Peter Zornio is CTO for Emerson, a Fortune 200 global technology and engineering company headquartered in St. Louis, Missouri.
Industrial automation is at a pivotal crossroads. Global manufacturers must navigate disruptive supply chains, shifting market demands and the relentless pace of technological advancement. Executives are no longer asking if they should digitize, but how to leverage their investments in digital technology to transform their operations for a future where adaptability and actionable data are the new currency of competitiveness.
This is not new; “digital transformation” as a strategy has been in vogue for nearly a decade. But new architecture and software technology can remove the obstacles many have encountered as they embarked on their transformations.
Data As The Core Investment Priority
Manufacturers worldwide are prioritizing investments in digital capabilities, with a distinct focus on harnessing data analytics and artificial intelligence (AI) to drive operational excellence. Deloitte’s 2025 Smart Manufacturing and Operations Survey reports that over 80% of executives see smart manufacturing as pivotal to future growth, with data analytics, cloud computing and AI topping their investment priorities.
Data is the core of AI and smart manufacturing. ARC Advisory Group’s latest findings echo this sentiment, emphasizing the emergence of the “industrial data fabric”—a unified, contextualized data infrastructure that leverages data from sensors, machines, enterprise systems and supply chains to power the next wave of industrial AI innovations. This evolution is not just about collecting more data, but about managing, contextualizing and leveraging it to unlock real business value.
Our customers echo this every day—that challenges with data, including data quality, contextualization and validation, are significant obstacles to AI implementation, as reflected by MIT Technology Review.
Data management tools that provide contextualization, governance and accessibility are the backbone of effective AI-driven automation. In summary, an effective data strategy is the underpinning of a successful AI strategy.
Transforming Control Functions
Another obstacle facing AI implementation is the traditional automation foundation of hardware-embedded control, which is inherently communication- and upgrade-limited. This drives costs, hinders continuous improvement and increases risk. Instead, forward-looking organizations are building a new, more flexible and scalable control foundation that is defined by software, data accessibility and intelligence.
Against this backdrop, four foundational elements are emerging as critical for industrial leaders shaping the next era of automation:
1. Software-Defined Automation
The move toward software-defined automation marks a fundamental shift. Instead of a foundation of fixed, proprietary hardware-bound logic, manufacturers are embracing modular, flexible architectures where control functions are decoupled from physical devices as part of a complete unified stack of automation and optimization software.
A software-defined automation platform bridges legacy systems with future-ready technology without the need for rip-and-replace. This enables seamless modernization, faster deployment of new technologies and AI-powered innovation—capabilities that are especially vital in today’s ever-changing global environment.
2. Data-Centric Operations And The Industrial Data Fabric
Future-ready automation places data at the heart of every decision. An industrial data fabric, which connects sensors, machines, process equipment, IT systems and cloud platforms, enables secure, real-time data flow across the enterprise. By contextualizing and governing this data, manufacturers can ensure that advanced analytics and AI operate on accurate and relevant information, driving improvements in productivity, reliability, safety and sustainability.
3. Advanced Analytics And AI Integration
Our work with customers shows they are deploying AI for predictive maintenance, quality control and process optimization and achieving measurable gains in uptime and efficiency. Consider rotating equipment such as pumps and compressors. Using wireless vibration and other sensors, AI-based machinery health software can diagnose mechanical problems and define corrective actions. Production anomalies are detected and traced to root causes using hybrid models combining first principles with historical data.
United in a data fabric, every piece of sensor data helps AI models paint a clear picture of current production, reliability and sustainability conditions, predict future outcomes, and suggest the optimum set of actions—or take those actions directly in an agentic manner, moving closer to autonomous operation.
AI also helps close the gaps created by the shrinking of an expert workforce. Industrial AI tools can help upskill employees faster than ever before and deliver in-context guidance to any worker: operations, engineers, IT analysts and others. This makes every user an expert, enabling them to collaborate with AI-based applications to address operational situations that come up.
4. Intrinsic Cybersecurity
In manufacturing, secure and scalable connectivity between personnel, OT (operational technology) software applications, devices and IT environments is mission-critical. Today’s automation architectures rely heavily on network segmentation for security, making connectivity arduous; a better model is needed for the hyper-connected architecture of the future.
Zero-trust security frameworks ensure that every access, device, application, connection and data remain reliable and protected, no matter where operations are located or software is being executed, whether in high-performance field devices, edge computers or the cloud.
From Vision To Execution: The Executive Imperative
The industrial automation revolution is not just about technology. Challenging traditional organizational structures and “silos,” empowering people and having support from top levels of management are all requisites to delivering the financial benefits the technology can deliver. Executives must champion the shift not just as a technical upgrade, but as a strategic necessity.
Leading companies are already seeing the rewards: faster time-to-market, improved sustainability, better risk management and the agility to turn disruption into opportunity. Those who invest in these four pillars today can define the competitive landscape of tomorrow.
For industrial leaders, the question is no longer “Should we transform?” but “How fast can we build the foundation for the next era?”
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