Audience Opinion Piece

By Ben Davis, EVP, IT – Industry 4.0, Cambria

‍The modern manufacturing landscape is undergoing a profound transformation, driven by the integration of Information Technology (IT) and Operations Technology (OT). This integration is delivering unprecedented levels of efficiency, productivity, and innovation. The introduction of Artificial Intelligence (AI) to this integration comes with its own set of complexities, risks, and opportunities.
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IT and OT Orchestration in Manufacturing

Imagine a home remodeling project. The IT team represents the architects and designers. They are responsible for the harmonious flow of data, process optimization and automation, and secure enterprise systems. IT ensures that the blueprints (data) are accurate, the tools (systems) are tuned, and the construction plan (processes) is optimized. The OT team, on the other hand, is the construction crew. They are on site, directly interacting with the materials, equipment, and control systems that produce the actual output. They ensure the measurements are accurate, the building is consistent, and the construction site hums with productive activity.

Historically, these two sections often worked separately, with limited interaction. However, deeper integration between IT and OT means they are now working from a single set of blueprints. Data from the construction site (OT) is no longer isolated; it's flowing into enterprise systems (IT) for analysis, optimization, visualization, and strategic decision-making. This enables predictive maintenance, real-time quality control, and agile production scheduling. All of these functions and more are prime candidates to introduce AI as the single set of blueprints to move IT and OT more closely together.
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AI: The Single Set of Blueprints

Thoughtful AI delivery with the right controls serves as the single set of blueprints to bring IT and OT together on strategic initiatives. AI launched without formal system and process controls applied by IT can be thought of as an unplanned design choice by an individual craftsman working on the home remodeling project. This approach might involve individual engineers or operations teams creating their own AI models to solve specific, immediate problems on the factory floor. While these initiatives can be agile and provide quick solutions, they also carry significant risks.
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Risks Exposed by the Unplanned Design Choice:

• Security Vulnerabilities: Imagine a construction site where an unvetted AI model, developed outside of established cybersecurity protocols, is connected to critical machinery. This scenario can be likened to leaving a factory door unlocked, making it vulnerable to cyberattacks, data breaches, or malicious manipulation.

• Data Silos and Inconsistencies: If different teams are developing their own AI solutions in isolation, disparate data sources or inconsistent methodologies could be used to deliver outcomes. This can lead to fragmented data, inconsistent insights, and a lack of a unified understanding of factory performance. It's like different sections of the construction crew building from slightly different versions of the same blueprints.

• Scalability and Maintenance Challenges: Standalone AI solutions are siloed and require detailed documentation to support enterprise-wide deployment and long-term maintenance. When the "craftsman" moves on to the next project or opportunity, who understands their unplanned piece?

• Compliance and Regulatory Issues: In regulated industries, the lack of oversight on AI models can lead to non-compliance with industry standards or governmental regulations, exposing the potential for fines or other operational penalties.
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Opportunities Exposed by the Single Set of Blueprints:

Despite the risks, AI also presents unique opportunities for standardization, innovation, and agility across teams.

• Rapid Problem Solving: Individual teams can quickly engage AI to address immediate operational bottlenecks or improve specific processes, often leading to faster, tangible results.
  
• Culture of Innovation: It allows teams to build a culture of experimentation and empower employees to identify and solve problems using cutting-edge technology, uncovering new areas for AI application.

• Proof of Value: Successful AI initiatives can serve as proof of value, demonstrating the potential of AI and building internal support for more formalized AI adoption.

The key is to bring AI into the spotlight, transforming it from an unscripted design into a well-integrated, yet innovative, single set of blueprints that guides the IT and OT manufacturing home remodeling project.
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Delivering Harmonized Results

IT and OT can use AI as the single set of blueprints, serving as their common thread to perform a magnificent and secure remodeling project. This combination allows for the pushing of boundaries, introducing new designs and materials that blur the lines between traditional roles. This common thread will challenge existing paradigms and leverage AI to fundamentally change how both teams operate.

Consider the application of AI to optimize an entire production line. This isn't just about individual machine optimization; it's about using AI to orchestrate the flow of materials, machine scheduling, and human intervention across the entire manufacturing process and involve:

• Agentic AI: Automating repetitive data entry tasks, and automating processes on the factory floor, to free resources to focus on more strategic and subjective work.

• Digital Twins with AI Simulation: Creating digital replicas of factory assets and processes, allowing both IT and OT to collaboratively test scenarios, predict outcomes, and optimize operations without disrupting physical production. This blurs the lines as IT provides the computational power and data infrastructure, while OT provides the real-world operational understanding to make the digital twin accurate and valuable.

• Generative AI for Process Optimization: AI models can suggest new, more efficient production workflows or even design new factory layouts based on real-time data and historical performance. This would require IT to develop and manage these advanced AI systems, while OT would provide the critical operational context and validate the AI's suggestions in a practical setting.

Manufacturing organizations can successfully navigate an AI-driven integration of IT and OT to unlock full transformative potential by ensuring that every section of the team works from the same set of blueprints. The integration creates a powerful and innovative home remodeling project that drives the future of manufacturing.

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