GenAI Adoption Is A Long-Term Commitment, Not A Simple Switch

There’s been no shortage of attention on AI these past two years—especially generative AI. Some teams already use it every day to draft messages, summarize research, explore code or outline ideas. Others are just now figuring where it fits. That range is normal.

What’s not realistic is treating AI like something you “turn on” and immediately see results from. That mindset is where most initiatives go wrong.

Practical AI adoption—in manufacturing or any other industry—is less about getting access to a model and more about making thoughtful, steady progress toward specific outcomes. That takes time, structure and commitment.

Start With Intent, Not Only Excitement

The question isn’t, “How fast can we use AI?” It’s, “Why are we using it?”

Clear intent matters because AI can play very different roles depending on the use case. Are you trying to speed up reporting? Help teams troubleshoot faster? Improve forecasting? Automate repetitive tasks? Assist with content development? Those are all valid—but they are not the same problem, and they don’t require the same level of integration, risk tolerance or oversight.

That’s why the first step is defining the outcome you’re trying to improve. Without that, AI becomes a novelty instead of an asset.

A few useful questions to ask up front:

• What specific work do we expect AI to improve or accelerate?
• What would “better” look like in practical terms—faster resolution, fewer errors, less downtime or more consistent output?
• Where, realistically, does human judgment still need to stay in the loop?

If you can’t answer those questions, you’re not ready to implement. You’re still defining the problem.

Progress Is Incremental

It’s tempting to assume that AI will lift the entire organization at once. In practice, meaningful adoption almost always starts small.

For example, use AI to:

• Analyze production data and surface likely causes of recurring issues, so engineers spend less time digging and more time fixing.
• Use AI-driven assistants to generate first-pass summaries, instructions or reports that a human then reviews and finalizes.
• Help teams interrogate historical data conversationally: “When did scrap rates spike on line three last quarter, and what changed upstream?”

These are focused, narrow applications. They don’t require you to redesign the business. They help people do their work with more context and less friction.

When that works, you expand—intentionally. You don’t scale based on hype; you scale based on proof.

Data, People And Process Must Be Ready

Even with a clear goal, AI stalls if the foundation isn’t there. The most common blockers fall into three areas: data, people and process.

Data

AI is only as useful as the data it can access and trust. If the data is incomplete, inconsistent or scattered across systems that don’t talk to each other, the output will be unreliable. Before you expect AI to generate insights or drive actions, you need clean, contextualized data and a way to get it where it needs to go.

People

The most effective AI programs are built to support people, not bypass them. That means training teams on how to use AI in their work, what to trust and where human judgment still has to lead. You’re not trying to turn everyone into a data scientist. You’re trying to make sure they’re comfortable asking questions, interpreting results and acting on them responsibly.

Process

AI layered on top of unclear or inconsistent workflows won’t create order; it will scale confusion. You need to understand how work is supposed to move, who is accountable at each decision point and where AI is allowed to assist. That structure allows you to measure impact and correct quickly if something isn’t working.

If those three pieces—data, people and process—aren’t in place, AI will generate activity, not progress.

Expect To Adjust—Continuously

Even in high-performing environments, AI is not “set it and forget it.”

Models drift. Operations evolve. Regulations change. The questions people ask of the system get more sophisticated. That means you’ll need to monitor performance, refine prompts and guardrails, update training and keep improving data quality.

This is where commitment matters. If you abandon the effort the first time the output isn’t perfect, you’ll never get the return you’re aiming for. On the other hand, if you treat early limitations as signals—whether the data is incomplete, the task is poorly defined or the team is in need more training—you can correct and move forward.

That’s how AI becomes embedded instead of experimental.

The Long View

It’s understandable that teams want fast wins. Pressure on cost, throughput, labor and responsiveness is real. But the organizations seeing meaningful results from AI share a few traits.

They are:

• Specific about what AI is for
• Involve the people doing the work, not just the people buying the software
• Building on what’s already working instead of trying to reinvent everything overnight
• Staying with it

AI can absolutely improve decision-making, accelerate analysis and reduce friction in day-to-day work. In some environments—like high-volume, highly repeatable processes—it can begin to automate whole classes of activity.

But none of that happens by accident, and none of it happens instantly.

If there’s one shift leaders should make, it’s this: stop treating AI like a switch you flip. Treat it like a capability you build. Define where it can help. Give it structure. Put people in position to use it well. Keep tuning.

That’s not the fastest path. It is the durable one.