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.

Orchestrating Digital Solutions With Multiple Perspectives

Contemplating the complexities of a manufacturing system has long been an area that I find deeply interesting. I find that its processes, equipment and technology relies on the contributions of diverse stakeholders, each with their own expertise. They work in concert with diverse technologies, physical and digital, to ensure smooth efficient operation that is continuously improving and adapting to changing business needs.

In the pre-digital world, manufacturing system design often operated in silos, as if orchestrating each instrument to play its part in isolation. The design process of monolithic solutions needed this approach. Engineers, the conductors of this orchestra, had to meticulously plan the production flow one instrument at a time in order to manage the complexity. The watchful eye of the project manager controlling scope, prioritizes and cost optimization meant allocation for different musical sections. Then when putting it all together the silos became painfully evident thus leaving it to the frontline operators, the tireless instrumentalists, to make it all work together and bring the production plan to life.

In the digital paradigm the complexity of a manufacturing system has to be understood using a multi-perspective design approach that dismantles these isolated sections and creates an orchestra where all instruments collaborate. Engineers contribute their technical knowledge, similar to the composer's understanding of musicality and frontline operators, with their firsthand experience, identify potential shortcomings and suggest improvements in workflow and ergonomics, akin to the instrumentalists providing feedback on the score's playability.

This collaborative approach fosters a holistic understanding of the system. It ensures that the designed solution translates seamlessly into the realities of the production floor.  Furthermore, the involvement of frontline operators, like the instrumentalists practicing together, fosters a sense of ownership, crucial for system adoption and success.

Agile iteration is the secret weapon that allows for continuous refinement throughout the building process. Just as an orchestra rehearses a piece, prototypes are built, tested on the shop floor, and feedback is rapidly incorporated. This ensures the design remains adaptable to unforeseen challenges and user needs. An agile approach allows for quick prototyping of the layout, followed by simulations and feedback from operators on ergonomics and workflow. This continuous loop of design, test, and refine leads to a more robust and user-friendly system. Therefore digital technology needs to support an iterative bottom-up adoption (design, deployment, implementation, integration, etc). In a way its the merging of Agile, from the software domain, and Continuous Improvement which are arguably the same fundamental concept. Both are "bottom up" methods to manage an ever changing environment, designing one step at a time, solving one problem at a time. 

Most pre-digital technology solutions used a top-down (waterfall) design everything first - then implement approach. This does not work in the digital paradigm and therefore digital technology has to not only allow but be a catalyst to these cycle of improvement and change. The magic ingredient here is the no-code frontline operations platform. These platforms empower non-technical personnel, like the frontline operators, to participate actively in the design process. Just as an easy-to-read score allows each musician to contribute their expertise, the platform allows operators to input data, propose changes to workflows, and visualize the impact of those changes. This democratizes the design process, ensuring valuable frontline insights are readily captured and integrated.

To support the bottom up approach - one that takes a problem by problem step wise path to building shop floor solution its important to have a consistent and repeatable design process. The core of the design should have set of  perspectives that effectively support the iterative process and allow for a holistic design. This is not as easy as you may think and needs a methodical study of design principles. This is another interest area of mine and I wanted to share some background before we come to the method that I find most effective. 

Let's start with a holistic design method such as the CATWOE framework that lays the foundation for a collaborative approach. Stemming from the Soft Systems Methodology (SSM), CATWOE stands for Customers, Actors, Transformation, Worldview, Owner, and Environment. By examining the system through these six lenses, we ensure all stakeholders are considered and their perspectives are integrated into the design process. This fosters a sense of shared ownership and leads to solutions that are not only technically sound but also operationally feasible and user-friendly. 

The CATWOE Framework

Another multi-perspective design approach that is interesting in this context is TOGAF (The Open Group Architecture Framework) standard. TOGAF promotes an enterprise architecture framework that considers the needs of various stakeholders across the organization.  Similar to the CATWOE framework, TOGAF emphasizes understanding the Business Architecture (customer needs, business goals), the Information Architecture (data flows), and the Technology Architecture (systems and infrastructure). By incorporating these diverse perspectives, TOGAF ensures a holistic view of the entire enterprise, just as the multi-perspective design approach ensures a holistic view of the manufacturing system. 

The TOGAF Standard

This synergy between the two approaches creates a robust foundation for designing efficient, adaptable, and user-centric manufacturing systems. SSM and CATWOE were the basis of methods I devised during my research in Holonic Manufacturing Systems, the archetype of modern IIoT solutions. The design approach I am defining here is inspired through CATWOE and a multi-perspective method that was developed by some of my mentors (Jens Bruun and Lief Poulsen) and based on TOGAF. Its a simple holistic design approach that allows a very effective way to move forward with the dynamic duo of agile iteration and no-code platforms.

The process has three sequential steps, each taking a unique perspective of the overall operations and that build on each other. Iterating through this sequence for each solution gradually uncovers the holistic design of the digital system. 

The three perspective for digital solution design:

1. Identify and clarify Business Objectives to find and prioritize the use cases in scope. Solutions should be designed to support frontline activities and business processes that a company has put in place in order to accomplish some specific strategic goals, i.e. the Business Objectives.
2. Define and understand the Physical Operations where the use cases of the digital solution will be implemented. Understand how the physical space, processes and value stream are laid out to meet the defined objectives. What is the physical flow of materials and operators, where are operators performing activities, what equipment is being used, and how the overall process integrates, material flow, equipment sharing, and frontline operators.
3. Understand the Activities and Business Processes that have been implemented to drive the operational value stream in the physical operations space. What activities do the operators perform in order to execute the manufacturing process, what systems and data are used and needed, where does an operation start and end, how is material moved and what are the production control mechanisms, what are the logistics of the operations and how do frontline operators interact with materials and equipment.  

The synergy between these three elements creates a dynamic and responsive design process. The multi-perspective approach fosters inclusivity, agile iteration ensures continuous improvement, and the no-code platform empowers frontline participation. This collaborative environment leads to the creation of digital solutions that are not just efficient but also user-friendly, sustainable, and adaptable to future challenges.

In conclusion, a successful manufacturing system requires a conductor – a multi-perspective design approach with agile iteration and a no-code frontline operations platform. This approach fosters a collaborative environment where diverse voices are heard, leading to a more robust, efficient, and user-centric manufacturing system. As manufacturing landscapes continue to evolve, this dynamic and responsive approach will be instrumental in ensuring long-term success, just as a well-rehearsed orchestra delivers a powerful and moving performance.

OK, lets talk ISA-95!

ISA-95 - How does it fit and what is its impact in the new digital manufacturing paradigm? No need for introductions and summaries of both topics, there is more than enough written on this to cover that. 

Let's start with a reminder that ISA-95 was developed and became a standard a long time ago and both technology and manufacturing has changed since then. It is based on the Purdue Model that dates to 1990s and relies on functional decomposition in general. Therefore at a minimum we should be critical and understand what has changed in order to reflect on the standard - specifically what is still valid and what is not, or needs to be changed. 

ISA-95 attempt to provide a holistic representation of how manufacturing operations should be supported with information systems. Being a standard it offers a double-edged sword. On the positive side it promotes efficient implementation, reduced risk, ease of integration and fosters trust in solution scope. However, standards can also stifle innovation by limiting creative design approaches, and adherence can be costly for manufacturers, especially companies with limited budgets. 

ISA-95 functional model pyramid where lifecycles intersect at operations (from "Beyond the Pyramid: Using ISA95 for Industry 4.0 and Smart Manufacturing", Dennis Brandl)

We have been using the standard for decades and it has helped many companies to clearly define and solve the shop floor control challenge. As such it was mostly used as a reference to define the solution architecture. For solution suppliers it was used mostly to show completeness of their products, i.e. in level of compliance to the standard which helped companies understand what they are getting and what no. The very structured approach that the standard brings certainly helped in effective integration and information exchange. However in the digital manufacturing realm where IIoT and composability is gaining acceptance this presents new challenges:

• Rigidity: The new paradigm emphasizes adaptability and real-time decision-making. The hierarchical structure of ISA-95 might seem somewhat rigid for these needs. Modern systems favor a composable approach for better scalability and networked IIoT architectures with edge and cloud components.

• Data Deluge: The explosion of data from the IIoT network of devices, apps, machines, etc. puts a strain on the traditional ISA-95 framework. We need to allow for real-time data processing and analytics at the "edge" (closer to devices) alongside centralized enterprise systems and cloud services. The standard dictates a central Manufacturing Operations Management (MOM) system for data routing. In an IIoT solution nodes communicate at multiple levels in a dynamic networked architecture with edge and cloud components.

• Gaining Insights: Extracting insights from data is crucial in digital systems and ISA-95 doesn't explicitly address advanced analytics needed to support both the volume and variety of data. It also sets a very rigid structure for data that doesn't lend itself to advanced analytics and AI/ML technologies. 

So Does this mean ISA-95 is obsolete? Not entirely. The core principles of data hierarchy and information flow remain valuable. One of the main thing that I find valuable are some of the patterns in the data models. The core hierarchy concept of organizing data flow into different levels (field devices, automation, operations, and business planning) remains a valuable framework for understanding information flow in a manufacturing setting.  It is not surprising, a lot of experience and time has been put into generalizing how to model production and process and these can be valuable when contextualizing content in next generation digital system. B2MML also provides some valuable insights in to ERP information sharing. But at the same time be warned, use the patterns to add context to data at the source, don't fall into the pit of rigidity and global one size fits all data models.  

So where does ISA-95 go from here (see Walker Reynolds interview Dennis Brandl)? Some advocate evolving the standard but I question the value. Not because of its relevancy but I fundamentally questions if we really need such an overarching holistic standard in the manufacturing operations space. In other words is it even worth the effort? As it stands we can leverage what we need from the standard, as mentioned above, and use these to accelerate adoption of the new paradigm. Let's not reinvent what already works but spend time to build out the principles of flexibility, data management, and openness into specific standards that provide targeted value. There is already plenty of effort and initiatives in this space such Sparkplug B, OPC UA, CESMIIs SM Profiles, Asset Administration Shell (AAS), UNS, etc.

We should also take into account that we are not done transforming. We are in the thick of the shift to the new paradigm and as such technologies are still being developed and some have not yet even emerged. Trying to standardize in a world that is still changing does not yield much. We can however be aware of the standard's relevancy and where if falls short. Focus on the need for agility, the data rich digital systems that are being adopted, and most of all on composability. We have to ask the hard question of the value of a holistic standard when we have citizen developers chatting with no-code systems to create content. So use what is relevant and evolve into the digital realm where it makes sense. Even a Tesla uses standard wheels and tires as a core component, no need to reinvent the wheel there! 

How Electricity Goes Around the Bend & Where is the Electricity Manager?

The Ghost Town

I recently was on a motorcycle ride to Bodie, a gold rush ghost town in California that is now a state park. I was fortunate enough to be with Mark, who apart from running motorcycle adventure rides, who is also is a bit of a gold rush history buff. He told a story about how electricity was perceived during the boom days at Bodie.

Bodie SHP, CA (California State Parks)

Electricity first came to the gold rush mining towns in the California desert of the Eastern Sierras in the 1890s and it was, as you would expect, quite a spectacle. It brought with it a major change in the way gold was mined and processed and offered great productivity increases.  At the the time there was a common misconception that the electricity power lines had to be run straight because the electricity would shoot out if it there was a bend in the line. Basically it could not flow around a bent or wire that changes direction.  Here is a bit more background from Chat GPT: "Did people believe that electricity can't flow through a bent wire in the gold rush towns?"

"One popular misconception of the time was that electricity followed the path of least resistance. In this context, the notion that electricity could not flow through a bent wire might have arisen. People might have believed that the bent shape of the wire created a higher resistance, hindering the flow of electricity."

Considering that this was a new technology that was brought to remote towns where the living, to say the least, was hard and dangerous such a misconception seems reasonable. Yet we can draw some striking similarities with this scenario and the introduction of digital technologies to manufacturing plants. Manufacturing plants are operational islands where financial survival is always a top priority and digital technology is not fully understood, or maybe understanding it is not the most important priority. Like electricity in the gold rush town, its hard to relate to a new technology that has lofty and even ungrounded promises such as "a fundamental change to how we live and operate" and "an order of magnitude productivity increase".

Managing Electricity

During the 2nd industrial revolution, where we transformed from steam to electricity all plants had an "Electricity Manager". Again Chat-GPT for some wisdom: "what was the role of the electricity manager during the 2nd industrial revolution?"

"Overall, the role of an electricity manager during the Second Industrial Revolution involved overseeing the generation, distribution, and management of electricity. Their responsibilities encompassed technical, safety, operational, and financial aspects to ensure the reliable and efficient supply of electrical power to support industrial and societal advancements during this transformative period."

So clearly we do not have this role in our manufacturing plants today, we simply pay for electricity as a service. Does this sounds eerily similar to the current roles of CIO or CDO in managing digital technology? What is the destiny of IT organization and CIOs? Will XaaS (Anything as a Service) become common place and make IT redundant?

Oh the Skepticism

I tell these stories to most skeptics that I meet in an attempt to explain that a new paradigm requires new thinking. We will not be able to experience productivity increases until we realize that what we have at our hands is so different than anything we have seen before. In other words electricity does flow around bent wires, data is safe in the cloud, citizen developers can build complex systems, you can validate a solution in hours, control of democratized technology is easy, IIoT can be safe, it's also for all size companies, etc. Oh and one more that is quite controversial; MES, LIMS, WMS, etc. are not digital technologies - they are relics of the previous industrial age (industry 3.0). They are what steam was to electricity!

The challenge that I face on a daily basis is how to dispel the myths of digital technology and relieve the skepticism that is inherent in most manufacturing organizations? This is in the perspective of the bigger challenge that is how do we plan to transform industry so they can start capitalizing at the order of magnitude productivity gains. In the words of Søren Kierkegaard: "Life can only be understood backwards; but it must be lived forwards."

Agility - The Business Benefits of Smart Manufacturing

Yes we are all very excited about Smart Manufacturing, Industry 4.0, IIoT, AR, etc. but do we really understand the business benefit? I am sure I am not the first or the last to ask this question, yet it is still very interesting and important. Companies that are implementing these technologies clearly have a specific business driver in mind and there are plenty of good examples. So I don't believe that its a case of technology for technology's sake but I am not sure that the industry realizes the ultimate potential that these technology can provide.

However if we "get into the helicopter" (a term I borrow from working with Danes for a number of years), which means to take a look at it from afar to gain a broader perspective - a picture of agility appears. What I am saying is that the application of these modern technologies can transform our manufacturing systems from rigid hierarchical control structure to a more agile distributed control structure and hence inherently make the production system more agile. In addition it provides a unique opportunity to embed data integrity, and a full history of every minute transaction being made. This means the potential for GMP compliance with very little effort!

Now I am going to go on a bit of a philosophical-academic tangent here, but guess what its a blog and where else can I do that? The premise is that production systems are characterized by chaos and that the best way to deal with managing chaos is by using system that have emergent behavior. One of the basic concepts with IIoT is decentralization and automation of the decision making by moving it to the end nodes of a system (end computing). This brings about emergent behavior which is the fundamental trait needed for agility. OK, this concept requires a bit more explanation, I understand but take my word for it, for now.

So what does this all mean? In short, the Smart Manufacturing/Industry 4.0 set of technologies provide a potential to have true agility in a production system with inherent compliance! Now all that is missing are a practical architectures and implementations, which I believe are well on their way in some industries.