IT/OT Convergence: Still Vague, Still Critical

For years, IT/OT convergence has been a recurring theme in digital transformation conversations. It’s almost become a cliché. Everyone agrees it’s important, but few can define it clearly, and every company seems to have its own “flavor.”

That vagueness is both a challenge and an opportunity. IT/OT convergence is not just about technology stacks, data pipelines, or network architectures. It is about organizations, people, and how digital capabilities become part of the fabric of operations. And in the context of continuous transformation, this conversation remains more relevant than ever.

Why IT/OT Convergence Matters in Continuous Transformation

Digital transformation is not a one-time project—it’s a continuous process of adapting, learning, and embedding new technologies into how we operate. In that context, IT/OT convergence is essential.

Why? Because transformation cannot happen in silos. The systems that plan and account (IT) and the systems that control and execute (OT) must work together seamlessly. If they remain separate—organizationally, technologically, or culturally—you end up with fragmentation that slows down transformation instead of enabling it.

Can We Define It? And Why Does That Help?

One of the reasons IT/OT convergence feels vague is because it is often reduced to a technical exercise—connecting networks, integrating databases, or sharing dashboards. But that misses the bigger picture. To make it actionable, we need a broader and more ambitious definition.

At its core, IT/OT convergence is about making IT and OT inseparable. Not aligned, not just integrated, but merged into one digital foundation for the business.

That means:

• Integration of technology across the entire operation—from planning and engineering, to execution on the shop floor, and even to customer-facing processes. IT and OT must form a continuous digital thread that spans the lifecycle of design, production, quality, logistics, and service.
  
  

• Merging organizational roles and responsibilities—so that IT and OT aren’t two camps negotiating interfaces, but one team co-owning outcomes. The boundaries blur until it becomes irrelevant whether a capability was once “IT” or “OT.”
  
  

• Embedding digital practices into operations—so technology isn’t an external tool to be “applied” to operations, but a core element of how the organization works, improves, and creates value.

One of the most dangerous misconceptions in digital transformation is treating technology as an external layer—something added on top of operations. This is what has been going on for decades, born out of necessity of dealing with super complex monolithic systems. It is a model that creates friction, and this friction is detrimental to progress - it will suffocate any digital adoption initiative.   

Defining convergence in this way is helpful because it reframes the conversation: it’s not about how to connect two separate worlds, but how to design an organization where there is only one world. That shift in mindset is what makes IT/OT convergence transformative.

For digital technology to be impactful, it must be embedded into the way work is done at all levels: from the operator on the line, to the planner in the back office, to the leadership team setting strategy. IT/OT convergence makes this embedding possible.

When data, insights, and digital tools flow seamlessly across operations, technology doesn’t feel like an “extra.” It becomes integral to how people work, decide, and improve.

The Composability Pillar of Agile Operations

Finally, IT/OT convergence is inseparable from the principle of composability. To be agile, organizations need technologies that can be composed, reconfigured, and adapted as needs change.

That means convergence cannot be separated—neither organizationally nor by use. If IT and OT are treated as distinct silos, agility suffers. But when convergence is embraced, composable technologies support operational excellence: flexible enough to adapt, strong enough to sustain, and aligned enough to deliver value across the enterprise.

How Do We Know When It’s Complete? And Does That Matter?

Here’s the truth: IT/OT convergence is never “complete.” Like continuous transformation itself, it’s an ongoing journey. Technologies evolve, organizational structures shift, and new business challenges arise.

The goal is not to check a box that says, converged. The goal is to continually deepen the integration between IT and OT so that technology becomes invisible—it simply is the way you run operations.

So whether or not it’s ever “done” is less important than whether it’s continuously evolving to support value creation.

Moving Beyond the Buzzword

So, is IT/OT convergence vague? Absolutely. But it’s vague not because the idea lacks merit—it’s vague because it was born out of conflict.

IT and OT have so far been separate domains, each with its own responsibilities, budgets, and power structures. IT managed enterprise systems, data security, and corporate standards. OT managed the machines, processes, and operational continuity. Bringing the two together is not just a technical exercise—it’s a challenge to established authority.

That’s why IT/OT convergence often feels like a “hot potato.” Nobody wants to own it fully because it requires organizations to do things that are uncomfortable:

• Merging organizations that were once distinct.

• Relinquishing power as decision-making becomes more distributed.

• Diminishing rigid responsibilities as democratized technologies empower more people to contribute to digital solutions.

At its heart, convergence means releasing control—accepting that digital technologies are no longer the sole domain of one function, but a shared capability that belongs to everyone.

And that’s hard. It’s hard for people who have built careers around defending their territory. It’s hard for organizations that have optimized themselves around silos. It’s hard because it requires a cultural transformation just as much as a technological one.

But here’s the truth: without this convergence transformation halts. You cannot achieve continuous transformation if half the organization is innovating in isolation while the other half is protecting legacy boundaries. The result is friction, fragmentation, and failure to capture the value that digital technologies promise.

This is why IT/OT convergence—however uncomfortable, however vague—remains critical. It is the cultural and organizational foundation on which digital transformation rests.

In the end, convergence is not about IT and OT learning to work better together. It’s about creating a new whole where the distinction ceases to matter. That is the mindset shift. And until organizations embrace it, “transformation” will remain more slogan than reality.

Why Are We Still Talking About MES–ERP Integration?

Every few months, I still come across discussions about how to integrate MES and ERP. And every time, I find myself asking: why are we still talking about this?

It’s a bit like asking whether a boat floats. The answer is obvious—yes, it does. The real question is where is it going and why are we on it?

Integration Isn’t the Problem

Let’s be clear: integration between MES and ERP is not new, nor is it unsolved. For decades, manufacturers have been connecting these systems to exchange the information that keeps their operations running. I challenge you—have you ever heard of an MES system that couldn’t integrate to ERP?

The technology is there. APIs, middleware, standardized data models, cloud-native platforms—the tools have only gotten better. Integration is no longer the hard part.

As I wrote in an earlier post "About Accountants and Production", ERP and MES have always been about different things. ERP is designed for financial management (order-to-cash) - transactions, costs, compliance, reporting. MES is built for the shop floor—real-time visibility, control, and execution. Each system has its domain. Integration ensures they don’t talk past each other.

But the value doesn’t come from whether or not you can connect the two. It comes from what you do with that connection.

From Technical to Value-Driven

When integration conversations remain technical—what middleware to use, which API calls to expose—we miss the bigger picture.

The true conversation should be:

• What processes, operations and decisions do we want to improve?
• What outcomes are we aiming to achieve?
• What value will the integration unlock for the business?

For example, integrating to have a streamlines and effective work order execution from ERP to MES is not valuable because the two systems are connected. It’s valuable because it eliminates manual re-entry, reduces errors, speeds up production scheduling, and ensures financial systems reflect operational reality in near real time.

Integration is the means. Value is the end.

Enter the Age of Digital and AI

We’re well into the era of digital, transformation is ongoing and constant, and AI in manufacturing is becoming a reality. Advanced analytics, machine learning, digital twins, and agentic AI are reshaping how operations are managed and humans work. Against that backdrop, spending time debating MES–ERP integration feels outdated.

The real opportunity is to ask: how do these systems, together, create the digital backbone that enables AI to bring operational insights that deliver business value?

ERP knows the plan. MES knows what actually happened. AI thrives when it can see both and spot patterns across them—optimizing schedules, predicting disruptions, and suggesting interventions. That’s the conversation worth having.

Time to Move On

So let’s put this to rest: MES and ERP can integrate. They do integrate. The technical questions have answers.

The real debate—the one that matters in the age of digital and AI—is about value. How do we design our digital architectures, processes, and cultures so that integration serves as the foundation for smarter, faster, and more agile manufacturing? Shift the focus from can we integrate? to what value will the integration deliver?

Don't Be the Last Dinosaur: Your Plant's Digital Evolution Starts Now!

Plant Managers, manufacturing leaders - look around you. The manufacturing world is not just changing; it's undergoing a seismic shift. While you're grappling with daily firefighting, your competitors – the agile, the digitally native, and those strategically investing – are busy building tomorrow's factories. They are not compromising; they are not settling for just automation, they are digitizing every process, they have rich digital data, they are ready to deploy intelligent, agentic systems that learn, adapt, and optimize production with unprecedented speed and accuracy.

The choice before you is stark: Innovate and adopt, or become obsolete.

If you continue to manage your plants with fragmented data, manual processes, and reactive decision-making, you are literally leaving money on the table. You're bleeding efficiency, compromising quality, and surrendering market share. The 'traditional' way of manufacturing is becoming a relic.

Here are the critical decisions and actions you must undertake RIGHT NOW to avoid being left in the dust:

1. Stop analyzing and planning- Start digitizing today: Your operations are awash in data, but is it usable? Is it real-time? Is it connected? The foundation of every advanced manufacturing system—from IIoT to agentic AI—is clean, contextualized data. 

ACTION: Invest immediately in robust digital data infrastructure. This means connecting your machines, sensors, and systems (OT and IT convergence is non-negotiable). If your data is trapped in silos, you are blind, and your plant will suffocate under its own inefficiency. This is simply critical - not having digital data is a waste, like other wastes in manufacturing!

2. Embrace the Power of Composability: Shift your thinking beyond overarching automation or top-down systems to digitize plant processes. The "lights-out factory" is not only outdated, it never truly materialized. Digital transformation isn't about replacing humans but augmenting their capabilities. Composability focuses on empowering your workforce, leading to significant productivity gains. Deploy pilot projects where AI agents can take over mundane, repetitive, or complex analytical tasks. Think predictive maintenance that schedules itself, quality control that detects micro-deviations before they become defects or failures, and production dispatching that dynamically adjusts to supply chain disruptions. 

ACTION: Identify one critical bottleneck in your plant that could be addressed by a digital solution - yes “Kaizen”, start bottom up and iterate. Adopt technology, find a partner and just start. There are some impactful digital technology platforms (I recommend starting with Tulip of course) and launch a focused pilot. Show your team and leadership the tangible benefits.

3. Future-Proof Your Workforce – Invest in Upskilling, Not Just Training: Your people are your greatest asset. They need to evolve from procedure followers to orchestrators, data interpreters, and system managers. This isn't just about technical skills; it's about fostering a culture of continuous learning and adaptability. And with democratization that digital technologies offer this quickly can create a ground swell, a movement within your organization. 

ACTION: Develop a strategic plan for workforce transformation. Enhance your operational excellence with augmented lean principles and identify critical future roles and necessary skill sets. Empower your team to embrace these new technologies and strike down any skepticism or technology fears.

4. Demand Agility from Your Systems – Ditch the Rigidity: The era of monolithic, inflexible manufacturing systems is over. Your plant needs to be agile, able to pivot production lines, incorporate new products, and respond to market shifts with unprecedented speed. This means moving towards composable, interoperable platforms that can readily empower your operations with the new digital capabilities including critically AI. 

ACTION: When evaluating new solutions (like MES, LIMS, CMMS or other systems), prioritize open composable architectures with cloud-native capabilities, IIoT platforms and and in herent AI capabilities that are not an afterthought or “bolt on”. Demand systems that are built for change, not for static operations. These systems have to enable you to transform, they have to provide you a way to implement according to the 5 pillars of composability. Make agile, emergent control a reality with agentic AI.

5. For Regulated Industries: Make GxP a Competitive Advantage, Not a Burden: If you're in Pharma, Biotech, or Med Device, the GxP implications of these technologies are paramount. But don't let compliance be an excuse for stagnation. Modern digital validation approaches and Pharma 4.0 guidances mean you can innovate with compliance. Transformative digital platforms have inherent built in compliance through detailed digital data, audit trails, and transparency to control mechanisms. Democratization means technology is simpler to understand with that comes transparency and self documentation.

ACTION: Engage with experts who understand both cutting-edge digital transformation and the nuances of GxP. Be proactive in defining your digital validation strategies for IIoT and AI, leveraging initiatives like Validation 4.0. This ensures your innovation is robust, secure, and compliant.

The clock is ticking. The question isn't if your plant will undergo this digital transformation, but when and who will lead it. If you hesitate, you risk becoming a case study in industrial obsolescence. Seize this moment, or watch your competition leave you in their digital dust. This is the mindset you need to adopt to thrive in this new era. What's your immediate next step?

How Holonic Dreams are Becoming Manufacturing Realities

Let me tell you, there are few things more thrilling than seeing a concept you poured your heart into decades ago slowly coming to life in ways you barely dared to dream. For me, that feeling hits hard with the incredible capabilities of AI in general, and specifically, Generative AI . These aren't just buzzwords; they're fundamentally reshaping how we think about digital manufacturing (or whatever the latest term is). While Generative AI is phenomenal for creating content, designing new products, or even simulating complex processes, its true power in manufacturing often lies in its ability to empower something even more profound - Multi-Agent systems. This is the realization of Holonic concepts in a composable manner to enable agile manufacturing with Agentic AI .

When I see these advancements I am just mesmerized and my mind goes back to the 1990s. That’s when my journey into this future really began, deeply immersed in the world of Holonic Manufacturing Systems (HMS) and the emerging field of multi-agent systems.

Back then, I was a young researcher PhD working a methodology and architecture for Holonic manufacturing systems in collaboration with other like minded researchers as part of global consortium. It wasn’t just an academic exercise; it was a burning ambition to make manufacturing truly agile and resilient. I envisioned a factory floor that wasn't a rigid, top-down hierarchy, but a vibrant, decentralized network of intelligent, collaborative entities – what we called "holons."

The Holonic Vision: A Glimpse into the Future I Believed In

Imagine a shop floor where every machine, every production cell, every product, wasn't just a passive component but an intelligent "holon" – a self-contained, self-regulating unit. They would have their own smarts, making decisions, talking to each other, and collectively adapting to whatever curveball the market threw at them. Koestler's concept of a "holon" – simultaneously a whole and a part – perfectly captured this idea of distributed intelligence.

The benefits? Oh, they were clear and seemed a world away but yet an eerily anticipatory need of the current political and economic circumstances.

• Agility beyond belief : Reconfiguring production lines in a flash, launching new products on a dime, responding to customer demands with unprecedented speed.
• Built-in resilience : If one holon stumbled, the others would dynamically pick up the slack, re-routing operations to keep things flowing. Downtime issue would be a distant memory.
• Seamless scalability : Adding new machines or processes would be like plugging in a new module, effortlessly integrating into the intelligent network.
• Optimization from within : Local decisions by these smart holons would ripple up to optimize the entire system, far surpassing anything a central, rigid control system could ever hope to achieve.

This wasn't just theory; it was a blueprint for a manufacturing revolution.

We are Still Waiting for a Digital Manufacturing's Breakthrough

The holonic concept was the perfect architectural dream, but the engine to power it, multi-agent systems, was still in its infancy. My research in the 90's focused on how to design these agent systems, how to give them that holonic spirit, but the reality was that our ambition ran ahead of the available technology. We were hitting walls. The computational power needed to run complex agent logic on shop-floor controllers was simply astronomical for the time. Getting a multitude of agents to communicate reliably and securely across a factory? That was a networking nightmare. And then the AI capabilities were nascent, a whisper of potential, compared to the what we wield in our hands today."

Those were exhilarating times for pure research, pushing the theoretical limits of what manufacturing could be. But bringing it to large-scale industrial reality? That was a bridge too far. Until now ...

Today's Reality: AI and Digital Infrastructure can Unleashing the Holonic Dream

Fast forward to today, and the technological landscape has dramatically evolved. The convergence of several critical advancements has not only rendered the holonic vision achievable but has propelled it into operational realms previously unimaginable:

1. Advanced AI and Machine Learning Capabilities : Modern Artificial Intelligence and Machine Learning algorithms now provide the sophisticated analytical and cognitive capabilities for individual software agents. These algorithms enable agents to learn from large-scale industrial datasets, execute robust predictive analytics with high precision, and achieve adaptive process optimization in real-time. This represents a fundamental shift from deterministic, rule-based systems to dynamic, self-optimizing intelligence.
2. The Industrial Internet of Things (IIoT) as the Network of Agents, Powered by Agentic AI : This is where the core holonic vision finds its full realization. The Industrial Internet of Things (IIoT) is not merely a collection of connected devices; it forms the very network of agents. The IIoT nodes themselves are designed as autonomous, intelligent agents . Each smart device, each sensor, each piece of equipment can be made to act as a data acquisition point and, crucially, as a decentralized intelligent entity. The emergence of Agentic AI imbues these IIoT nodes with advanced capabilities for complex task decomposition, strategic planning, execution, and critical self-reflection. This synergy of IIoT as the foundational agent network and Agentic AI providing the cognitive layer directly mirrors the autonomous and cooperative principles fundamental to our original holonic concepts, enabling seamless interoperable collaboration across the manufacturing ecosystem. 

Futuristic Use Cases Becoming Reality: Empowering the Digital Twin for Tomorrow's Factory

For all these visions to truly become reality, we needed more than just powerful tech; we needed a concept that can effectively by applied to the realities of a physical operation. Here we can use a Digital Twin approach to represent the reality of the operation and build a foundational model with a set of constructs that aligned perfectly with the holonic principles I’d spent years researching.

This is also where Agentic AI enters the stage, becoming the intelligence that breathes life into this digital replica. My research from the 90s proposed a consistent model for these foundational constructs, embodying them as intelligent Product, Order, and Resource agents (or holons). This was the blueprint for how the real-world manufacturing elements could become autonomous, cooperative entities within the digital twin.

Let's step into this future for a moment, and I’ll paint a picture of what a manufacturing operation truly looks like when its digital twin is powered by these agentic principles, or in other words a next generation paradigm shifting composable manufacturing system . In this world, a work order isn't just data on a screen; it instantly awakens an intelligent Order Agent within the digital twin - or plainly the manufacturing system. This agent immediately gets to work, dynamically negotiating with Resource Agents —the digital representations of machines, tooling, even the specific human expertise required—to secure optimal production slots and materials in the virtual space, which then collaboratively drives actions in the physical factory assisting operators in orchestrating the operation.

As raw materials enter the facility, each component, or even the nascent product itself, manifests as a Product Agent within this digital twin. This agent carries its own unique digital identity and manufacturing instructions, literally guiding operators to route its counterpart through the physical production line. It's constantly communicating its status and needs within the digital twin, ensuring it receives the precise processing at each stage in the real world. If a specific machine (a Resource Agent ) suddenly reports a slight anomaly – say, a bearing starting to warm up – its built-in intelligence within the digital twin instantly flags it. Instead of waiting for a catastrophic failure, the line's collective intelligence, orchestrated by operators using the various software agents operating within this digital twin environment, might subtly re-route the physical Product Agent to an alternative, readily available machine. Or, better yet, the affected Resource Agent might even initiate a precise, self-healing routine or schedule a just-in-time, predictive maintenance intervention, ensuring that the issue is resolved before it impacts production, all while the Order Agent helps ensure deadlines are still met.

Quality control isn't a post-production check; it's baked into every micro-decision. Product Agents and Resource Agents , leveraging their digital twin data, are continuously monitoring parameters, spotting the tiniest deviation, and triggering immediate corrective actions to ensure "right first time". This seamless, autonomous orchestration – where products find their way, orders fulfill themselves, and machines manage their own well-being, all empowered through the precise, real-time fidelity of their digital twins – transforms the factory into a living, breathing, self-optimizing organism. It’s a level of agility, efficiency, and resilience that felt like pure science fiction in the 90s, but is now can become our tangible reality.

This leads us to an operational reality where the use cases that may seem futuristic are in fact possible , for example:

• Self-Optimizing Production Lines : Imagine entire lines monitoring themselves, sniffing out bottlenecks, predicting breakdowns, and then autonomously re-routing production or tweaking parameters to keep output optimal. Empowering human operators with currently unimaginable support in orchestrating operations.
• Dynamic Resource Allocation : Agents negotiating for machines, tools, and materials in real-time, ensuring every asset is utilized perfectly, eliminating idle time. Elevating scheduling and dispatching to unheard of levels of effectiveness and accuracy. 
• Predictive Maintenance and Self-Healing Systems : No more waiting for a breakdown. Agents predict failures with incredible accuracy and can even kick off self-repair routines or proactive maintenance, slashing downtime and costs.
• Enhanced Quality Control : Agents tirelessly monitoring processes and product quality, spotting deviations instantly and triggering immediate corrective actions. This is "right first time" manufacturing, every time.
• Boosted Compliance : Automated data collection, precise procedure execution, immutable digital records – agentic systems dramatically reduce human error and guarantee adherence to the toughest regulations.
• Unparalleled Traceability : Every single action, every decision by an agent, meticulously recorded. Audit trails become pristine, investigations swift and clear.
• Driving "Right First Time" : By minimizing variability and providing real-time feedback, these systems help ensure products meet quality specs from the outset, slashing costly rework.
• Accelerated Innovation : With more efficient, reliable processes, companies can pour more resources into R&D, bringing life-saving drugs or mission-critical components to market faster.

The journey from the elegant theories of holonic manufacturing systems to the practical, jaw-dropping capabilities of Agentic AI has been long, but intensely rewarding. What started as pure academic curiosity, exploring the power of decentralized, intelligent control, has now become the very bedrock of digital transformation in manufacturing. We’re no longer just imagining; we are actively building factories that learn, adapt, and optimize themselves , powered by the incredible, collaborative intelligence of software agents, physical machines, and empowered humans. It's a testament to the enduring power of fundamental research, a strategic commitment to true digital transformation, and the relentless, accelerating pace of technological innovation.

But hold on, not so fast. Here’s where my passion often turns to frustration. We have the technology today, and more is coming fast – innovation is accelerating at an exponential rate! Yet, a fundamental problem persists in manufacturing: so many companies still don't grasp that adopting digital technology demands a profound transformation , not just a simple upgrade.

The Agility Forum , a 1990 initiative to transform manufacturing, proclaimed that we need to thrive in an environment where change is the only constant. In today's volatile business landscape – marked by unprecedented geopolitical shifts, rapid market fluctuations, and increasingly fragile global supply chains – this is not longer a theory; it's the raw truth of survival. While our research in the 90s certainly anticipated a future of greater dynamism and the critical need for manufacturing systems to adapt , even we couldn't have fully foreseen the sheer velocity and breadth of the disruptions we face now.

This intense, continuous flux means leveraging that change, embracing agility itself, as your ultimate competitive advantage. The sheer ability to adapt, to pivot swiftly, and to continuously evolve your operations is precisely what will differentiate leaders from those left behind. In this dynamic landscape, digital transformation is not a static one time event, nor is it a project with a defined end; it is a continuous process of adaptation to changing business and technological environments - hence we need to start talking about Continuous Transformation .

The core issue isn’t the lack of innovative tools; it’s the mental, organizational, and cultural shift required to truly embrace them - yes its still really about people . Real digital manufacturing means rethinking everything – your processes, your workflows, even how you do business. It’s a complete reimagining, and that's precisely why the original holonic concepts, now enabled by modern tech, offer the inspiration for a breakthrough path. Embrace this paradigm shift, or risk being outmaneuvered by those who do. The future of manufacturing is intelligent, interconnected, and increasingly autonomous, built directly on the visionary concepts laid down decades ago.