AI in Manufacturing: Do’s and Don’ts for Smart Adoption

Artificial intelligence (AI), especially Generative AI (GenAI), is creating significant buzz in manufacturing, with many companies eager to invest.

According to Rockwell Automation’s 9th Annual State of Smart Manufacturing Report, 83% of companies plan to implement AI this year, making it the top area for investment in the next 12 months.

As a manufacturing leader, you’re likely exploring AI’s potential to transform your operations. But with so much hype, how do you separate reality from fiction?

In our latest article, “AI in Manufacturing: Do’s and Don’ts for Smart Adoption,” TBM’s CEO, Bill Remy, and Board of Directors member, Gary Freburger, provide practical advice on successfully integrating AI into manufacturing while steering clear of common pitfalls, including the danger of overconfidence.

In this article, you’ll discover:

How to set realistic expectations for AI implementation
Strategies to leverage your existing data effectively
Ways to balance AI capabilities with human expertise

The AI Journey: From Small Steps to Big Wins in Manufacturing

AI is a powerful tool for enhancing decision-making in manufacturing, but it’s not a replacement for human expertise and execution. It can identify improvement opportunities, but its effectiveness hinges on the quality and organization of data. Poorly managed data leads to unreliable AI analytics, making proper data management essential. To ensure successful AI adoption, manufacturers should take a phased “crawl, walk, run” approach—starting with basic analytics and performance metrics, then gradually scaling AI once a solid foundation has been built and its value proven.

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Frequently Asked Questions

Why should manufacturers be cautious when adopting AI technologies?

Manufacturers should be cautious because AI is not a plug‑and‑play solution and can amplify existing problems if underlying processes and data are weak. The article emphasizes that adopting AI without clear use cases, reliable data, and operational discipline often leads to disappointing results. Successful AI adoption requires understanding where AI adds value and ensuring the organization is operationally ready before scaling technology investments.

What are the key “do’s” for smart AI adoption in manufacturing?

One of the most important “do’s” is to start with clearly defined business problems rather than technology for technology’s sake. The article highlights the importance of grounding AI initiatives in operational reality, ensuring data accuracy, and integrating AI into existing management systems. When AI supports decision‑making at the front line and reinforces disciplined execution, it becomes a practical tool rather than an abstract analytics exercise.

What mistakes should manufacturers avoid when implementing AI?

Manufacturers should avoid treating AI as a replacement for sound management, process stability, or human judgment. The article cautions against over‑reliance on complex models without transparency, ignoring change management, and deploying AI in environments where basic operational fundamentals are not in place. These missteps can reduce trust, slow adoption, and prevent AI from delivering meaningful business impact.

Meet the Expert

Bill Remy

Chief Executive Officer & Board MemberEmail Bill

Bill Remy is the CEO of TBM Consulting Group and serves on the TBM Board of Directors. His career expertise includes deep knowledge of operational performance improvement, site transitions, acquisition integration, new product development and supply chain management.