New data from Rockwell Automation disclosed that 90% of life sciences manufacturers consider digital transformation necessary to their business, while 58% have deployed smart manufacturing technologies at scale or across parts of their operations. It found that manufacturers are investing in artificial intelligence (AI), process automation and cybersecurity to improve quality, digitize operations and reduce risk. AI and machine learning ranked as the technologies expected to deliver the biggest business outcomes, cited by 46% of respondents, followed by process automation at 44% and cybersecurity at 39%.
In its recent report, ‘The New Operating Model for Life Sciences Manufacturing Moving from Compliance to Continuous Readiness,’ Rockwell identified gaps in data readiness and cybersecurity as life sciences manufacturers expand digital operations. Among manufacturers already using AI, 64% plan to expand its use within the next year, and 49% expect to increase investment over the next five years, but only 32% said they effectively use more than half the data they collect. Meanwhile, 54% of life sciences organizations experienced at least one cyberattack in the past year, with exposure reported across IT systems and enterprise networks (41%), remote access and connected equipment (33%) and IT/OT (operational technology) integration points (29%).
The findings are based on responses from 104 managers and executives across life sciences manufacturers, original equipment manufacturers, system integrators and engineering procurement companies in 16 countries.
“Manufacturers used to treat regulatory readiness as a project — something they ramped up for,” Matt Weaver, vice president, global industry for life sciences at Rockwell Automation, said in a media 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.”
Digital transformation is no longer viewed as a future initiative in life sciences manufacturing. It is becoming a business imperative driven by the need to improve quality, reduce risk and create more resilient operations. As organizations navigate an increasingly complex operating environment, investments in smart manufacturing are shifting from isolated projects to enterprise-wide strategies.
The latest State of Smart Manufacturing report shows that 58% of life sciences organizations have already implemented smart manufacturing technologies either at scale or across portions of their operations. At the same time, 90% believe digital transformation is necessary to respond to changes and challenges in industrial technology. These findings suggest that manufacturers are moving beyond asking whether to transform and are increasingly focused on how to scale and deliver value from their investments.
As organizations deploy new technologies, many are discovering that value is not created by individual systems alone. Greater impact comes from connecting data, workflows and decision-making across functions. For life sciences manufacturers, this means breaking down silos between production, quality, engineering and business systems. It means creating greater visibility across operations and establishing a trusted foundation for advanced capabilities such as AI, predictive analytics and continuous improvement programs.
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. Organizations that can align technology investments with business processes, people and governance will be better positioned to improve performance and adapt to evolving industry demands.
Rockwell identified several barriers to continuous readiness in the life sciences industry, noting that digital transformation is accelerating but technology adoption alone does not guarantee readiness. The findings point to foundational challenges that continue to limit progress and make it more difficult to scale digital initiatives, AI programs and cybersecurity efforts.
Connectivity is bringing new exposure and complexity. Cybersecurity is the leading external obstacle to growth for life sciences organizations, with 54% having experienced at least one cyberattack during the past year. The most significant vulnerabilities are linked to IT systems and enterprise networks (41%), remote access and connected equipment (33%) and IT/OT integration points (29%). As digital transformation continues, cybersecurity must be embedded into operational strategies, technology decisions and risk management programs.
Data is also growing faster than organizations can use it. Manufacturing systems generate vast amounts of information, yet only 32% of organizations report effectively using more than half of the data they collect. Without a connected data foundation, efforts to support AI, improve decision-making and increase operational visibility become more difficult to sustain.
Finally, technology alone is not enough. Many organizations have already invested in smart manufacturing, AI, and cybersecurity solutions. Still, the next phase of maturity will depend on how effectively they integrate those technologies across systems, processes, and teams. Successful transformation requires a foundation built on trusted data, clear governance and operational alignment.
The Rockwell report identifies seven ways for life sciences manufacturers to build continuous readiness: digital evidence management for Remote Regulatory Assessments (RRAs) and electronic records requests, cyber resilience for connected products and digital operations, industrial data operations and standardized data models, scaled AI from practical, validation-ready use cases, building Quality by Design into the manufacturing backbone, continuous software assurance for validated systems, and knowledge management across the lifecycle.
On digital evidence management, Rockwell recommends implementing an inspection-ready ‘evidence layer’ that includes a secure portal, curated exports, standardized record packages and audit trails. This layer can rapidly assemble and deliver batch records, deviations, Corrective and Preventive Action (CAPA), change control, validation evidence and product quality reviews with full context.
On cyber resilience, the report advises building resilience into connected products from design through deployment and maintenance. By improving visibility, strengthening vulnerability management, enhancing governance and managing supply chain risk, organizations can reduce cyber exposure, maintain trusted operations and adapt to evolving regulatory expectations and emerging threats.
On industrial data operations, Rockwell calls for establishing capabilities to connect, contextualize and govern operational data across equipment, processes and manufacturing systems. Standardizing metadata, data models, lineage and access allows information from historically fragmented OT and enterprise systems to be trusted, reused and traced. This foundation improves interoperability and operational visibility while preparing manufacturers for advanced analytics and validated AI use cases.
On scaling AI, the report recommends starting with well-defined, lower-risk applications that augment engineering workflows while maintaining human oversight, such as helping generate and interpret control code and identifying gaps between specifications and logic. As data readiness improves, manufacturers can expand AI into process modeling, tech transfer, validation documentation, equipment performance and quality monitoring, with clear intended use, traceability and validation controls.
On Quality by Design (QbD), Rockwell says manufacturers should connect process development data to manufacturing execution. This allows processes validated at smaller scale to transfer directly to clinical and commercial-scale production. On continuous software assurance, the report points to validation approaches supported by automated testing, Continuous Integration/Continuous Deployment (CI/CD) controls where appropriate, and continuous monitoring. These approaches generate objective evidence efficiently and maintain control through frequent software changes.
Finally, on knowledge management, Rockwell recommends deploying product lifecycle management (PLM) capabilities that connect development, tech transfer, manufacturing and post-approval change management. This enables faster, more consistent execution and stronger lifecycle knowledge management.
