90% Say Digital Is Critical. Only 32% Use Their Data Well. See what Rockwell’s report reveals.

By TheCyberStreet News Desk

For the people running production lines, quality checks and cybersecurity, digital transformation promises visibility and faster decisions. 

But buying technology is only the start. Teams also need data they can trust, systems they can protect and clear rules for using artificial intelligence (AI).

New research from Rockwell Automation shows how wide that gap is. 

Of the life sciences manufacturers surveyed, 90% call digital transformation business-critical, yet only 58% have introduced smart manufacturing technologies in some or all their operations. The findings come from 104 managers and executives in 16 countries, across manufacturers, equipment suppliers, system integrators and engineering procurement firms. They are part of Rockwell’s State of Smart Manufacturing research, conducted with Sapio Research.

Lots of data, little use

The most striking number is about information. Only 32% of respondents say they effectively use more than half of the data they collect. Even so, among companies already using AI, 64% plan to expand it within a year.

This raises an obvious question. How can employees rely on AI when the data underneath is scattered or hard to interpret? 

Rockwell’s answer is to connect and govern operational data first, so analytics and validated AI applications have something solid to stand on.

A growing security risk

Security adds to the pressure. More than half of the organisations surveyed (54%) reported at least one cyberattack in the past year. 

The exposure runs across enterprise networks, remote access, connected equipment and the points where business IT meets factory-floor technology.

Readiness as a daily habit

Rockwell’s central argument is that readiness should be part of everyday operations, not something companies scramble to prove before a regulatory review. Matt Weaver, the company’s vice president for life sciences, describes it as continuously connecting data, securing systems and validating AI. The report advises starting AI with defined, lower-risk uses that assist engineering teams while keeping people in charge.

The takeaway is about coordination rather than shopping. Production, quality, engineering and business teams need connected workflows and information they can trust. The next challenge, Rockwell suggests, is getting existing investments to work together, so people can decide with confidence and keep operations dependable.