With the advent of artificial intelligence (AI), life sciences manufacturers recognize now more than ever the importance of digital maturity. However, many companies’ digital strategies are still developing alongside their adoption of these rapidly growing technologies, and even those who are actively implementing AI, automation, and connected systems are facing challenges about integrating these platforms into their operations. 

A report from Rockwell Automation, The New Operating Model for Life Sciences Manufacturing: Moving from Compliance to Continuous Readiness, found that while more than half of manufacturers (58%) have already deployed smart manufacturing technologies — either at scale or across portions of their operations — many are also navigating common challenges around data accessibility, system integration, governance, and operational complexity. 

While 90% of life sciences manufacturers say digital transformation is now business-critical, the report revealed that only 32% of organizations effectively use more than half of the data they collect, making it harder to scale technologies like AI which rely on connected, trusted, and contextualized data.  

The bottom line is that AI adoption among life sciences manufacturers has accelerated faster than governance, infrastructure, and organizational readiness, with fragmented data, limited interoperability between manufacturing systems, and uneven digital maturity remaining as barriers to success.  

At the same time, regulatory agencies are increasing expectations around cybersecurity, data integrity, digital records, AI governance, and continuous quality oversight. Regulators are ultimately asking manufacturers to place greater emphasis on transparency, traceability, and the ability to demonstrate control across increasing digital operations, according to the report. 

“Manufacturers used to treat regulatory readiness as a project — something they ramped up for,” Matt Weaver, vice president of global industry-life sciences at Rockwell Automation, said in a statement. “Now, it has to be a daily discipline built into how the facility runs. The companies connecting their data, securing systems and validating AI deployments today are the ones that will be ready for what comes next from regulators or from the market.” 

Technologies essential to digital maturity 

AI and machine learning are the top technologies that life sciences manufacturers expect to drive the biggest business outcomes, according to the report, which is based on responses from more than 100 managers and executives from life sciences manufacturers, original equipment manufacturers, system integrators, and engineering procurement companies. Following AI, other technologies expected to have the greatest business impact include process automation/optimization and cybersecurity. 

Investments in these technologies are closely tied to business objectives, the report noted. Life sciences organizations cite improving quality (43%), digitizing operations (36%), and reducing safety, cybersecurity, and compliance risks (35%) as essential components of their digital maturity strategies. 

The report contends that this reflects a shift in the perspective of digital transformation as a strategic business initiative rather than one that is strictly technological. The success of these initiatives will be measured not only by the deployment of technology, but by the ability to improve consistency, strengthen resilience, and create more connected operations. 

Why connectivity is vital 

Better connectivity was stressed throughout the report as a fundamental part of digital maturity for manufacturers, while emphasizing that greater impact of these new technologies comes from connecting data, workflows, and decision-making across functions.  

It also emphasized the importance of breaking down silos between manufacturing systems and establishing a trusted foundation for advanced technologies across operations. 

“The next phase of digital maturity will be defined not by the number of technologies organizations deploy, but by how effectively they integrate those technologies to create a connected, data-driven operating model,” according to the report. “Organizations that can successfully align technology investments with business processes, people and governance will be better positioned to improve performance and adapt to evolving industry demands.” 

AI boom and how it supports manufacturers 

The survey found that despite growing pains associated with the adoption of AI, it is expected to be a predominant industry tool. According to survey respondents, among manufacturers already using AI approximately half expect to increase investment over the next five years, underscoring continued interest in AI across the life sciences. 

Meanwhile, 64% of manufacturers using AI plan to expand use of the technology within the next year, Rockwell Automation’s survey revealed. The report found that the top three planned applications of AI for improving performance across operations in that timeframe are concentrated in quality control (50%), cybersecurity (45%), and process optimization (44%). 

The intention to use AI to improve cybersecurity is significant as 54% of those surveyed said they experienced at least one cyberattack during the past year. With cybersecurity the leading external obstacle to growth for life sciences organizations, the most significant vulnerabilities are linked to IT systems and enterprise networks (41%), remote access and connected equipment (33%), as well as IT/OT integration points (29%). 

How manufacturers can boost digital readiness 

The report listed seven operational priorities for manufacturers to consider that could help enable a better foundation for digital readiness. 

Among Rockwell Automation’s recommendations: create a digital evidence management system for Remote Regulatory Assessments (RRAs) and electronic records requests; build resilience into connected products from design through deployment and maintenance; establis industrial data operations and standardized data models; scale AI from practical, validation-ready use cases; and implement Quality by Design into the manufacturing backbone. 

Rockwell Automation also suggested that manufacturers adopt validation approaches supported by automated testing, Continuous Integration/Continuous Deployment (CI/CD) controls — where appropriate — and continuous monitoring, as well as deploy product lifecycle management (PLM) capabilities that connect development, tech transfer, manufacturing, and post-approval change management.