Navigating the crunch point: Volatility and change in manufacturing

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 10^th 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.

Manufacturers are realizing this now puts them at risk. So, to get ahead of it, they need to be more proactive and work updates into their plans, so they don’t get to that bad end state and have risk. But this takes broad company support and a willingness to change the old mindset here. There’s more to do, but the impacts are already clear: faster detection, smarter patch prioritization and better recovery planning.

But that’s just the start of AI’s utility in the industry. Robotics is the next major chapter in the manufacturing industry’s AI transformation. As technologists debate the final form of physical AI, manufacturers are exploring the practical uses for these technologies now. In my work, I’ve seen manufacturers experimenting with fleets of autonomous transporters taking literal tons of goods from point A to point B, and sparing people from more dangerous tasks in production. And we’re starting to see that borne out in the data — 37% of respondents in this year’s survey indicated their organizations plan to experiment with robotics in the next 12 months. 

IT leaders in manufacturing need to recalibrate the time it takes to establish “best practice” and act quickly to keep pace with the rate of AI. So, on the one hand, that means rapid adoption and openness to piloting experimental technology.

To be an early adopter, though, you need a supportive workforce.

Championing transformation with a workforce resistant to change

The speed of change is catching up with our manufacturing workforce. 30% of our respondents this year identified resistance to change as their top leadership challenge — more than any other option we offered. This factor didn’t even make our top three in 2024. 

The industry’s continued breakneck innovation speed (23% of respondents said they lack the technology they need to outpace competition in the next year) means manufacturers are unlikely to overcome this challenge without ensuring employees directly feel the benefits. Whether it’s major adjustments — like automating dangerous tasks and retraining an employee to supervise the process — or something as simple as a chat feature to keep team members connected across the facility, this industry must ensure its workforce sees the tangible benefit of this rapid change.
 

Manufacturers need to adopt smart technologies across their enterprise, and they must provide their employees with the reassurance and training they require to keep pace. The next 12 months will be crucial to threading that needle. 

What’s coming for manufacturers in 2026? 

The competing pressures squeezing manufacturers right now are unlikely to change, but options for reacting to them might. 

As more organizations adopt emerging technologies, we’re likely to learn what meaningfully works and identify AI-powered manufacturing best practices. We already know some functions (like cybersecurity and quality control) will continue to help manufacturers navigate volatility, but I’m curious to see how else AI will evolve the industry. Manufacturers may apply it further to vision systems for quality inspection. I’m also betting that AI in robotics will continue to change how intelligent devices can improve efficiency, with AI-driven humanoid robots on the horizon.

Manufacturing runs nonstop now, and R&D will need to match pace

As best-in-practice AI use cases solidify, they’ll in turn open new opportunities to push the boundaries of smart manufacturing. Despite economic pressures, this is not the time to scale back on technology pilots, proof-of-concepts or employee skill-building.

Security isn’t just physical for manufacturers, and appropriate governance is overdue

Cybersecurity and data governance will be as important as the safety measures manufacturers already deploy on the factory floor. To facilitate this, technology leaders must establish rigorous independent governance principles, particularly amidst the fractured environment of global regulations.

Manufacturers will maintain market stability through change, but employees need to be on board

Educate employees on your smart manufacturing roadmap (and how you plan to bring them along) before they ask. We need to foster trust in the tech, transparency in the plans and ensure workers know how it helps them be more effective. If we do this, they become the advocates and will be the drivers for change. 

This means that investing in employees is a business imperative. This isn’t new to the industry — only 7% of our respondents indicated plans to hire fewer workers — but the nature of employee engagement and reskilling will need to change. Instead of simply training employees in facts and processes, we’ll have to refocus on how they can engage with technology to get what they need or do the job differently. It’s a transformation from knowing facts to truly understanding one’s domain.

It’s time to pull out all the stops

Our data this year makes our assumptions undeniable — manufacturing faces new, faster and more intense pressure from all aspects of its enterprise. To build through this challenging time, manufacturers will need every set of hands, every mind and every new tool at our disposal.
 

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