Democratization Is Not Anarchy. Learn to Deal with IT!

In nearly every conversation I have with manufacturing executives about their digital transformation programs, the topic of democratization surfaces the same way. As a problem. "How do we make sure people aren't building things outside of our control?", "How do we enforce compliance with IT governance?" "How do we prevent shadow IT from getting into production?" "What's our policy on AI-generated code?" The conversation turns to containment before it ever reaches potential. And in my experience, that is exactly the wrong place to start.

I've written about democratization as one of the 5 Pillars of Composability for years. It generates more internal friction than any other pillar — not because it is the most dangerous, but because it is the most misunderstood. Organizations treat it like a problem to be controlled. What they should be treating it like is the engine that has been quietly driving their best productivity gains for the past three decades — whether they recognized it or not.

The evidence has been right in front of us the entire time. We just keep refusing to see it.

The Proof Has Been Hiding in Plain Sight

Look at Microsoft Excel, there is no piece of enterprise technology that has driven more distributed manufacturing productivity gains than a spreadsheet application that anyone can use. Long before no-code platforms existed, Excel was what people reached for when the digital solution they needed simply didn't exist — or when the monolithic system in place couldn't provide it. From simple hour-by-hour production trackers to complex inventory management models, engineers and operators built what they needed with what they had. The gap was always there, because ERP, MES, WMS, QMS, EAM — every monolithic system of record was designed for structured, standardized workflows. The contextual, the ad-hoc, the highly specific operational problem always fell through the cracks. Excel caught them. It democratized data manipulation — it placed analytical and tracking capability in the hands of every engineer, quality manager, and production planner on the floor. IT organizations hated it. Shadow IT. Ungoverned. Risky. And yet — it worked. The productivity it unlocked was enormous, precisely because the people building the tools were the people who understood the problems.

The same story played out with Human-Machine Interfaces on the plant floor. When SCADA and DCS vendors began providing configuration environments that operators and process engineers could use directly, the pace of process improvement accelerated. The automation engineers closest to the process could suddenly instrument, visualize, and adjust without waiting months for a programmer to interpret their requirements, write a specification, queue the work, and deploy a change. Democratization of configuration capability drove productivity. Again.

The pattern is consistent: 

When you give operationally knowledgeable people the tools to solve their own problems, the rate at which problems get solved increases dramatically. This is not a hypothesis — it is a thirty-year track record.

This is precisely the type of democratization that no-code and low-code platforms have extended into frontline operations over the past decade. The shift from "IT builds, operations uses" to "operations builds what they need" compresses the lag between identifying a problem and solving it from weeks to hours. As I've described through the lens of digital maturity, organizations that make this shift don't just get more efficient — they develop a fundamentally different operational capability. They become adaptive

AI Is Closing the Last Gap — and That Changes Everything

No-code lowered the barrier to building digital solutions, but it still required learning a new environment, a new paradigm, a new way of thinking about logic and workflow. AI is closing that gap entirely. You can now describe what you need in plain language and have a working solution generated in front of you. The barrier to content creation for is approaching zero.

Think about what that means for the examples we just discussed. The engineer who used to spend days building a complex spreadsheet model to track WIP and yield can now describe the logic conversationally and have the model built for them. The process engineer who needed a SCADA supplier's configuration team to update an HMI display can now specify the change in plain language and iterate in real time. AI isn't just another wave of democratization, it super charges it. It skips no-code as the primary mechanism by which non-programmers create digital solutions.

And here is the implication that most organizations are missing: as the barrier to creation approaches zero, the value of the platform it runs on increases dramatically. Anyone can generate a solution. Not everyone is generating solutions that are version-controlled, validated, connected to the right data sources, maintainable by someone other than the person who built them, and operating within a compliant environment. The purpose-built platform — composable, human-centric, compliance-ready — becomes more critical, not less, as AI democratizes creation. Ungoverned AI generation without a platform foundation is not democratization. It is the digital equivalent of everyone writing their own procedures on sticky notes.

As I described in A Composable Agentic Framework for Frontline Operations, the emergence of builder agents — AI that helps domain experts design and iterate digital solutions in real time — is the realization of this shift. The question it raises for every manufacturing organization is not "how do we control this?" It is: do we have the platform foundation to make what our teams are about to build actually work?

And the organizational response? The same one we've seen before. Fear. The containment reflex. "What's our policy on AI-generated code in production?" "How do we prevent people from deploying things that haven't been validated?" "Who is accountable when something built with AI goes wrong?"

I am not saying those are wrong questions. I am saying they are being asked before the far more important one: what becomes possible when your process engineers, quality specialists, and production leads can build and iterate the tools they need in hours rather than weeks? That is the question that unlocks value. Governance comes second — as the framework that makes value creation sustainable — not as a replacement for asking whether value is even being pursued.

In both previous waves of democratization, the organizations that responded with blanket restriction fell behind the ones that built governance structures capable of channeling the new capability. As I've been observing as the industry traverses the digital divide, the companies pulling ahead are not the most cautious — they are the ones with a sound strategy and culture that enables effective governance.

Why Democratization Gets Managed Instead of Harnessed

The answer is structural, and it runs deep. Most manufacturing organizations still operate with a mental model inherited from the era of monolithic systems — where digital capability was scarce, expensive, and necessarily centralized. In that model, IT was the gatekeeper because it had to be. Building anything digital required specialized skills, expensive licenses, and careful change management. The architecture was fragile. A mistake in one place could propagate across the whole system. In that context, tight central control was not a choice — it was a necessity.

The decline of monolithic architectures didn't just change the technology. It changed the risk profile. Composable platforms are designed for distributed development — with version control, role-based permissions, validated templates, and isolated workspaces that contain failure to a single application or station. But organizational culture moves slower than technology. The gatekeeping mindset persists long after the scarcity that justified it has disappeared.

So we still see organizations applying the change governance frameworks designed for monolithic systems deployments to no-code app development. We see IT review boards that were built to manage quarterly release cycles now being used to evaluate whether a process engineer can add a field to a workstation app. The tools changed. The governance didn't. And the result is that organizations spend more energy suppressing the creative capacity of their most operationally knowledgeable people than they do enabling it.

There is also a subtler dynamic that I've observed consistently. Organizational skepticism tends to attach itself to democratization specifically because its outputs are distributed and visible — not because they are more dangerous than centralized systems. A poorly architected process buried inside a monolithic MES can affect the entire operation and take months to unwind. A poorly designed app built by a process engineer affects a single workstation and can be corrected in an afternoon. The distributed failure mode is actually less catastrophic. But it's more visible, and visibility triggers the control reflex — even when the underlying risk doesn't warrant it.

Democratization Is Not Anarchy. It Requires Democratic Governance.

Here is the point I want to make directly: democratization is not the absence of rules. It is the distribution of capability within a system of rules.

We have a model for this. It is called democracy. Functioning democratic systems are not anarchies — they are the most sophisticated governance structures humans have built. They have constitutions, laws, institutions, independent accountability mechanisms, and ethical norms. They distribute power not because they have abandoned governance, but because they have built governance structures capable of handling distributed power. The result — when it functions — is a more resilient, adaptive, and innovative system than any centralized alternative has ever achieved.

The comparison to manufacturing governance is direct. What I have consistently called controlled democratization means giving people the capability to solve their own problems within a governance framework designed to channel that creativity productively — not police it into submission. Policing is a dictatorial method. It is also what traditional top-down, monolithic systems use. And it is precisely why those systems generate compliance without generating adaptability.

The most well-governed democracies do not work because they have the most police. They work because they have strong institutions, a culture that understands and values the rules, and mechanisms for accountability when those rules are violated. Manufacturing organizations that want to harness democratization need to build the equivalent: platforms that enforce governance by design rather than by gatekeeping, centers of excellence that guide rather than approve, and a culture that treats accountability and empowerment as the same thing — not opposites. The power of that combination, when you've seen it operating at scale, is not incremental. It is categorical.

The Question Is Not Whether. It's How.

Your teams will use AI to build things. The only variable is whether they do it inside your governance framework or around it. Blanket restrictions don't prevent use — they push it underground, where it operates without documentation, without platform guardrails, and without accountability. That is the actual risk. And it is a risk created entirely by the policing reflex.

Every previous wave of democratization taught the same lesson. The organizations that tried to stop it accumulated missed improvements, unsolved problems, and eventually lost the people who were motivated enough to try. The organizations that built governance structures to channel it gained ground that compounded over time.

Continuous transformation is only achievable if democratization is operating at full potential. You cannot get there by treating your most operationally knowledgeable people as risks to be managed. The difference between democratization and anarchy has never been the absence of rules — it has always been the quality of governance.

Build governance worthy of the capability your teams are ready to use. Don't be the last to start.

Agentic AI in Action: What I Learned Experimenting with Operational and Builder Agents

Over the last several months, I’ve been deepening my exploration of Agentic AI within Tulip, applying the concepts I laid out in my Agentic Framework and testing them in real operational scenarios. What started as curiosity has quickly become something else entirely: a recognition that we are opening a fundamentally new chapter in how manufacturing systems are built, operated, and scaled.

As I’ve experimented with both operational agents—those that support frontline teams in real time—and builder agents—those that help design, generate, and improve digital solutions—I am realizing how deep and wide the impact is going to be. The more I explore, the more use cases reveal themselves, and the more explosive the potential becomes. Its much more than I initially thought, and I have been thinking in terms of multi-agent systems for manufacturing since the 90's! Agentic AI (multi-agent systems powered by generative AI) are a much bigger step change than I would have imagined to how we think about creating and running manufacturing solutions.

Let's start with a brief recap. Operational agents extend the capability of the production system realizing digital twin capabilities in ways that introduce reasoning, interpretation, and contextual understanding directly into the work being done on the floor. Builder agents open the door to a multithreaded, parallel engineering process that fundamentally changes the speed and depth at which solutions can be created. It feels less like a “copilot” assisting a developer and more like a coordinated team of SMEs designing solutions - Augmented Lean at hyperspeed!

This combination—augmenting frontline execution while accelerating the design and iteration of digital systems—points to a future where humans orchestrate agent ecosystems rather than manually building every piece of a solution themselves. This brings me to the motivation for writing this post that became clear to me in a recent customer conversation about DCS integration in support of a digital solution for pharmaceutical manufacturing of clinical drugs.

Reimagining Composable Integration with DCS and ISA-88 Through Agentic AI

The question I was asked recently wasn’t the classic “How do you integrate an MES with a DCS?”—that problem has been addressed in many different ways in the traditional architectures. The real question was far more interesting: How do you integrate a Composable MES built on a Frontline Operations Platform with a DCS or other ISA-88 based batch system?

In a traditional MES world this integration immediately triggers a familiar debate about how to partition the recipe across systems, define boundaries of responsibility, and reconcile master data, recipe models and equipment hierarchies. And that debate is almost always constrained—if not outright dominated—by the rigidity of monolithic MES platforms. The architecture drives the discussion more than the operational needs do.

But in a composable environment, the constraints that shaped those historical debates simply don’t apply. Let's look at what happens when you apply a composable, agentic model.

1. Composable Apps Remove the Traditional Constraints

In a composable architecture, apps are not bound to a predetermined master data model or recipe structure. This means that there is no need for recipe model partitioning, no need to replicate equipment hierarchies, no predefined S88 recipe model to map into. 

This flexibility removes the most painful barrier in traditional MES ↔ DCS integration: the structural reconciliation of recipes and equipment models. The DCS can continue using its ISA-88 representations. Tulip apps can represent the process in the most intuitive and useful way. And the integration simply becomes the mapping of meaning and intent between the two worlds. You design the representation of the process that makes sense for your operation—not the one dictated by the systems.

Composable solutions also shift the perspective entirely by taking a human-centric, activity-based approach organized around the physical reality of the shop floor. I fully recognize that, in the traditional monolithic MES world, standard models like ISA-88 were considered essential—they provided structure, discipline, and a shared language for process-centric systems. But composability represents a fundamentally new paradigm. 

To democratize operational systems and bring them closer to frontline work, we must prioritize operator-first design rather than forcing every SME to become a master of S88 modeling. ISA-88 remains invaluable for process control, but the surrounding operational systems must be simplified and democratized so they can work hand in hand with the distributed nature of modern manufacturing. Composable platforms do exactly that: they allow process engineers, chemical engineers, and frontline teams to collaborate without being constrained by rigid, expert-only models.

This alone would dramatically simplify integration. But the real breakthrough comes with agents.

2. Builder Agents Enable Multithreaded, Generative Solutioning

Builder agents transform integration work from a linear, manual design activity into a parallel, iterative, and generative process. They don’t just help you “build faster”—they fundamentally change how solutions are conceived and engineered.

I experimented with builder agents that can ingest a full ISA-88 recipe structure and conduct deep introspection on it: understanding the procedural models, identifying phase logic, parsing parameter definitions, and extracting the relationships between equipment, units, and operations. It then suggested mappings, app contexts, and design patterns—not only based on expert interpretation of the ISA-88 standard, but also from what they’ve learned across existing apps, historical integrations, real-world performance of similar solutions and critically expert knowledge of composable design principles. In other words, these agent combines domain expertise with empirical insight, offering design options that reflect both best practices and operational realities.

This alone already feels like having a team of process engineers and MES architects working in hyperspeed. But the true power emerges when operational agents begin contributing dynamic intelligence into that design loop.

Operational agents provide real-time feedback about process variability, material availability, logistics implications, quality status, or unexpected delays. They can accommodate non-optimal or evolving recipes by dynamically dispatching materials, reallocating resources, or bringing the right expertise into the process at the right time. This dramatically increases operational resilience and reduces risk—because the system adapts rather than stalls when confronted with real-world complexity.

And then there’s compliance...

Specialized builder agents trained on GxP principles can support on-the-fly risk assessments, propose mitigation strategies, and generate validation documentation as part of the design cycle. Operational validation agents can take this further, enabling true continuous validation—monitoring execution conditions, evaluating deviations against risk models, and providing traceable explanations for decisions. Compliance becomes embedded, in fact native, in the system rather than layered on top.

When you step back and think about the implications, the potential is almost infinite. The combination of builder and operational agents elevates agility and compliance to levels we’ve never imagined in traditional MES architectures and design approaches. It enables systems that are not only faster to build, but continuously improving, self-aware, and aligned with both operational needs and regulatory expectations.

This is the beginning of a new era in how manufacturing solutions are designed, executed, and validated. It feels like a generative design process running at hyperspeed. Not a single assistant helping you code tasks faster — but a team of AI experts collaborating to create a complete solution.

And this unlocks something we have never had before in manufacturing software: the ability to rapidly iterate and explore multiple viable integration architectures before committing to one. This is enormously valuable in an ISA-88 context, where recipes, equipment logic, and operational variability rarely align perfectly.

Seeing the Explosion of Use Cases

If you let the builder and operational agents begin to work together, the number of possibilities just explodes - its the first step towards a Multi-Agent System (MAS). These agents don’t simply execute tasks—they learn, reason, and collaborate in ways that constantly reinforce and expand what’s possible. Suddenly, problems that used to take months of engineering effort can be tackled in days—or even hours.

Some of the notable and exciting use cases I’ve come across include:

• Automatically mapping process logic into app structures.
• Rapidly generating compliant workflows for regulated environments.
• Exploring recipe variants and operational scenarios through simulation.
• Using agents to assist in validation and documentation.
• Dynamically interpreting and adapting recipes at runtime.
• Applying cross‑system reasoning to catch inconsistencies early.
• Coordinating multiple agents to design complete production solutions.

Each one opens a new door—where imagination, not technical limitation, becomes the real constraint.

What stands out to me most is the sheer power of these systems and what they make possible. Seeing a builder agent reason through an ISA-88 recipe, or an operational agent adapt to a real-time process disruption, feels less like traditional programming and more like working alongside a tireless, highly capable collaborator. My role has shifted from hands-on integration to guiding and steering intelligent agents—and that shift fundamentally changes how we think about manufacturing systems. The emerging dialogue between human expertise and machine reasoning opens up an entirely new design space, one where adaptability, resilience, and scale are no longer constrained by human bandwidth.

I’m also starting to document and share some of these experiences through AI‑generated videos, another capability I’m learning to use. They’ve turned out to be a surprisingly powerful way to show what agentic systems can do—and to help others visualize these new forms of collaboration on the shop floor. It’s a learning journey in itself, but it feels like the right extension of this exploration: using AI not only to build better systems but to communicate and learn in entirely new ways.

Seeing all of this unfold up close, it’s clear we’re not just evolving automation—we’re watching Holonic concepts come alive, the manifestation of the new digital manufacturing reality.

Crossing the Digital Divide: Human-Centric Manufacturing in a Multi-Agent World

When you step back and look at what emerges from the combination of builder agents and operational agents, it becomes clear that this is not just another productivity boost or architectural evolution. It is a convergence point — one that aligns remarkably well with how manufacturing operations have always been run by humans.

Manufacturing has never been a purely deterministic, rules-based environment. It is adaptive, situational, and deeply human. Engineers design intent. Operators respond to reality. Supervisors balance constraints. Quality professionals manage risk. For decades, our digital systems have struggled to reflect this reality, forcing people to adapt to rigid models and monolithic workflows rather than supporting the way work actually happens.

Multi-agent systems change that equation by enabling digital systems to finally reflect the way manufacturing actually operates—through parallel problem solving, continuous adaptation, and coordinated decision-making across people, processes, and technology.

Builder agents mirror how engineering teams work: exploring options in parallel, iterating designs, learning from past outcomes, and continuously refining solutions. Operational agents mirror how plants operate: responding to variability, adjusting to constraints, coordinating people and materials, and managing risk in real time. Together, they form a digital system that finally behaves the way manufacturing organizations behave — collaborative, contextual, and resilient.

 

This is profoundly human-centric, because it aligns digital systems with how manufacturing teams actually operate — dynamically, collaboratively, and contextually.

It also brings into sharp focus a theme I’ve been writing and speaking about since the late1990s. For decades, we have tried to digitize manufacturing by automating tasks, enforcing standard models, and embedding rigid logic into systems. That approach delivered value, but it also created the very constraints that now limit agility, scalability, and innovation.

What we are seeing now is the realization of a different paradigm — one where digital systems augment human reasoning instead of replacing it, where composability replaces monoliths, and where intelligence is distributed across agents rather than centralized in static applications. This is the paradigm shift I’ve been pointing to for years, and it is finally reaching a practical, scalable form.

The convergence of composable platforms, agentic AI, and multi-agent collaboration marks a true inflection point. We are no longer just modernizing legacy systems. We are crossing the digital divide — moving from systems that support transactions to systems that participate in operations.

The potential here is vast! Agility, resilience, productivity, and compliance are no longer trade-offs. They become reinforcing outcomes of a system designed around human workflows, continuously learning agents, and real-world context.

This is not the end state — it’s the beginning. But for the first time, the tools, platforms, and paradigms are aligned. And that alignment is what makes this moment different from other transformative eras that came before. And it will not stop - that is why we call it continuous transformation! 

Experimenting With AI as a Creative Assistant: How I Created My Recent Videos

Over the last few weeks, I have been playing with AI as a creative assistant. Since my multimedia creative skills are - let's say sub par, I have used AI as a partner, or assistant in. The goal is to enhance content to promote knowledge sharing in manufacturing. Not AI as a replacement for expertise, but AI as a way to translate expertise into formats people actually absorb.

As part of this, I created two videos and I wanted to share the behind-the-scenes story of how I made them, what tools I used, and what I learned along the way.

Digital-First & Composable: The Future of Pharma Manufacturing Design

 

Grandpa Learns AI.

Why I’m Doing This

A few months ago, I was interviewed by a research team connected to the World Economic Forum. They’re studying the future of work and education in the digital age—specifically how people learn and adapt in environments that are changing faster than ever.

That interview got me thinking: Manufacturing is changing. Digital tools are changing. But our learning models haven’t caught up.

And if I’m being honest, my own communication style tends to be direct, dense, and sometimes… too straight to the point. Great for experts, not always great for everyone else.

So I wanted to see what happens when I let AI help me explain the concepts I care about—but in a completely different voice. So I leveraged the generative AI tools (specifically I used NotebookLM from Google for no other reason than availability - its free for now) and I’ll admit: I expected the usual AI fluff but the results was… surprisingly good.

With some well thought out prompting and iteration NotebookLM didn’t just rewrite my explanations—it transformed them into something more approachable, more story-driven, and dare I say it, more human. It brought out a teaching style that’s very different from my natural tone.

Transforming the Content

The first video was really just a "let me just feed some content and see what I get...". I recently wrote a whitepaper titled "Digital-First and Composable— A NewParadigm for ConceptualFacility Design in Pharmaceutical Manufacturing" about why its critical to take a digital first approach to the design of pharmacuetical manufacturing facilities. (Its not published publicly yet, but let me know if you are interested in a copy)

I wanted to test whether NotebookLM could help explain this somewhat deeper and more technical topic in a different way to non technical people. Basically as if you are explaining this to your grandmother. This is a known exercise that is commonly used to create a simplified and easier to understand content of technical topics. It was something I typically asked my students to do when defining their research topic, e.g. the The Feynman Technique. 

Here AI surprised me again. It took my content and created a narrative that felt clear, structured, less consultanty and was like a guided tour of the future of manufacturing It delivered the same intellectual payload—but in a format that's easier to digest for people who aren’t neck-deep in these topics every day.

For the second video I fed it the transcript from my WEF conversation about how people learn, and the AI picked up on a few of the stories that I used to exemplify how to explain new digital concepts to the industry. It took the my grandpa story  and created a story about a grandpa discovering AI for the first time. It turned a complex topic into something relatable and a little emotional. 

I shared both the whitepaper and the video I created with customers and colleagues and the feedback was that the video is by far more valuable than the whitepaper. The surprising part was that people actually learned from it. They weren’t just “getting the point.”, they were experiencing it - maybe even feeling the point. 

Why Use Personas?

One thing that became clear through this experiment is that who explains something matters just as much as what is being explained.

In manufacturing, we’re all guilty of communicating like… well, manufacturing people. Precise. Direct. Dense. Focused on efficiency. It’s great for experts, but not always for learners who don’t live and breathe MES architectures or Pharma 4.0.

This is where personas come in. Sometimes the most effective way to teach a technical idea is to have it explained by someone who is not you.

• A grandpa.
• A mentor.
• A line worker.
• A curious newcomer.
• A future digital assistant.

AI helped generate voices and storytelling styles that I simply wouldn’t have used myself. And that difference matters. It’s disarming. It opens people up. It creates emotional connection. It makes the content stick.

But—and this is important—it didn’t invent anything on its own. It worked because I gave it:

• the right context
• the right source material
• the right stories
• and a clear intention
• grounded in my decades of experience

AI can’t fabricate expertise but it can translate expertise into a form that reaches people where they are. The personas made the learning accessible and my context made it accurate. It’s a powerful combination.

What I Learned

In the end, this experiment taught me that AI can significantly expand my creative range—but only when it’s grounded in the right context. AI didn’t magically produce valuable content; it was effective because it worked with my whitepaper, my WEF interview, my research, and my own stories from years in manufacturing. When AI has that depth to draw from, it becomes an amplifier rather than a generator of fluff. 

I also realized how essential storytelling is for real learning. The emotional layer—whether it was explaining a digital-first facility as if to a grandmother or turning my grandpa anecdote into a touching narrative—made the concepts stick in a way traditional technical writing rarely does. And using personas was far more powerful than expected: having someone unlike me tell the story didn’t dilute the expertise; it made it more approachable and meaningful. What this ultimately reinforced is that AI isn’t the expert—it’s the assistant. It can translate, reframe, and humanize ideas, but only when guided by intention and supported by real experience. And that, I think, is exactly how AI will create value: by helping us communicate better, teach more effectively, and unlock new ways to share the knowledge we’ve spent years building.

Composability, Governance, and the Future of Agentic AI in Manufacturing

We’ve all heard the stories: “Somebody created a solution in just a few hours with no-code or vibe coding…”.

It’s exciting, right? Engineers solving problems in record time, building digital tools with nothing more than intuition, creativity, and a bit of AI support. This is the promise of democratization—Agentic AI empowering everyone to innovate and improve.

And in many contexts, that speed is a superpower. But in manufacturing, the story is more complicated. Operations are inherently complex: machines, materials, people, processes, schedules, quality checks, and compliance requirements all interact in a dynamic web that is nonlinear, a complex adaptive system. In such environments, even small changes can cascade unpredictably, amplifying into disruptions far greater than their cause—an inherent feature of systems where interdependencies drive emergent outcomes.

This is where the risk lies. If an unguided agent makes the wrong decision in such an environment, things can go wrong very quickly—and often in ways that are difficult to anticipate or trace. The result might be downtime, compromised product quality, production delays and at worst safety issues. But the bigger danger is cultural: when something fails spectacularly, it doesn’t just cause operational damage—it can scare the organization away from using the technology at all.

Instead of unlocking incredible productivity, one misstep can trigger a mindset of “we don’t dare do this again…”. That’s not just a lost opportunity; it’s a setback that can stall digital transformation for years.

Digital Maturity Gaps

With the incredible pace of innovation of digital technologies and specifically Agentic AI, we have to acknowledge a hard truth: most manufacturers are still not fully ready to capitalize on this technology. The foundations are often shaky, and if we introduce solutions built by and with agents into this environment without addressing the gaps, the risks multiply.

Some of the most common issues include:

• Fragmented data that prevents end-to-end visibility.
• Disconnected IT and OT systems that block seamless collaboration.
• Cultural skepticism and lack of digital maturity that slow adoption and erode trust.

These are not new problems. I discuss them in detail in this blog.  Our traditional models both in technical architecture and deployment of operational solutions for manufacturing were never designed for the agile, composable, and connected environments we’re striving to build today. In addition we can't ignore that in any given manufacturing facility there are machines and systems of varying ages and legacy status.

Consider a Scenario

An enthusiastic engineer uses vibe coding to create an agent that dispatches materials to keep machines and operators as busy as possible. At first, everything looks good, machines are running at full capacity, operators always have work, and parts move quickly into assembly.

But no one realized the agent doesn’t properly recognize WIP limits or downstream capacity. It keeps dispatching jobs even when assembly stations and testers can’t keep up. The result: piles of excess material stack up between stations, overflowing into walkways and creating unsafe conditions for operators.

In a matter of hours, what was meant to boost productivity causes overproduction, flow disruption, and a serious safety hazard. The line is forced to stop because the system created too much of it, in the wrong place, at the wrong time. 

This isn’t hypothetical. It’s the kind of risk that emerges when Agentic AI is unleashed without governance. And unlike traditional tools, agents can act autonomously and at scale, amplifying errors at a speed we may not be able to react to.

Managing Complexity with Composability: Governance, Framework & Platform

Given this reality—where digital maturity is uneven, legacy systems abound, and the dynamic nature of manufacturing operations—the arrival of Agentic AI presents both an enormous opportunity and a serious risk. Agents are coming at us fast, and their power lies in democratization and speed. But those same qualities, in an environment as sensitive as manufacturing, can amplify problems just as quickly as they solve them.

This paradigm shift cannot be left to chance. To harness Agentic AI safely and effectively, we need to approach it in an organized, managed, and focused way. That requires: governance, framework, and platform all working together and grounded in composability.

• Governance gives us the rules, guidance, and guardrails to ensure reliability, safety, quality, and compliance are never compromised. Governance is not just about control; it actively helps organizations overcome barriers like fragmented data, siloed systems, and cultural skepticism by providing structure, discipline, and confidence. Done right, it helps in the transformation of the manufacturing environment from both a technical readiness (data integrity, system integration) and an organizational readiness (culture, mindset, trust).

• Framework provides the structure to make governance actionable. In A Composable Agentic Framework for Frontline Operations, I laid out how agents can be defined by type, given clear goals, and aligned through the artefact model. The framework ensures that agents are not just scattered tools but part of a purposeful, multi-agent system that reflects and supports real operations.

• Platform is the enabling layer. An Agentic manufacturing operations platform purpose-built for frontline operations makes it possible to apply governance and framework seamlessly—across both authoring (building digital solutions) and execution (running the production line). Unlike generic vibe coding environments, such a platform is designed specifically for process engineers and operations teams. It embeds an understanding of how manufacturing works—constraints, variability, compliance, and safety—and provides tools to build solutions quickly while remaining aligned with operational best practices. Most importantly, it enables the creation and deployment of operational agents at scale that are are integral parts of the production system itself.

And underlying all of this is composability. Composability ensures that agents and solutions don’t exist in isolation, but as modular, human-centric, bottom-up elements of a system that adapts as needs evolve. Digital transformation success depends on treating composability not as an IT concept, but as an operational paradigm. In fact, composability was conceived for multi-agent systems (MAS)—and Agentic AI now makes that vision practical.

Final Thoughts

I think that we all agree that Agentic AI despite all the hype is not a fad—it’s here, its real and it’s already reshaping how work gets done. But to harness it safely and effectively, we need to recognize the two distinct scenarios where agents play a role:

1. In building and engineering: where agents help create digital content, processes, and solutions. Here, governance ensures what gets built is valid, safe, and aligned with operational goals.

2. In live operations: where agents support and even run production activities, interacting with machines, data, and humans in real time. Here, governance ensures reliability, compliance, and resilience in execution.

Both scenarios are powerful—they are needed but both also carry risks if unmanaged. And in manufacturing, where operations are complex and dynamic, small missteps can cascade into detrimental consequences.

That is why governance, framework, and platform are critical. Governance provides the rules and guardrails; the framework gives agents structure, goals, and alignment through the artefact model; and the platform operationalizes it all, giving process engineers and frontline teams the environment to deploy agents as integral parts of the production system, and all of this at scale.

And beneath it all is composability. As I’ve argued before, composability was conceived for multi-agent systems—modular, autonomous, and collaborative components working toward shared goals. Agentic AI now makes that vision practical on the shop floor.

The challenge ahead is not whether manufacturers will adopt Agentic AI, but whether they will do so in a way that balances speed with safety, democratization with discipline, and autonomy with alignment. Done right, it promises resilience, reliability, and the kind of productivity gains that digital transformation has always aspired to deliver.

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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.

Digital Maturity Embracing the Paradigm Shift with Composability

In my last post, The 5 Pillars of Composability, I broke down how composable systems have to be bottom-up, agile, democratized, human centric and compliant to enable a resilient digital  manufacturing environment. However, these pillars don't standalone and you may have noticed that the graphic drew the pillars within a structure, i.e. a house. Yeah a bit cliche but its a simple way to drive the point - the foundations is connectivity and data integrity while the roof is digital maturity. Without connectivity to reliable data and a high level of digital maturity, the benefits of composability can be diminished. 

• Data integrity ensures that the digital solutions operate on accurate, consistent, and trustworthy data, preventing breakdowns in decision-making or system performance. High-quality, accurate data is essential for making informed, evidence-based decisions
• Digital maturity enables organizations to effectively adopt composable architectures, ensuring they have the technical capabilities, culture, and processes in place to take full advantage of modular solutions. 

Together, data integrity and digital maturity complete the story of composability by ensuring that organizations can both build and sustain these flexible, adaptive systems in a reliable and future-proof manner. In this post I want to dive deeper into these concepts as they are foundational concept that propels us forward in the digital paradigm shift to reshape manufacturing operations.

Digital Maturity, Connectivity & Data Integrity complete the composability model

What is Digital Maturity?

Digital maturity represents an organization’s capacity to leverage digital tools and processes effectively based on their strategy with the objectives of significant increases in productivity. It's not a simple matter of capabilities related to adoption or implementation new technologies but rather about integrating them strategically to align with long-term goals. As companies mature digitally, they move beyond basic digital adoption to foster seamless connectivity across systems, data transparency, an empowered workforce and with that comes order of magnitude productivity improvements - the ultimate goal for transformation.

A digitally mature organization is one where digital tools support real-time decision-making, democratized technology access, and predictive insights - aligning perfectly with the benefits of composable principles. This is also what the Pharma 4.0 operational model prescribes, that manufacturers need to do more than automate - they need to integrate everything from operations to compliance in a way that’s seamless, agile, and deeply data-driven.

What is Connectivity & Data Integrity?

Its not news that data must be accurate, accessible, and trustworthy across all systems for true digital maturity. It must be connected, collected, contextualized and stored to ensure that data collected from production lines, suppliers, and product design all feed into a single, reliable source, creating actionable insights and reducing costly errors. Yet surprisingly it still is very much a challenge in many solutions that I encounter. Mostly in legacy situation, implementation of monolithic system, but also if not considered appropriately in newer digital technologies.

Connectivity in manufacturing is all about creating a seamless flow of data across systems, devices, and people. Imagine every machine, sensor, and workstation talking to each other and feeding data into a single network that anyone can access in real time. When systems are connected, it’s like moving from an isolated set of puzzle pieces to seeing the whole picture. Connectivity enables manufacturers to understand what’s happening on the production floor instantly, respond to issues faster, and improve coordination across departments. For example, in a highly connected factory, when a machine experiences a slowdown, that data can flow directly to maintenance teams and operators, letting them address the issue right away.

But connectivity is only as useful as the quality of data being shared, which brings us to data integrity. Data integrity is about making sure that information is accurate, reliable, and complete across its entire lifecycle. It’s not just about having data; it’s about having good data you can actually trust. In the Pharma 4.0 model, where data integrity is critical, maintaining high-quality data is a must, especially for meeting strict regulatory standards. This means putting practices in place to ensure that data isn’t duplicated, corrupted, or altered improperly, so everyone—from operators to auditors—can make decisions with confidence.

Together, connectivity and data integrity are the backbone of any digitally mature operation. They enable real-time visibility, reliable decision-making, and the flexibility to adapt to change. Without them, even the best technology can fall flat. So, as manufacturers embrace digital maturity and composability, focusing on solid connectivity and data integrity will be crucial for a smooth, resilient operation.

The journey from Technology Adoption to Strategic Transformation

Many manufacturers today are adopting digital tools, but there's a significant difference between early digitalization and achieving digital maturity. A mature digital approach emphasizes:

1. Strategic Data Utilization: Digital maturity involves a shift from collecting data in isolated pockets to having unified, actionable insights. For manufacturers, this means no longer relying on static, siloed data but leveraging real-time insights that span from the shop floor to the boardroom. Yes, this in a way nothing new and really dates to Industry 3.0 concepts - however with new digital tools this has become and achievable reality.

2. IIoT & Interoperability: Digitally mature systems don’t merely integrate; they interoperate, embodying the composable principle of Bottom Up where IIoT components are autonomous and collaborative. Composable architectures are inherently emergent in both design and control - the manufacturing solution is required to evolve with minimal friction.

3. Human-Centric Technology: In a departure from an automation focus, the current paradigm shift places people at the center of the digital equation. Technology becomes an enabler for employees, from line operators to managers, allowing them to respond dynamically to changes and resolve issues swiftly.

4. Resilient and Adaptive Workflows: A composable manufacturing ecosystem relies on digitally mature workflows that can adapt to disruptions, whether due to supply chain variances or unexpected equipment breakdowns. A digitally mature manufacturer leverages their digital capabilities to enable resilience, be predictive and adaptive.

The digital transformation journey towards order of magnitude productivity improvements

The path to digital maturity requires a tailored, strategic approach that elevates an organization from a technological upgrade to a business transformation—one that enables agility, resilience, and sustainable growth. The first step in this journey is to assess and align digital initiatives with overarching business goals. Defining what a mature digital state means for each organization—whether it's minimizing downtime, improving product traceability, or streamlining supply chain management—is critical. Aligning digital initiatives with operational excellence or lean initiatives by implementing data-driven approaches to cut down production waste and achieve near-real-time optimization are critical. Drive value by prioritizing areas where digital maturity will have the most impact on operational outcomes.

A characteristic of digitally maturity is how well your organization is equipped to handle the ever-evolving challenges and capitalize on new opportunities. Embracing composability allows your organization to not only keep pace with the current demands but to thrive in the future - thrive with the accelerated pace of digital innovation. Digital transformation should be more that mere adoption of new technology - it is embedding it deeply in your operational fabric, enabling sustainable growth and resilience in the face of change.