Manufacturing is under pressure from all sides, from tariffs to recession worries to extreme competition. But right here, right now, leaders across the industry are rising to the occasion and investigating every advantage technology offers. I’ve seen it firsthand — both through data and in between the trendlines.
My colleagues at Rockwell Automation set aside time each year to survey thousands of manufacturing professionals about their experiences with and uses of smart technology. What’s working? What isn’t? Which internal and external factors are motivating their changes? It’s a process I’m proud to support, and I always look forward to comparing this quantitative data against the qualitative data I’ve gained through decades of conversations in the field. I worked as an industry consultant helping customers apply solutions to solve problems in my earlier career. Now as a business unit leader, I talk often with leaders looking to the future and making sure we are aligned. This almost always results in discussions about what future trends are likely, how manufacturing will evolve and how we can jointly make the best business decisions possible to be prepared and reduce risks.
When I reviewed the data for our 10th survey, I saw an industry caught between a constellation of rocks and hard places. People alone cannot match the hour-by-hour volatility of current economic conditions or keep up with the cybersecurity arms race that leaves supply chains vulnerable, and 81% confirmed that these pressures (internal and external) are accelerating their digital transformation timelines. This makes sense. Manufacturers need to fill gaps. However, they also need to push to beat their competition to the AI use cases that will generate current and future value — whether that’s mass adoption of physical AI on the factory floor or pragmatic quality control. And surrounding it all, an industry-wide resistance to change.
Manufacturing needs AI — But they’re still figuring out where and how
Manufacturing leaders are almost unanimously adopting AI — our survey this year found that 95% of respondents are turning to the technology. This doesn’t surprise me based on what I’ve seen firsthand, but I was excited to see established use cases from our research last year turning into best practices. Notably, AI-powered quality control is changing manufacturing. Nearly half of the respondents (48%) plan to deploy this use case. In the field, I see the impact human error can have on quality control, especially in situations like our current trade conditions. Manufacturers now must quickly adjust where and when things are made, and that means new processes and people will come into play. That introduces opportunity for human error, leading to lower quality, so it is important to apply these AI use cases in conjunction with flexible automation solutions to ensure quality is maintained.
Our survey’s respondents also highlighted cybersecurity as a key AI use case, as manufacturing companies accounted for 21% of all ransomware attacks in 2024 — only inflation and economic growth ranked as more concerning risks among our survey’s respondents. As bad actors adopt more sophisticated tactics to deploy cyberattacks, manufacturers are realizing that they can’t have people “watching” the system for bad actors. It is just too much and too complex. They are relying more on AI to do that for them and catch things quicker. In fact, nearly half of our survey respondents indicated they plan to use AI/ML for cybersecurity over the next year.
We’re even seeing industry leaders pivoting from reactive to proactive. They’re proactively planning improvements in system hardening, patching and monitoring, and tying into current risk levels. This philosophy shift is especially noticeable in end-of-life (EOL) migrations. Historically, manufacturing EOL policy has been “since it is running, don’t touch it…” That resulted in old systems with out-of-date or obsolete parts in the critical system.
