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AI Readiness: OT's Painful Path to Scale

Summarize the Content of the Blog

Operational Technology (OT) is in a race to leapfrog the foundational technologies required for AI readiness. While AI may be smart, it cannot skip the necessary steps of growth. This journey is like a salmon swimming upstream. The struggle is not a failure, it is the only way to reach the AI-capable spawning grounds.

The Stalemate of Progress

OT is changing, but at what pace? At times, the discussion around AI makes it feel incredibly fast, yet simultaneously, we are at a stalemate, waiting for the perfect "North Star" solution to emerge. It’s not that simple. Critical, fundamental processes are still needed before we can truly leverage AI. I love analogies because they remind us that we cannot skip the painful process. We view this as the "Acceleration to Learning," but the learnings are instrumental, uncomfortable, and necessary. You must lean into the pain of growth and relish the process, viewing these trials as the indispensable path forward.

The Integrator's Role

How will you manage this growth? You could continue to leverage the same system integrators (SIs) by simply telling them what to do. Or, you can choose to capture the knowledge of their skills and allow them to guide you through the pain of growth. A good integrator will take you through the necessary trials and warn you of the obstacles ahead. You then have two options: commit to internalizing those warnings and acting on their advice, or commit only to your initial perceived needs. The latter choice, accelerating to learning, often means figuring out all the wrong ways of doing things.

The Foundational Pieces We're Missing

At a recent conference, I saw two recurring themes: Products struggling to articulate their purpose, and System Integrators selling a distant future with few exceptions. The excitement is glossing over missing steps. What are the foundational pieces vendors are ignoring?

The answer is context enrichment, and it must come from the engineers and factory workers who live and breathe the systems every day. These are the people without budgets. The factory floor engineers.

The Budget Catch-22

So, why the budget deficit for these critical engineers? It's a simple, and painful, Catch-22. The people with the clearest vision for change, typically junior engineers, are the very people recommending changes viewed as 'overhead.' The people who have the vision to change are often the ones who cannot make the changes.

They have an appetite for necessary evolution but lack the budget and the data to back up their recommendations. This is, in my view, the core reason engineers are abandoning manufacturing at an accelerated pace. Couple this with the growing interest in working from home, and you have a genuine recipe for disaster and burnout.

I speak from experience. I sit here now, in my gray sweatpants and a button-up, a person who once worked in a plant, worked weekends and holidays, and spent years traveling to random hotels two hours away from facilities. Do I miss the accomplishments of that life? Hell yeah. But I am also at a different stage where those accomplishments are no longer the priority. I have spent the last 10+ years guiding the people who live and breathe these machines.

The solution is not to continue building the past or selling a future we can’t reach. We must build what is needed now to enable the future, hand in hand with engineers who have an appetite for change. But we must accept that, just like the salmon, that can only be done painfully.

Frequently Asked Questions (FAQ)

Many OT organizations are rushing toward AI adoption while skipping essential foundational steps like >data contextualization, clean infrastructure, and operator knowledge capture.

Just like a salmon that cannot bypass the upstream journey, AI cannot skip the necessary stages of growth. The excitement around AI is causing organizations to gloss over the painful but essential groundwork.

Acceleration to Learning describes the process of learning through discomfort and trial — often by discovering the wrong ways of doing things first. While painful, these learnings are instrumental and necessary.

Organizations must lean into this process rather than seek shortcuts, viewing the struggles as the indispensable path to AI readiness.

A good SI should do more than execute instructions — they should guide organizations through the growth process, warn of obstacles, and share the knowledge needed to navigate change.

Companies must choose between simply directing their SI or allowing the SI to lead them through the necessary trials. The latter approach, while harder, builds lasting capability aligned with secure and scalable operations.

Context enrichment. This is the critical step that vendors and SIs are glossing over in their rush to sell AI-ready futures.

Context enrichment must come from the engineers and factory floor workers who live and breathe the systems every day — but these are precisely the people without budgets or organizational authority to drive change. Learn how this aligns with manufacturing data strategies.

The Budget Catch-22 is this: the people with the clearest vision for change — typically junior engineers on the factory floor — are the very ones recommending improvements that get labeled as “overhead.”

They have the appetite and knowledge to evolve the operation, but lack the budget and data to back their recommendations. This mismatch is a core reason engineers are leaving manufacturing, and it compounds with remote-work trends to create a genuine risk of burnout and brain drain.

The solution is to stop building the past or selling a distant future, and instead build what is needed now — hand in hand with engineers who have an appetite for change.

Organizations must accept that progress, like the salmon swimming upstream, can only happen the hard way. Embracing that painful process, rather than avoiding it, is the only genuine path to an AI-capable operation.

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