Agentic AI and the Future of Smart Manufacturing

Agentic AI gives IT systems unprecedented control over physical assets. While most agentic workflows will likely be accompanied by a “human in the loop,” IT leaders must take great care to ensure that new tools do not expose critical infrastructure to cyberthreats and operational disruptions.

Network Segmentation: The convergence of IT and OT requires consistent segmentation to prevent compromised users, devices or AI agents from becoming launching pads that allow attackers to move freely across operational environments. Segmentation should isolate critical production systems so that a breach in one part of the environment does not disrupt operations or reach systems that could jeopardize safety if compromised. In addition to promoting safety, network segmentation can preserve operational resilience by allowing raw data and real-time decisions to remain close to the production environment.

Zero-Trust Security: If they have not already done so, manufacturers should move beyond perimeter-based security and instead adopt a zero-trust approach. In a zero-trust environment, every employee, device and AI agent must be verified before it can access corporate networks, systems and data. Zero trust is especially important as AI expands the number of endpoints, machine-to-machine connections and data flows that security teams must govern. As organizations grow their AI environments, they must grant AI agents only the access and authority required for their defined operational roles.

Data Governance: IT leaders already know that AI outputs are only as good as the data used to train AI systems. However, many organizations lack clear governance policies for data quality, ownership, access and appropriate use. Manufacturers should undertake efforts to standardize, connect and govern data from both OT and IT devices, laying a data foundation that will support AI solutions, yield better insights, improve operational efficiency and ensure compliance with data safety regulations.

Continuous Monitoring: When organizations continuously monitor their environments, they can detect threats earlier, increasing the chances that they will be able to neutralize them before attackers are able to access critical systems and data. In a manufacturing setting, monitoring practices should go beyond conventional cyber alerts and identify whether operational conditions are drifting outside intended margins. AI solutions increase the number of endpoints and data flows in manufacturing environments, making it harder to maintain coherent policies and detect anomalies before they cause disruption. However, security vendors are also leveraging AI in their monitoring tools, helping to reduce false alarms and more quickly identify true threats.

Compliance Readiness: Thanks to global regulations such as the European Union’s General Data Protection Regulation, data location and sovereignty requirements have become an important component of operations for organizations across industries. Manufacturers must adopt regulatory-compliant policies documenting how operational data is used, where AI may act autonomously, when humans must intervene and who remains accountable for outcomes. Rather than treating compliance as a one-time certification, organizations should conduct recurring assessments.

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Organizations that successfully combine infrastructure, security and data readiness — while prioritizing business impact over technology — can begin scaling toward increasingly autonomous operations.

Start With High-Value Use Cases: With conferences, news headlines and boardrooms all buzzing about AI, leaders may understandably be tempted to implement trendy, high-profile use cases. This is the wrong approach. As with any other technology, investments in AI should be guided by business value. In manufacturing, this often means a focus on predictive maintenance, quality monitoring and workforce productivity improvements. These are not only valuable outcomes but also can be accurately measured to quickly validate use cases. After scaling these early efforts, manufacturers can apply the lessons from these investments to additional pilots.

Modernize in Phases: Organizations that use a phased roadmap to implement AI will be positioned to prove value, limit production risk and scale successful pilots across the enterprise. A phased approach is especially important in manufacturing, where legacy business-critical technology may not always seamlessly integrate with AI solutions. Phased modernization should include pilots, but these pilots must be tied to business outcomes, with a realistic plan for wider deployment to prevent organizations from becoming permanently mired in pilot mode. In many automation journeys, AI will first produce business-critical insights, followed by simulation and validation, with solutions progressively taking on greater decision authority over time.

Empower the Workforce: Change management is a critical part of AI implementation. Often, workers worry that automation will replace their jobs, and organizations that do not address these concerns may see lower adoption and overall impact. Leaders should explain how automation will augment employee expertise and help workers to make better decisions in their roles. By taking time to gain buy-in from employees, organizations can leverage this expertise, giving employees an active role in shaping AI use cases. According to a Rockwell Automation report, half of manufacturers plan to repurpose existing workers in the next year as a result of their smart manufacturing investments. “Technology drives efficiency, but people drive outcomes,” the report states. “The organizations that succeed will be those that invest as much in workforce transformation as they do in digital capabilities.”

Measure Business Impact: Some organizations evaluate their AI programs by tracking adoption, activity or token consumption. However, these metrics only tell leaders how extensively AI is being used throughout their organizations, not how much value the technology is actually providing. Instead, organizations should track business objectives such as employee productivity, operational downtime and product quality. Also, any changes in security and resilience metrics may indicate whether AI solutions are opening new vulnerabilities. By establishing clear baselines and linking their AI investments to measurable outcomes, organizations can demonstrate value, identify unintended risks and determine which future investments will have the greatest potential impact.

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