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8 Steps to Prepare Your Manufacturing Operations for AI

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Digital illustration of AI in manufacturing, showing a connected globe with data networks, gears, and a worker interacting with a virtual interface

AI is identified as a potential solution for many challenges that manufacturers are facing – labor shortages, skills gaps, quality control, and managing external pressures. According to our 2026 State of Smart Manufacturing, nearly half of manufacturers surveyed ranked AI and machine learning (ML) as the top outcome driver over the next 5 years. And yet, the shifting global landscape and fast-moving technologies like AI/ML can feel disruptive.

As operational complexity increases and the business and geopolitical climate continue to change, manufacturers are emphasizing risk reduction. Manufacturers need solutions that combine automation, AI, and secure architectures from edge to cloud to optimize operations and reduce exposure to cyber, compliance, and operational risk, while building the resilience needed to navigate uncertainty with confidence.

Progressing your digital maturation

Infographic view

This is the digital maturity journey that manufacturers are navigating today. Most large organizations are still sitting around Phase 3— the ‘Connected Plant’ – which means they’ve begun integrating systems like ERP, MES, and automation. They’ve moved off paper. They have visibility across parts of their operations, but there’s still heavy reliance on manual decision-making, disconnected data, and reactive processes.

The difference between the ‘Connected Plant’ and one that enables predictive operations is huge – analytics guide decisions proactively, supply chain visibility is real-time, and digital twins help model outcomes before they happen. And ultimately, autonomous operations, where systems are modular, closed-loop, and capable of self-optimizing with minimal intervention.

This shift is about more than just technology. It’s about enabling faster response times, higher resilience, better quality, and less downtime.

Taking steps today to be ready for AI

Most plants are still running—or still investing in—systems built for earlier generations of manufacturing: recordkeeping systems, control-focused systems, siloed point tools, or platforms that require heavy customization just to work. 

These systems served their purpose, but they were never designed for what manufacturers need today: autonomy, interoperability, distributed intelligence, and AI-driven operations. Imagine having AI woven into every layer of your business:

  • Working in your business by empowering the frontline with AI-powered workflows that reduce errors, streamline production, and elevate day‑to‑day performance.
  • Working on your business by empowering leaders with operational intelligence to drive strategic decisions, unlock capacity, and accelerate continuous improvement.
  • Working with your business by empowering IT partners to rapidly build and deploy solutions that accelerate time‑to‑value and lower the total cost of ownership.

So, what is truly needed to make the shift to more elastic solutions that can put you on the path toward autonomous operations?

  1. Prove value vs. technology: Technology works. Find and prioritize specific digital use cases that solve manufacturing and operational issues.
  2. Investments with a short-term payback: Transformations stall when ROI is slow. Build rapid, steady flow of value to drive adoption and self-funding.
  3. Plan for scalability: To deliver desired outcomes at scale, plan for the optimal set of technologies with integration to the existing backbone. Focus on common work processes across the enterprise.
  4. Foster enterprise collaboration: Siloed solutions are a dead end. Enterprise (OT / IT) digital connectivity and collaboration unlock exponential value.
  5. Learn, iterate and improve: Long-term planning helps, but inflexibility can mean missed opportunities. Keep an eye on your digital vision while learning and adjusting your strategy and execution to build on proven value as it emerges.
  6. Communicate progress and success: Momentum matters. Spread the word beyond the impacted group to build and maintain excitement for what’s possible.
  7. Define and apply governance: Protect sustained value. Embrace new ways of working, including adherence to process and data standards.
  8. Equip and champion people: To get ROI from digital, empower people beyond introduction of new technology. Skills and mindsets that support new ways of working are key to success and driving self-service. 

The future starts now

The potential of the future of industrial operations is exciting – and the future is happening right now. Manufacturers are moving from automation to autonomy. From systems that are programmed to systems that learn, fueled by the evolution of AI, robotics, and other industrial tech; systems that anticipate needs, solve problems, and continuously improve without human intervention; freeing up people to focus on other tasks, to make more meaningful contributions.

As important as the enabling technology is —the building blocks of intelligent autonomy – the right partner is equally critical. Technology has the potential to transform your operations, but it can also add layers of complexity, and require specialized skills. To capitalize on the opportunity of autonomous operations, you need to partner with a trusted team who can guide the way toward short-term value and long-term success. Someone who understands the complexities of IT and OT, who understands your KPIs and the unique challenges of industrial operations. Someone who recognizes the relationships between throughput and quality, labor and asset availability, downtime and revenue. 

Curious about how you can leverage AI for data analysis, issue prediction, and response coordination? Tune in to “From Data to Decisions: Driving Quality with AI” to hear more about empowering your MES and QMS solutions with AI technology.

Topics: Manufacturing Execution System (MES) Enterprise Resource Planning (ERP) Quality Management System (QMS) Production Monitoring Asset Performance Management (APM)

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