment, empowering employees to act while maintaining clear accountability for outcomes.
Decision-making is already shifting closer to the employees doing the work. As AI becomes embedded in enterprise software, teams will have access to real-time insights, recommendations, forecasts and automated workflows that can help them make faster, more informed decisions without relying on multiple layers of management.
This shift does not mean organizations will need fewer managers or less oversight. Instead, leadership will still need to evolve. The challenge is no longer simply to make decisions faster, but to help employees make better decisions aligned with business priorities and customer expectations.
That distinction matters. Technology only becomes part of the team when employees trust it enough to use it naturally. And that trust doesn't happen overnight.
Trust and Data
Employees won’t trust AI immediately. Building familiarity with the tools will take time.
How AI is introduced matters just as much as the technology itself. You can’t assume incorporating AI will be as straightforward as adding another tool to your existing system. Employees may need to adjust to new capabilities, new ways of interacting with information, and, in some cases, new workflows.
The transition will be faster and smoother if you embed AI into existing workflows that employees already use. Then experience remains familiar, even as they wind up interacting with the data in different ways.
AI's deployment will also make organizations face the quality of their data. Trust in AI starts with trust in the underlying information. The existence of duplicate records, inconsistent information, and other long-standing data problems will become harder to ignore when it influences automated recommendations.
Before companies can rely on AI's recommendations, they first need confidence that the data feeding it is accurate. That's why human oversight remains so important, particularly during the early stages of adoption. Employees need to validate both the data and AI's recommendations before they become comfortable relying on either.
AI also should not become a substitute for interactions between people. Your team will always be your organization's most important asset, and conversations with employees should remain human-led, especially when they involve organizational changes or other sensitive decisions.
Human Judgement
Critical thinking becomes especially important when employees are asked to approve AI recommendations. They need to unpack a recommendation and ask why the AI is surfacing it. What kinds of insights can we get from the data? Why are we seeing this recommendation? Does it really make sense?
Blindly following a recommendation is dangerous. We need frontline workers and business leaders with the wherewithal to use their own judgment to decide when to trust the recommendation or question it.
Experience and expertise remain essential because AI is not perfect, and consequential business decisions can still produce an unintended negative outcome. People need to ask what has been done before, who is involved, and whether going down a particular path makes sense.
That will become especially important when an AI recommendation conflicts with the instincts of an employee who has been doing the job for 25 years.
The answer shouldn’t be to automatically favor one over the other. AI may surface patterns or options that people haven’t considered, while an experienced employee brings years of operational knowledge and context. If that employee questions the recommendation, there may be a very good reason. Perhaps the AI isn’t configured correctly. Perhaps the data is missing something important. Or perhaps that feedback identifies an opportunity to improve the system itself.
That kind of interaction is exactly what organizations should want. Managers need to be willing to work with employees side by side, helping them make the right decisions based on the AI’s recommendations. It can’t be something that a manager simply turns on and expects employees to use. Managers need to be ready to have these conversations and to take accountability if the decisions they make alongside their team members have a positive or negative impact.
Consider what that might look like in practice. An AI system may recommend switching materials because one factory is out of stock. The alternatives might include moving the work to another factory that has the same material or using a lower-cost substitute at the existing plant.
Each choice could affect cost, sustainability and the delivery schedule. The manager and employee still need to decide which trade-off makes the most sense for the business and the customer.
This is also why a phased approach is so important. If an organization runs full steam ahead without understanding the basics, it might be in trouble. The most effective way to build confidence is to start with focused use cases, learn from them and expand as employees gain experience.
Start in a smaller part of the business, where the risk is lower. Employees can learn how best to interact with the AI, how to optimize its recommendations and how to ensure the right data and privacy controls are in place.
Companies should also be clear about the outcomes they want to achieve and communicate those goals to employees. If people understand the value AI is expected to deliver and feel that they are part of the change, they are far more likely to help the organization succeed.
The way we are transforming our businesses with AI is new for all of us. Leaders need to stay close to their teams, communicate clearly and create the conditions for people to use AI with confidence. The real opportunity is not AI making decisions for us but helping people make better ones.
