“Standards and rigor be damned—that’s the cost of doing business.”

For three decades, this mentality defined manufacturing operations. Plant floor heroes solved problems with whatever tools they could cobble together. Innovation happened one use case at a time. Each solution worked brilliantly. Together, they created a technical debt crisis where a small manufacturer would need to spend $100,000 for capabilities they could now access for $4,000.

Episode 18 of Practitioners Unplugged, recorded live at Schneider Electric’s Innovation Summit, explored how that dramatic cost reduction became possible. John Dyck, CEO of CESMII (Clean Energy Smart Manufacturing Innovation Institute), explained why interoperability represents an inflection point for American manufacturing—particularly for the 98.4% of manufacturers that are small and medium-sized operations.

Manufacturing productivity in the United States declined for the first time in recorded history over the last five years. Meanwhile, AI at scale remains out of reach for most organizations, blocked by technical debt accumulated through decades of problem-solving.

“Interoperability is one of those loaded terms, but it remains a super important idea and capability with very real outcomes,” John acknowledged.

At its core, interoperability is the capability of disparate software systems to communicate seamlessly using standard interfaces and APIs. However, the definition matters less than the problem it solves.

Thirty years of manufacturing data solutions created significant technical debt. Great companies built highly innovative solutions for productivity, quality, and maintenance. Nevertheless, each one became its own data silo with its own stovepipe architecture.

“When great new capabilities show up, as we’ve seen with the fourth industrial revolution and certainly now with AI, we’re prohibited from innovating at scale because of that lack of rigor and lack of focus on data standardization,” John explained.

John posed a striking challenge: “I can count on one hand how many Fortune 100 companies have effectively scaled large-scale manufacturing operations platforms across their entire fleet of sites.”

The most resourced organizations still struggle to scale digital solutions. Not because the solutions don’t work—they work brilliantly. Rather, the lack of interoperability makes replication prohibitively complex and expensive.

This scaling failure devastates the 98.4% of US manufacturers that are small and medium-sized operations. These companies need access to smart manufacturing capabilities but can’t afford the $100,000+ price tags that come with traditional approaches.

CESMII’s approach centers on three foundational imperatives.

First: Standardized Information Models. Today, you’re building data context from scratch every single time, and there’s no way to share these models because the structure varies from vendor to vendor.

CESMII’s solution? Crowdsource standardized information models and make them freely available. “We can collaborate, come together as a community and build these standardized information models, put them in a marketplace, and make them accessible for every platform, for every manufacturer, for free,” John explained.

These models cover every manufacturing asset—pumps, conveyors, compressors, CNCs, robots. Instead of reinventing the wheel, manufacturers access pre-built, standardized models that work across platforms.

Second: Type-Safe Information Models. CESMII treats these models as “type safe”—preventing anyone from modifying the standard. This ensures consistency across deployments.

Third: Standard APIs for Universal Access. The third imperative creates an open standard API that allows any platform to expose those information models and allows any application to discover those objects and their relationships.

A small manufacturing company in Medina, Ohio with fewer than 20 employees faced major quality and reliability challenges. They couldn’t afford sophisticated software solutions or systems integration consulting.

Through a small systems integrator familiar with CESMII’s approach, they deployed a standards-based solution for less than $4,000.

“They were able to take a standards-based information model for a waste scale, bring that information into the cloud with no on-premise infrastructure and using Excel, solve their quality and reliability challenges,” John explained.

In a traditional environment? That same capability would have cost approximately $100,000.

This small manufacturer gained capabilities they could actually sustain. They didn’t have IT staff to maintain infrastructure or in-house expertise to connect complex systems. Nevertheless, with crowdsourced, open approaches, they now have a data platform that works with Excel, Power BI, or any other solution against the same API.

Sree raised a critical technical point: the limitation of hierarchical data models.

“I’ve never seen a manufacturing system that isn’t hierarchical,” John acknowledged. The ISA-95 model taught the industry to think in hierarchies: enterprise, site, area, line, asset.

“But when you bring material or people or energy into that process, the hierarchical model falls down,” John explained.

This limitation becomes critical when deploying AI at scale. “AI at its core wants to infer relationships—how equipment and operators and material interact,” John explained. “But in a hierarchical system that’s just not coded unless it’s custom coded every single time.”

CESMII’s answer? Graph relational databases—the same technology social media companies use to determine relationships. “That’s really powerful in manufacturing because now you can expose data and figure out causation and correlation you wouldn’t have dreamed of before,” Dante noted.

Graph databases treat every object as nodes with relationships to other nodes. Moreover, these relationships change over time. “AI can now infer real value from these relationships in ways that can be scaled very rapidly from site to site,” John explained.

Sree offered a powerful analogy for communicating interoperability’s value to leadership: “If every country comes to the UN with different languages, you need translators. But that’s exactly what we have—layers and layers of translators at different levels of the stack. When we say technical debt, that’s exactly what it is.”

These translation layers compound complexity and cost. Worse, they erode data trust. “When you go through layers of translation and all these Frankenstein integrations, you lose trust in the data,” Sree noted. This insight particularly resonated from his life sciences experience, where regulatory compliance demands absolute data integrity.

For CEOs, this creates the “aha moment.” Technical debt isn’t an IT problem—it’s layers of expensive translation that slow innovation, increase costs, and undermine confidence in the very data needed to make better decisions.

Sree observed that “the mindset challenge is bigger than the actual technical viability.” John agreed emphatically.

A significant part of the challenge involves helping manufacturers understand the disconnect between their business strategy and their digital or smart manufacturing strategy. “It turns out most manufacturers don’t have a cohesive digital or smart manufacturing strategy,” John revealed. When that epiphany happens, the rest falls into place.

John highlighted a fundamental challenge: “As Americans, we’re as individualistic as corporations as we are as people—we don’t like collaborating.” CESMII exists precisely to overcome this tendency. As a vendor-agnostic consortia, they bring competitors to the table to solve problems collaboratively. “One of the most important things we can instill is a mindset biased towards collaboration and partnering,” John emphasized.

This isn’t just philosophical. Interoperability cannot be solved by any one vendor or any one manufacturer alone. Therefore, it demands the kind of collaborative effort that CESMII facilitates.

John described what he called the “bailing twine and duct tape mentality” that created current technical debt. “Fred or Sue on the shop floor—she’s gotta solve that problem right now so we can get product out the door. Standards and rigor be damned, that’s the cost of doing business.”

This problem-solving approach made heroes of individuals who kept operations running. However, scaled across 30 years and thousands of sites, it created the interoperability crisis manufacturers face today. The transformation requires moving from heroic individual problem-solving to systematic, scalable solutions.

“We’re at an inflection point,” John emphasized. “I’m very aggressive in telling every manufacturer I have the opportunity to speak with: You can continue propagating this technical debt, this cost and complexity, or you can choose a different way—a way that will lead you towards productivity, competitiveness, fundamentally enabled by interoperability.”

Manufacturing productivity in the United States has declined for the first time in recorded history. The next 30 years cannot be marked by the same stovepipe architecture approach that defined the last 30. Consequently, manufacturers face a choice: lead the transformation or scramble to catch up later.

CESMII’s strategy centers on three pillars: knowledge (training and certification programs), technology (the three Smart Manufacturing Imperatives), and ecosystem collaboration. “All three have to be a vital part of any transformation,” John emphasized.

For American manufacturing to reverse its productivity decline, capture AI’s potential at scale, and remain globally competitive, more organizations need to choose a different way. The inflection point is here. The question is whether manufacturers will recognize it and act while they still lead rather than follow.

Ready to explore interoperability? Visit CESMII.org to learn about knowledge programs, technology initiatives, and how to join the collaborative ecosystem transforming smart manufacturing.

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For years, the automation industry talked about decoupling hardware and software. IT/OT convergence became a buzzword that everyone nodded at but few truly believed would arrive.

Episode 17 of Practitioners Unplugged, recorded live at Schneider Electric’s Innovation Summit in Las Vegas, proves the skeptics wrong. Greg Boucaud, Chief Marketing Officer of UniversalAutomation.org, and Renato Silva, CEO of Aimirim, shared real implementations, actual deployments, and measurable results.

This wasn’t a vision presentation. Instead, it was a field report from practitioners who’ve already made the future work.

UniversalAutomation.org launched in November 2021 with nine founding members. Four years later, the nonprofit association has grown to 115 members spanning end users, system integrators, technology vendors, and academia.

“Our motto in life is to unlock the automation software—decouple hardware and software in the automation space,” Greg explained. While the goal sounds simple, it represents a fundamental shift in how industrial automation functions.

Think about your personal computer. You download software, install it, and never wonder about your laptop brand. Industrial automation historically hasn’t worked this way. Instead, you needed the right brand, the correct hardware, and the proper topology.

Through open standards—specifically IEC 61499—UniversalAutomation.org brings the IT world’s flexibility to OT environments, enabling true hardware/software decoupling.

Greg highlighted a critical challenge: “If you invest in your OT systems, your investment is tightly tied to the lifecycle of your hardware. If your hardware tends to be end of life, then you need to reinvest for the next generation.”

That’s an expensive, disruptive cycle. When hardware reaches obsolescence, companies face massive capital expenditures to replace entire systems.

However, decoupling changes this equation dramatically. Software becomes independent from hardware lifecycles. Manufacturers can upgrade hardware without throwing away automation logic. Furthermore, they can add new capabilities without rip-and-replace projects.

For facilities running 20, 30, even 50 years, this matters enormously. Consequently, the ability to evolve automation without complete overhauls transforms how organizations approach digital transformation.

What makes UniversalAutomation.org different? A deliberate community structure bringing together three critical groups.

Users of automation represent the entire value chain—end users, system integrators, technology integrators like Aimirim, and distributors. Technology vendors include both traditional automation companies and increasingly IT companies attracted by easier data access. Academia completes the picture by training the next generation with full curriculum provided by UniversalAutomation.org.

Greg cited a sobering statistic: “By 2030, 2 million jobs will go unfilled in the automation space because of lack of engineering skills.” Therefore, academia’s role in closing this gap becomes strategic, not optional.

Renato Silva’s journey provides the perfect case study for why open standards matter. Aimirim specializes in advanced process control based on artificial intelligence and real-time optimization.

Here’s what makes the story remarkable: Aimirim accomplished in nine years what typically takes established companies 30-40 years. Moreover, they achieved this by building on the IEC 61499 framework.

“Would it be possible to do in 90 years the same thing we do without a framework for scaling? No,” Renato emphasized. “The scalability of complex technology is only feasible right now because of the standard.”

Aimirim’s flagship implementation with British American Tobacco (BAT) demonstrates open automation’s practical viability. Starting in Uberlândia, Brazil, Aimirim digitalized BAT’s entire industrial operation using IEC 61499 and Universal Automation Organization (UAO) runtime.

What started locally scaled globally. Consequently, the same technologies now operate across at least eight countries for BAT, proving the approach works across different regulatory environments.

“We could deploy an advanced process control in one week,” Renato explained. “And then they say, can you scale to 15 more facilities?” For startups competing against 30-40 year old companies, the open standard framework became the competitive differentiator.

Dante asked the technical question practitioners need answered: what compelled choosing IEC 61499 over traditional PLCs and SCADA infrastructure?

Renato’s explanation reveals the architectural shift: “The point about the PLC and DCS systems for us is that it became purely infrastructure. We take some edge device—it doesn’t matter the brand—and we fully integrate this edge device to any PLC.”

Traditional PLCs lack edge compute power for complex AI models. In contrast, Aimirim’s approach integrates edge compute at the same layer, treating existing PLCs as infrastructure.

“If we can read, I can take this information and stream it to a cloud,” Renato explained. “If I can write, I can take some decisions on the edge and instantly write on the PLC.”

Critically, this approach maintains safety: “When the PLC becomes infrastructure, we keep the automation as they are. If you turn off my technology, you instantly go back to your old technology.”

As a result, this architecture enables brownfield deployments without ripping out existing systems. Organizations add capabilities on top of current infrastructure, upgrading incrementally.

Renato shared an insight from driving 120,000 kilometers per year for three years, visiting facilities and listening to operators, supervisors, managers, and executives:

“The client doesn’t want one standard or another. They just don’t care. They want to know if you as a company are compliant. They want efficiency increase.”

This perspective matters. Technology providers often sell features and standards. However, end users buy outcomes and compliance.

Sree highlighted the strategic thinking behind standards: “You were able to think two, three steps ahead. The client wants scalability. The value of standards in enabling that.”

End users struggle to envision the end state of their digital transformation journey. Therefore, practitioners and vendors thinking strategically understand that standards enable the scalability clients need even when they can’t articulate it.

“It’s a game of proving everything you say,” Renato noted. “You go to a big client and you say, I can deploy an advanced process control in one week. And they say, okay, do it.”

The proof comes through demonstration, not presentation. Startups especially must prove capability before negotiating terms. Moreover, standards enable that rapid proof-of-concept by providing frameworks that work reliably.

Greg emphasized that open automation applies across implementation scenarios, not just new facilities.

ExxonMobil’s Baton Rouge project represented greenfield application—full rip and replace using Open Process Automation (OPA) with Universal Automation Organization as a technology enabler.

Kongsberg Maritime’s oil and gas platforms demonstrated brownfield application. They couldn’t replace everything on existing platforms. Instead, they added an orchestration layer using UAO technology on top of existing systems.

Aimirim’s BAT deployment represented another brownfield approach—facilities with existing infrastructure where new capabilities deployed incrementally.

“Some people may tend to think that this technology is only for greenfield application,” Greg noted. “But actually, no. One of the easiest ways is going through the orchestration layer, adding a layer on top of what is existing.”

Kongsberg Maritime standardized well barrier testing—previously done manually by different operators in different ways. They automated it using universal automation technology, orchestrating the process without replacing underlying systems.

This flexibility matters because few organizations can afford complete system replacements. Furthermore, the ability to add open automation capabilities incrementally, proving value before expanding, enables adoption that wouldn’t happen otherwise.

Dante highlighted a powerful advantage: “Creating that abstraction or control layer and being able to manage obsolescence simultaneously, developing your software simultaneously without having to worry about tremendous downtimes.”

Traditional automation faces a harsh reality. When hardware reaches end-of-life, organizations face downtime, capital expenditure, and risk.

With hardware/software decoupling, organizations abstract the control layer first, then gradually replace underlying hardware. “Building it into that layer over time as opposed to ripping everything out and starting over,” Dante summarized.

Greg emphasized the investment perspective: “You need to ensure that this money which is invested, you will have your return on investment quite soon. But as well, that your investment lasts a certain amount of time.”

Sree posed the critical question: “Could we say that the big takeaway here is we’ve been talking about this for a while, but this is happening now?”

“This is happening now,” Greg confirmed emphatically. “Adoption is happening and adoption is accelerating.”

The timeline provides context. Germany launched Industry 4.0 in 2010—15 years ago. France called it Industry of the Future. Similarly, the U.S. talked about digitalized industry.

“It takes time for sure, because we speak about OT processes, physical processes,” Greg acknowledged. “But luckily now we have the tools. We have the technology. We have the right companies to do it.”

Aimirim’s nine-year journey mirrors the Industry 4.0 timeline. The companies, technologies, and standards needed for open automation matured together. What seemed theoretical five years ago now operates in production environments across multiple countries.

Renato’s approach to clients captures the moment: “I say to them, you’re gonna make it now or tomorrow. And I am here right now, so why waiting?”

Dante observed an interesting dynamic: democratization of hardware creates competitive pressure on vendors to deliver best-in-class products.

“Most systems today are vendor locked. It puts pressure on you as a manufacturer to come out with best in class hardware to compete in this now open space.”

Greg acknowledged Schneider Electric’s commitment: “I’m quite proud that Schneider is saying openly we go towards open automation. That’s the way we see it for industry, for energy. This world needs to be open.”

This represents a fundamental shift from “lock in the customer and lock out the competition” to competing on hardware quality, service, and ecosystem value.

Renato emphasized mindset as critical to this transformation: “This moment requires a lot of open-minded people. To be with Universal Automation means that you have the right mindset for an open world.”

His call to other startups: “It’s not really about the competition. It’s about changing the market. A new market is being created and to be part of it, you have to be open-minded enough to join.”

Dante agreed: “Open-minded is what’s definitely missing right now. It’s just rigid thoughts of 50 years of how we’ve done automation systems.”

Sree referenced the old approach: “Lock in the customer and lock out the competition.”

The future approach recognizes that ecosystems require frictionless exchange of information. Furthermore, open standards provide the foundation that makes ecosystems viable.

Several critical insights emerged for practitioners evaluating open automation:

Standards enable startup speed at enterprise scale. Aimirim’s nine-year achievement demonstrates how frameworks accelerate complex technology deployment. Therefore, standards aren’t bureaucratic overhead—they’re competitive advantages.

Brownfield deployment removes the biggest adoption barrier. The ability to add open automation layers on top of existing infrastructure means organizations don’t need greenfield opportunities to start. Moreover, incremental adoption with rapid proof points drives expansion.

End users buy outcomes, not standards. While standards enable scalability strategically, sales conversations focus on efficiency and proven results. Consequently, standards matter most in delivery and scaling.

Investment protection matters more as facilities age. For 20, 30, 50-year operations, the coupling of software to hardware lifecycles creates expensive replacement cycles. However, decoupling transforms capital planning and operational risk.

The ecosystem finally exists. Technology, companies, standards, and talent converged to make open automation viable. What seemed theoretical five years ago now runs in production across industries.

Competitive dynamics shift from lock-in to excellence. Vendors must compete on product quality and ecosystem value rather than proprietary control. Consequently, this accelerates innovation and benefits end users.

Episode 17 marked a transition point in the open automation conversation. For years, industry events featured vision presentations about possibilities. This conversation presented field reports about what’s already working.

Greg Boucaud and Renato Silva didn’t pitch concepts. Instead, they shared implementations—British American Tobacco across eight countries, ExxonMobil’s Baton Rouge facility, Kongsberg Maritime’s oil platforms.

UniversalAutomation.org’s growth from 9 to 115 members in four years, with 15 commercial offers available, indicates momentum. Moreover, Aimirim’s rapid scaling demonstrates that open standards enable capabilities previously reserved for established players.

The convergence of IT and OT that Industry 4.0 promised in 2010 finally materialized in practical, deployable form. Standards matured. Companies built products. Practitioners proved concepts. Furthermore, academia started training the next generation.

As Renato told potential clients: “You’re gonna make it now or tomorrow. And I am here right now, so why waiting?”

The question for manufacturers isn’t whether open automation will happen—it’s already happening. Rather, the question is whether they’ll lead the transition or scramble to catch up later.

For practitioners tired of vendor lock-in and frustrated by hardware obsolescence cycles, the tools exist today. The standards work. The implementations prove viability. Additionally, the ecosystem supports adoption.

Open automation isn’t the future anymore. It’s the present for organizations open-minded enough to embrace it.

Ready to explore open automation? Visit UniversalAutomation.org to learn more about the technology, join the community, and access training resources.

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As 2025 drew to a close, Episode 16 of Practitioners Unplugged took a reflective look back at the conversations that shaped our understanding of digital transformation throughout the year. Cohosts Dante Vaccaro and Sree Hameed unpacked the common threads, surprising insights, and memorable moments from a year of conversations with practitioners driving real change.

What started as a podcast focused broadly on digital transformation evolved into something more nuanced—a collection of stories revealing the human challenges, infrastructure realities, and cultural shifts that determine whether technology initiatives succeed or fail. From live recordings at Automate in Detroit (complete with an infamous train that became a running joke) to conversations about change management and AI’s reality, 2025 brought clarity to what actually matters in Industry 4.0 and beyond.

“It really seemed like it kind of morphed into little breadcrumbs from everyone we talked to kind of evolving into different drawdown topics that didn’t even think to consider,” Dante reflected on the year’s journey.

The practitioners who made 2025’s conversations possible:

Each brought unique perspectives from different corners of the industrial ecosystem, painting a comprehensive picture of where digital transformation stood in 2025.

Jim Mayer delivered one of the year’s most memorable insights when he challenged a fundamental assumption about transformation initiatives.

“Humans hate change, and that’s just not true,” Jim explained, “What did you have for lunch today, Sree?” Sree replied, “I had a slice of pizza.” Jim then turned to Dante, “What did you have for lunch today?” Dante replied, “Kebabs.” Jim concluded his point, “I had a wrap. We love change.”

The real issue isn’t change itself—it’s how change is managed and communicated. When people feel blindsided, resistance follows naturally. However, when teams are brought along and feel like participants rather than victims, adoption becomes significantly easier.

“Change has to be done simultaneously with the strategy, bringing people around from day one,” Dante observed. “It can’t just be the afterthought at the end where we say, hey, nobody’s doing what we set out to do.”

Sree connected this to multi-site MES rollouts, noting the critical importance of centers of excellence that genuinely included stakeholders rather than dictated to them. When people participated in the design process, projects tended to progress much more smoothly.

The lesson applied broadly: organizations needed to question whether they were truly bringing people along or simply informing them of decisions already made.

Nick Valdez articulated a challenge many guests touched on when discussing water infrastructure modernization:

“We struggle with people really understanding what information they need and what’s the best way to achieve that information because not everyone in the water wastewater industry is on the modernization train. They don’t necessarily want to spend the capital or invest the time or energy or effort.”

Across industries, culture determined whether organizations could even recognize opportunities for improvement. When “that’s the way we’ve always done it” became the default response, technology investments delivered minimal returns.

Jim Mayer’s work on manufacturing culture reinforced this point. His focus on creating environments where people wanted to work rather than simply needed to work addressed the human foundation that made everything else possible.

Technology amplified existing culture. If the culture valued learning and improvement, technology accelerated both. If the culture resisted change, technology became another source of friction.

The conversation kept returning to workforce challenges throughout 2025. Experienced workers were retiring with institutional knowledge, younger workers had different expectations about development, and technology evolution meant even veterans needed ongoing skill development.

Organizations addressing this successfully shared common approaches: structured knowledge transfer programs, apprenticeship and mentorship models, continuous learning culture, and technology as a capability multiplier rather than replacement.

“It does pay off even if it means projects get delayed in terms of being able to find the right resources,” Sree noted. “You do end up making change that is sustainable.”

Rushing implementation with inadequate capability development created technical debt. Taking time to build capabilities properly produced more sustainable results.

While AI grabbed headlines throughout 2025, practitioners consistently emphasized that more fundamental data challenges continued to limit what organizations could actually accomplish with advanced technologies.

Jonathan Wise’s discussions around Unified Namespace and Craig Henry’s “industrial nervous system” metaphor reinforced the same point: organizations investing heavily in AI (the “brain”) while neglecting connectivity and data infrastructure (the “nervous system”) created powerful capabilities that couldn’t sense what was happening on the factory floor.

Organizations making progress addressed these systematically rather than expecting AI to magically overcome poor data foundations. They invested in connectivity, established data governance, and created infrastructure that enabled data to flow where it was needed when it was needed.

If there was one topic that generated both excitement and skepticism in 2025, it was artificial intelligence. The year brought clarity about the gap between vision and reality.

Sree captured the challenge: “I look at AI and my reaction is that is not in my comfort zone. I want explainability and transparency and see the promise of AI and I’m still okay, there’s a lot of learning to do here.”

When AI systems functioned as “black boxes” without explaining reasoning, practitioners struggled to trust them for critical decisions. In manufacturing operations where safety, quality, and efficiency had direct consequences, this transparency gap mattered enormously.

Dante made an important distinction: “When we talk about practical AI, this goes back into what’s proven. This is more machine learning. This is neural network building. All of this stuff existed before without the generative component to it on top.”

Established forms of AI—machine learning for predictive maintenance, computer vision for quality control, optimization algorithms—had proven track records. These applications delivered measurable value in specific use cases.

In contrast, generative AI represented something newer, less proven in industrial contexts. While the promise was significant, so were the unknowns around reliability and appropriate applications in high-stakes environments.

The conversation pointed toward where 2026 needed to evolve: from hype toward practical implementation stories. What was working? What wasn’t? How could organizations maintain human expertise even as they deployed AI tools?

One of the year’s clearest messages: organizations couldn’t skip foundational work. As a customer Sree referenced noted: “There is no Industry 4.0 without Industry 3.0. It builds on that, right?”

Organizations rushing to embrace Industry 5.0 concepts without properly implementing Industry 4.0 foundations faced significant challenges. “If you didn’t start your Industry 4.0, you’re going to be left in the dust,” Dante emphasized. Successful modernization required honest assessment of current capabilities and systematic progression rather than attempting to leapfrog without building necessary foundations.

The live recording experiences from Automate in Detroit added memorable dimension to 2025’s podcast journey. Recording on the show floor captured real energy and context—along with an unexpected guest star: a train that repeatedly rolled through recordings, eventually becoming a running joke turned into a montage of train puns.

“I didn’t realize actually how many times that train really just disrupted the flow of the conversation,” Dante laughed while reflecting on the experience.

The incident illustrated something important: authenticity and real-world context mattered more than perfectly controlled conditions. Real implementation happened in messy, imperfect environments. Success came from adapting and learning rather than waiting for ideal circumstances that never arrived.

As 2025 concluded, Dante and Sree outlined what they hoped to explore in 2026: more stories, more specifics, more honest accounts of what was working and what wasn’t.

“Stories, successes, trials, tribulations, what’s working, what’s not,” Dante outlined. “Learning the biggest impacts, the biggest failures.”

The emphasis on practical AI over hype would continue. The focus on people and culture alongside technology would remain central. And the commitment to learning from practitioners actually implementing initiatives rather than consultants selling visions would define the podcast’s approach.

If 2025’s conversations proved anything, it was that successful digital transformation required integration across multiple dimensions simultaneously. The best technology failed without capable, engaged people. Brilliant strategies couldn’t overcome cultures that resisted change. Big visions meant nothing without foundational infrastructure to support them.

Organizations that fragmented these elements—treating culture as HR’s job, technology as IT’s responsibility, strategy as leadership’s domain—struggled to achieve transformation that stuck. Those that recognized these elements must evolve together created conditions where change could take root and grow.

Dante and Sree expressed appreciation for their sponsors—Schneider Electric and AVEVA—whose support made the podcast possible while allowing editorial freedom.

Most importantly, they thanked listeners who showed up, shared episodes, and provided feedback throughout the year. The invitation remained open: if there were guests who should be featured, topics that needed exploration, or perspectives missing, the Practitioners Unplugged team wanted to hear about it. The community shaped the content, and that collaborative approach aligned with the podcast’s philosophy that the best insights came from practitioners sharing real experiences.

Looking back at 2025, Practitioners Unplugged provided something increasingly rare in discussions about digital transformation: grounding in reality.

The practitioners featured throughout the year offered nuanced perspectives. Yes, technology enabled new possibilities. But also: people and culture mattered more than most discussions acknowledged. Infrastructure determined whether advanced capabilities could actually function. Sustainable change required bringing teams along. And honest assessment of what was working versus hype helped organizations invest wisely.

As manufacturing and industrial operations moved deeper into Industry 4.0 and began exploring Industry 5.0 concepts, these grounded perspectives became increasingly valuable. Organizations that would thrive wouldn’t necessarily be those with the biggest technology budgets. Rather, they would be the ones that systematically built capabilities, developed their people, strengthened their culture, and deployed technology thoughtfully.

Practitioners featured in 2025 understood this. Their stories, struggles, and successes provided the practical wisdom that helped other organizations navigate similar journeys. That was the value Practitioners Unplugged delivered—not abstract theories or consultant frameworks, but lived experience from people doing the work.

Here’s to another year of conversations that cut through hype, celebrate honest problem-solving, and build community among practitioners driving real change.

Keep practicing. Keep learning. Keep transforming.

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Most people take for granted that clean water flows when they turn on the faucet. Wastewater disappears when they flush. Episode 15 of Practitioners Unplugged pulls back the curtain on this critical infrastructure with Nick Valdez, Automation & Controls Business Development Manager at Vessco Water. He reveals how digital transformation is revolutionizing an industry that quietly serves every community.

Operating across 41 states through a portfolio of acquired companies, Vessco Water represents the evolution from component supplier to comprehensive solutions provider. Nick’s journey—from culinary school dropout to manufacturing to leading automation initiatives for water/wastewater systems—provides unique perspective on bringing change to an industry historically resistant to modernization.

This conversation explores a different dimension of Industry 4.0: not sexy manufacturing facilities, but the essential infrastructure that determines whether communities can grow, whether rivers stay clean, and whether taxpayers get value from their utility investments.

As Nick puts it: “A lot of our customers don’t know what they don’t know. They don’t know that there are great products that can help them be smarter and better. They really lean on us as a platform to help them solve their problems.”

Here are the five key insights from our conversation with Nick:

Water and wastewater utilities face a classic chicken-and-egg problem. They won’t invest in sensors and data infrastructure until they see value. But they can’t see value without data. Nick’s team has mastered breaking this impasse by creating what he calls the “virtuous cycle.” (Originally “drinking the Kool-Aid” before our marketing expert Sree upgraded the terminology.)

“Once they start, it continues, it perpetuates. It’s almost incredible to watch how customers really do drink the Kool-Aid and say, oh wow, this is incredible. We’re able to have this information, now we can use it, and then it makes their lives a lot easier.”

Across municipalities, the pattern repeats consistently. Initial reluctance gives way to pilot projects. Pilot results then drive broader deployment. Suddenly, utilities that ran equipment from the 1970s until failure are calling to retrofit entire station networks.

What’s the key? Demonstrating tangible benefits in the first installation. This includes energy reduction, better operational visibility, or predictive maintenance that prevents costly failures.

Moreover, this insight challenges the “big bang” digital transformation approach. In water infrastructure, patience and proof points matter more than comprehensive strategies. One successful pump station with VFD controls and smart analytics generates more momentum than any presentation about Industry 4.0 possibilities.

The challenge Nick identifies is clear: “We struggle with people really understanding what information they need and what’s the best way to achieve that information because not everyone in the water wastewater industry is on the modernization train.”

Vessco Water’s geographic spread—41 states and growing—provides insight into how dramatically water infrastructure approaches vary across America. New York City faces completely different challenges and regulations than Iowa communities. This requires solutions that adapt while maintaining quality standards.

“In New York City, our platform partners there have a different challenge and different mindset of their customer than say what one in Iowa does. The real trick to it all is for us as a platform to have the resources and the availability of equipment and engineering and manufacturing to make a holistic approach to solving each of the different mindsets of our customers.”

Consequently, this diversity drives Vessco Water’s pillar strategy: service, aftermarket, automation and controls, pumps, and distributed products. Rather than forcing one-size-fits-all solutions, they combine specialized capabilities from different regional partners to address local requirements. For example, an Iowa partner specializing in pump distribution might team with a regional automation expert to serve an engineering firm’s specific needs.

The lesson extends beyond water infrastructure to any organization serving diverse markets. Standardization matters for capabilities and quality. But application requires flexibility. Build pillars of expertise that can be combined differently depending on customer context rather than rigid solutions that ignore regional differences.

Nick’s point about becoming “subject matter experts” resonates here. Customers lean on Vessco Water precisely because they understand the nuances of different regulatory environments, population densities, and community expectations across geographies.

Unlike consumer goods or even most industrial equipment, water infrastructure investments span decades. This time horizon fundamentally changes how buying decisions happen and what matters to customers evaluating partners.

“People buy from people in our industry. It’s very rare someone looks on the internet and just says, oh, I wanna buy a flight pump. They know the local community uses them. They call their local rep or distributor. They call one of our platform partners, come out and talk to me. Let’s build a relationship because the equipment that you’re gonna put in and the solution you’re gonna provide has to last for decades. That’s the expectation.”

This relationship focus shapes Vessco Water’s entire approach. They offer free training classes on drives and pump optimization. They provide arc flash studies and safety training. They bring mobile equipment demonstrations to municipalities and colleges. These investments build trust and educate customers who “don’t know what they don’t know” about available solutions.

The strategy addresses a critical industry challenge: water infrastructure expertise is retiring without adequate knowledge transfer. Vessco Water’s “new leaders group” creates mentorship paths where experienced professionals transfer “ancestral knowledge” about handling specific situations and selecting appropriate equipment.

In industries with long equipment lifecycles and high switching costs, customer education and relationship building deliver better returns than transactional selling. Your ability to help customers make informed decisions matters more than any individual product sale.

Nick’s Minnesota success story illustrates how modernization transforms economics and community impact simultaneously. A municipality running pumps with “archaic” starting methods moved to variable frequency drives with smart analytics. This generated benefits across multiple dimensions.

“They were able to reduce the amount of energy they were gonna have to use for their treatment of wastewater. They were able to support more people moving to that city. We could interpolate and predict high flow scenarios. So when it was gonna rain or when people were getting off work, we were gonna have more of an intake to the facility.”

Energy optimization reduced costs for taxpayers. Predictive capabilities prevented overflow events that would contaminate rivers and trigger regulatory fines. Better operational control enabled the municipality to support population growth. The technology investment paid dividends far beyond the initial business case.

Nick emphasizes downstream effects people don’t consider: “If they don’t have equipment in place that can understand that and predict that, or recognize that scenario in real time and then augment and change to mitigate it, then they do have these events where maybe they flood a whole bunch of houses or there’s regulatory fines and penalties for them having to divert effluent into a creek or a river.”

The parallel to manufacturing is clear: digital transformation delivers value across multiple dimensions simultaneously. Don’t evaluate investments purely on energy savings or maintenance reduction. Consider community impact, regulatory compliance, growth enablement, and risk mitigation together.

While many manufacturers outsource to reduce costs, Vessco Water makes the opposite choice. Nick explains their decision to keep manufacturing, engineering, and quality control in-house despite seeing competitors find success with outsourcing.

“If we outsource some of that work, the quality control measure gets limited. We want to control the quality that our customers get and the experience that they have so we can put our stamp and brand on it and we know for a fact what the outcome’s gonna be.”

This reflects a broader philosophy about customer experience versus short-term cost optimization. Vessco Water offers service agreements guaranteeing pump replacement every five years and 100% operational uptime. Delivering on these promises requires controlling the entire value chain.

Furthermore, Nick’s passion for company culture is evident: “Everybody is important. Everybody has their role and responsibilities that contribute to it, and we all breathe and believe in the same thing as we have one common goal. The most beautiful thing about our company is our culture.”

In addition, the insourcing decision also addresses talent challenges. Rather than struggling to find experienced engineers in a tight market, Vessco Water invests in developing people through mentorship, cross-platform learning boot camps, and technology that amplifies individual capabilities.

The strategic lesson: outsourcing and cost reduction aren’t always optimal strategies. When brand reputation and customer experience drive competitive advantage, controlling quality through vertical integration may deliver better long-term value despite higher upfront costs.

Nick Valdez’s perspective reveals that digital transformation reaches far beyond traditional manufacturing into infrastructure that touches every home and business daily. Water and wastewater systems represent critical infrastructure where modernization delivers community benefits that extend well beyond operational efficiency.

First, start small to create momentum through demonstrated value. Build flexible solutions that adapt to regional requirements while maintaining quality standards. Invest in relationships and education because equipment lifecycles demand trust. Evaluate modernization through multiple value dimensions including community impact. Consider whether vertical integration better serves brand promises than cost optimization.

As Nick advises those hesitant about modernization: “Pick up the phone and call somebody. Reach out, go to your local distributor, go to your local channel partner. Learn what’s out there. Really give yourself the opportunity to understand that there are incredible products and technology that can reduce the amount of headache and heartburn. Be proactive, not reactive. The more proactive you are, the less reactive you have to be.”

The water infrastructure story provides valuable perspective for all digital transformation initiatives. Technology alone doesn’t create value. Technology combined with trusted partnerships, customer education, and commitment to quality experiences drives lasting change.

While manufacturing often dominates Industry 4.0 conversations, infrastructure modernization demonstrates how these same principles apply to essential services that most people never think about until something goes wrong. The municipalities transforming from run-to-failure to predictive operations create value that reaches every constituent, even if that value remains invisible when everything works properly.

The ultimate measure of success isn’t impressive technology demonstrations but communities where clean water flows reliably, wastewater disappears safely, and infrastructure adapts to support growth while protecting the environment. That requires exactly the combination Nick describes: smart technology, trusted relationships, and commitment to customer experience over transactions.

While people may take clean water for granted, the professionals modernizing infrastructure understand that reliability requires constant innovation and partnership. The transformation from archaic pump stations to predictive operations happens one relationship, one pilot project, and one virtuous cycle at a time.

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If you’ve invested in AI, machine learning, and digital twins only to see disappointing returns, Episode 14 of Practitioners Unplugged diagnoses the problem. Craig Henry, Global Account Director for Amazon at Murrelektronik and author of Super Connected: The Future of Industrial Nervous System, cuts through the AI hype with a sobering truth: your factory’s expanding “brain” is useless without a properly designed nervous system to connect it to reality.

With over 30 years in advanced manufacturing and inter-logistic systems—including major roles at Amazon, Siemens, and Danaher—Craig brings a practical, infrastructure-focused perspective to Industry 4.0 and 5.0. His book and this conversation challenge the current obsession with AI capabilities by asking a more fundamental question: can your AI actually feel what’s happening on your factory floor?

As Craig puts it: “We’re operating a brain that is suspended in space and isn’t connected to a body. Without the nervous system, we are crippled.”

Here are the five key insights from our conversation with Craig:

While manufacturers pour resources into AI analytics platforms (the “brain”) and field devices (the “extremities”), Craig identifies a critical gap: the connectivity layer that bridges them. This “forgotten middle” represents the difference between AI that transforms operations and AI that generates impressive demos with no business impact.

“As I’ve talked with Amazon and others in the field, I’m seeing that when you get the connectivity right, it opens up all of the promises of Industry 4.0 and Industry 5.0 and the digital twin. But without it we are crippled.”

The problem is pervasive: manufacturers operate with 30-year-old control systems that create “black box effects.” A CEO cannot drill down through cybersecurity, edge infrastructure, and proprietary PLCs to see a single sensor reading that might explain why a critical customer order is delayed. The data exists but remains invisible.

Craig’s prescription: invest in the forgotten middle before investing in AI. Move from analog signals to digital communication networks. Implement open architectures like OPC UA that expose data across the organization. This infrastructure investment delivers the fastest ROI because it enables you to see and feel what’s happening across your operations for the first time.

The parallel to Amazon’s approach is instructive: Jeff Bezos mandated 20 years ago that every system would have an open API with exposed data. This infrastructure-first philosophy enabled Amazon’s rapid innovation and operational excellence—not because they had better AI, but because their AI could actually see the operation.

Craig addresses the objection every plant engineer faces: why spend $20,000 installing a $200 sensor when budget pressures demand “value engineering”? His answer reframes the investment from cost to insurance against catastrophic failure.

“I would venture to say having that data could mean you’ve saved a million dollar problem down the line from happening. And the $20,000 would be small compared to what could be just operating blindly and hoping for the best.”

The critical distinction: don’t install analog signals—install communication networks. Run digital, self-checking, deterministic, error-checking protocols through that expensive conduit run. This enables condition monitoring that moves unplanned downtime to planned maintenance, a transformation that pays for sensor investments many times over.

Craig cites the harsh reality of “value engineering” in capital projects: data infrastructure gets cut first, then costs 30-50% more to retrofit later when problems emerge. Leading organizations take the opposite approach—they get data first, even before knowing exactly how it will be used, because they understand the infrastructure enables future capabilities.

The lesson extends to OEMs and system integrators: companies that deliver equipment with comprehensive digital twins and exposed data differentiate themselves from competitors still selling hardware. The conversation shifts from price to ROI when you can demonstrate how your system will improve throughput by specific percentages.

Craig challenges both the “lights out factory” dream and the passive “human in the loop” compromise with a more sophisticated paradigm: human in command. This distinction becomes critical as automation advances and skills fade threatens operational resilience.

“It’s not human in the loop, but human in command. The system should be able to push up to him: I’m seeing this. Here are three options. Please choose one and approve it.”

The analogy to chess masters illustrates the point: the highest performance comes from humans with computer assistance, not computers alone. The master can recognize patterns, apply judgment, and execute strategies that algorithms miss. Similarly, factory operators should manage 20 stations with AI flagging exceptions and recommending actions rather than pushing buttons on individual machines.

This approach addresses multiple converging crises: the global labor shortage (500,000 positions unfilled in U.S. fulfillment alone), the competency crisis (87% of companies cite skill gaps as their biggest constraint), and the skills fade problem (automation that works well until catastrophic failure when humans have stopped understanding the underlying process).

Craig’s warning about autonomous driving accidents applies equally to manufacturing: when humans assume automation has everything under control, disasters happen. The solution isn’t eliminating automation but designing systems where humans maintain operational understanding and decision authority even as AI handles routine optimization.

Craig takes a strong stance on the standards debate, arguing that proprietary protocols and closed systems amount to holding customers hostage. For manufacturers evaluating equipment purchases or system upgrades, this represents a critical strategic decision.

“If I decide, hey, I’m going to force my customers who buy my equipment to use my protocols and my networks to the exclusion of everybody else, what they’re really doing is holding their customer for ransom. And that’s not cool. It’s not okay.”

His recommendation is clear: use OPC UA, which bridges devices to cloud, includes built-in encryption, and democratizes access across vendors. Avoid fieldbus systems controlled by single vendors. Demand that OEMs expose data through standard interfaces even if internal IP remains protected.

The business case for open standards extends beyond technical elegance. Standards enable faster deployment, broader talent pools for implementation and support, and protection against vendor dependency. When Amazon and other large end-users mandate specific protocols, they’re protecting their ability to innovate independent of equipment vendor roadmaps.

For OEMs, Craig’s advice flips the conventional wisdom: don’t view open standards as giving away competitive advantage. Instead, differentiate through consulting on process improvement and ROI rather than locking in through proprietary interfaces. Companies that help customers achieve measurable performance gains win business regardless of protocol choices.

Craig delivers sobering statistics: cybersecurity attacks now generate more revenue than illegal drug trafficking, with loss of use (ransomware) as the primary attack vector. For manufacturers, this isn’t just an IT problem—it’s an operational resilience issue that requires scenario planning and regular drills.

“We have to be able to have plan A, plan B, plan C, and to your point that a human is there skilled and experienced enough to be able to drive that and to make those decisions.”

The challenge intensifies with automation dependence. Craig’s example of his wife’s county clerk office illustrates the problem: when systems went down, resistance to manual processes was so strong that basic operations nearly stopped despite manual procedures being available. In manufacturing, returning to manual operation may be impossible when robots perform tasks no human workforce could replicate.

The solution requires acknowledging that attacks will happen and preparing accordingly: secure data in three places, maintain operational knowledge that doesn’t depend on systems, and ensure humans understand underlying processes enough to make decisions during disruptions. This represents the flip side of the human-in-command principle—humans must maintain enough competency to take control when automation fails or becomes compromised.

Craig’s perspective on large language models adds another dimension: 80% of people using LLMs can’t recall the content they produced. If we build similar dependency in manufacturing operations, how do humans respond when AI systems become corrupted? The answer requires intentional design to maintain human competency even as automation advances.

Craig Henry’s infrastructure-focused approach provides the reality check that digital transformation initiatives need. While the industry obsesses over AI capabilities—with projects like Stargate committing $400 billion to expand the “brain”—the nervous system that connects that brain to physical operations lags dangerously behind.

The key lessons for practitioners: invest in the forgotten middle connectivity layer before chasing AI solutions, move from analog signals to digital communication networks throughout your operations, design for humans in command rather than lights-out automation, demand open standards that prevent vendor lock-in, and prepare for disruption through scenario planning and competency maintenance.

As Craig warns, “We have an uninformed brain floating in space, thinking it understands things.” The physical world of manufacturing requires more than thinking—it requires feeling, sensing, and responding through a properly designed nervous system.

The path forward isn’t abandoning AI but building the infrastructure foundation that makes AI useful. Organizations that invest in super-connected operations—with digital networks exposing real-time data, open standards enabling scalability, and humans maintaining command authority—position themselves to leverage whatever technologies emerge next.

The revolution isn’t in the AI capabilities themselves but in the infrastructure that lets those capabilities touch reality. Build the nervous system first, and the brain’s potential becomes achievable. Skip this step, and your AI investments remain impressive demonstrations disconnected from operational impact.

The question isn’t whether to invest in digital transformation but where to invest first. Craig’s answer is clear: start with the body your AI brain needs to function.

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If you’ve wondered why some manufacturing organizations consistently outperform their peers while others struggle to scale operational improvements, Episode 13 of Practitioners Unplugged provides the blueprint. Manufacturing excellence principles, not technology purchases, form the foundation of sustainable success. Ed Koch, operations veteran with over 20 years across Unilever, SAB Miller, and AB InBev, cuts through the digital transformation noise with a timeless truth: sustainable manufacturing excellence starts with building capabilities, not buying technology.”

Working across six continents and 120+ facilities, Ed helped design and implement SAB Miller’s legendary “Manufacturing Way”—a systematic approach to operational excellence that enabled rapid integration of acquisitions while maintaining performance standards. His philosophy challenges the current obsession with AI and automation: focus on developing people and processes first, then leverage technology as an accelerator.

As Ed puts it: “Manufacturing is a team sport, and excellence is only achieved through deliberate focus on continuous improvement and organizational learning.”

Here are the five key insights from our conversation with Ed:

The concept of organizational “ways” goes far beyond standard operating procedures. For SAB Miller, the Manufacturing Way served as one of eight strategic capabilities (alongside Marketing Way, HR Way, etc.) that provided a unified framework for a rapidly growing, acquisition-heavy business.

“The idea behind ways was really a single credible point of reference in how we operate, how we run the business, how we believed was the best way to run our operations,” Ed explains. This wasn’t about creating rigid processes but establishing enduring principles that could adapt to local contexts across Latin America, Europe, Asia, and Africa.

The genius of the approach lies in its structure: overarching principles that don’t change, organizational design that adapts to regional needs, competencies that build over time, and work practices that evolve with technology. Think of it as a four-layer framework where the foundation remains stable while the surface adapts to changing conditions.

This principle-based approach enabled SAB Miller to integrate acquisitions seamlessly. New facilities could understand “how things work around here” through a common language and methodology, accelerating both operational performance and career development across a global organization.

Ed’s observations working with manufacturing leaders worldwide reveal a stark divide: companies that treat manufacturing as a core competency versus those that view it as a cost center to optimize. The leaders consistently pull away from the pack because they understand that operational excellence creates sustainable competitive advantage.

“We have some clients that have fantastic performance and brilliant factories. One of the things that we observe is that the leaders continue to lead and in fact get faster, get better, faster. They innovate faster and pull away from the pack.”

These leading organizations share common traits: strong engaged leadership close to the workforce and factory floor, well-trained manufacturing technicians and operators, and what Ed describes as “a restlessness, a hunger to find new ways to perform better.”

The implication for executives is clear: manufacturing excellence isn’t achieved through periodic improvement initiatives or technology deployments. It requires sustained investment in building organizational capabilities that compound over time. Companies that understand this create performance gaps that competitors struggle to close.

When designing SAB Miller’s “brewery of the future,” Ed’s team asked a fundamental question: if we built a brewery with the latest processes and technology designed to operate for 30-40 years, what would a day in the life of an operator look like?

The answer revealed the inadequacy of traditional job descriptions. Modern operators must run equipment, ensure quality, perform basic maintenance, track performance, develop team members, and learn new skills continuously. It’s a complex, critical role that directly impacts business performance.

“These are the people that really add value to the product. These are the people that make the difference in the business,” Ed emphasizes. This led to the concept of the “master manufacturing technician”—a role that leverages all available capabilities to solve problems in real-time.

The parallel to pop culture is apt: like the Mandalorian with advanced gear and capabilities, the future manufacturing worker operates within a system that amplifies their problem-solving abilities rather than replacing them. This represents a fundamental shift from Henry Ford’s industrial model toward human-technology collaboration.

One of SAB Miller’s most powerful tools was a maturity model covering ten operational areas with defined stages of development. This created a common language across 120 facilities worldwide and enabled sophisticated internal benchmarking and knowledge transfer.

“It allowed us to see how all those facilities were both performing in terms of a defined set of metrics, but also how mature they were in terms of their work practices,” Ed explains. The system drove shared learning where lagging facilities could learn from leaders, creating a virtuous cycle of improvement.

The assessment process balanced self-evaluation with external calibration. Facilities conducted self-assessments twice yearly, with regional calibration covering one-third of operations annually. This three-year cycle ensured consistency while maintaining cost-effectiveness.

The key insight: maturity models must evolve as capabilities advance. What represented leading practice five years ago becomes middle-of-the-road today. Organizations must recalibrate their standards to maintain competitive advantage and drive continuous improvement.

While AI dominates current manufacturing conversations, Ed maintains focus on fundamental operational excellence. His approach to digital transformation reflects hard-won experience: establish solid operational foundations before adding technological complexity.

“I think if we create that standard way of operating around the world, it also creates a common language of people being able to talk to common processes and standard methodologies. When you start thinking about digitizing and using digital tools, you want to make sure that the processes and systems that you have in place, the fundamental building blocks, are good ones.”

Ed advocates for a hybrid approach combining digital insights with human problem-solving. While real-time data provides valuable performance information, gathering cross-functional teams around physical problem-solving boards often produces faster results than sophisticated digital tools.

The key is viewing technology as an enabler for proven methodologies rather than a replacement. Organizations with strong operational foundations can leverage AI and advanced analytics more effectively because they understand the underlying processes and can interpret technological insights within proper context.

Ed Koch’s experience across global manufacturing operations demonstrates that sustainable excellence comes from systematic capability building, not technology deployment. The Manufacturing Way approach—anchoring on enduring principles while adapting practices to local contexts—offers a proven framework for organizations seeking lasting competitive advantage.

The lessons extend beyond manufacturing to any operation-intensive business: identify strategic capabilities that differentiate your organization, build systematic approaches to develop those capabilities, create measurement systems that drive internal benchmarking, and maintain leadership that stays close to front-line execution.

As manufacturing faces pressure from shorter careers, frequent organizational changes, and technological disruption, Ed’s approach provides stability. By building capabilities that don’t depend on individual knowledge or specific technologies, organizations create resilience that survives workforce turnover and technological transitions.

The Manufacturing Way isn’t about resisting change—it’s about building the organizational foundation that enables rapid adaptation to whatever challenges emerge next. Companies that invest in these capabilities first position themselves to leverage new technologies effectively while maintaining operational stability.

Excellence is built one principle, one process, and one person at a time. But once established, it creates momentum that separates leaders from followers for decades.

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If you’ve been tracking the hype around AI in manufacturing, you’ve heard it all—pilot purgatory, proof-of-concepts that don’t scale, shiny tools that don’t move the P&L. Episode 12 of Practitioners Unplugged cuts through that fog with Jonathan Alexander, Global Manufacturing AI and Advanced Analytics Manager at Albemarle. His philosophy is refreshingly clear: start with operational excellence, use technology as an accelerator, and make context the backbone of everything.

Working within Albemarle’s global manufacturing excellence organization, Jonathan brings over 15 years of experience turning digital transformation buzzwords into measurable results. His approach challenges conventional wisdom: instead of chasing the latest AI trends, focus on solving universal manufacturing problems through standardized, scalable systems.

As Jonathan puts it: “Semantic models are king and context is king.”

Here are the five key insights from our conversation with Jonathan:

When asked where his function sits—OT or IT—Jonathan’s answer reveals Albemarle’s core philosophy: “I would consider myself neither OT or IT, I would consider myself manufacturing.” This manufacturing-first lens shapes everything: problem selection, architecture, and adoption strategies.

Jonathan’s most powerful analogy centers on infrastructure investment. Just as President Eisenhower’s Interstate Highway System transformed American commerce by creating standardized routes, manufacturing organizations need an “analytics highway” before they can scale AI initiatives.

“We had to say, well, how are we, what problems are we going to solve? And the traditional way of people doing digital transformation is they find a technology and then they go and apply it on a certain use case, and then they step back and take that technology and find another place for it.”

Instead of technology-first thinking, Albemarle started with their biggest universal problem: process variability. They built a standardized infrastructure using PI System’s Asset Framework to contextualize 76,000+ instruments across six sites. This 300-page standardization document—admittedly “the least fun thing” Jonathan ever created—became the foundation that enabled rapid deployment of analytics at scale.

The ROI parallel is striking: the Interstate Highway System generated $6 for every $1 invested over 70 years. Similarly, Albemarle’s infrastructure investment pays dividends through reusable templates that eliminate repetitive custom engineering work.

Albemarle didn’t chase isolated use cases. They picked a global class of problems—process variability—and built a standard path from raw signals to action. Rather than building custom dashboards for each use case, they standardized on Statistical Process Control (SPC) charts as their universal interface.

“We said, okay, imagine if we have all of our instruments monitored with this, with the right filtering so that they’re giving good signals. It’s kind of like the concept of Google Maps.”

Just as Google Maps uses consistent visual language across any destination, Albemarle’s approach means operators in Australia, China, or South America see the same interface: green dots for normal operation, red dots for out-of-control conditions. This eliminates training barriers and enables consistent interpretation across diverse workforces.

Think of SPC at scale as traffic conditions across thousands of “lanes” (tags): you can spot congestion instantly and know where to focus. It’s common-sense, teachable, and consistent—exactly what you need across regions, shifts, and skill levels.

Jonathan deliberately banned the term “dashboard” in favor of “action boards”—a semantic shift that fundamentally changed how his team approached analytics deployment.

“You can create all these great insights, but if nobody used them, it’s just another art piece on the wall. And so for us, what we wanted to do is teach people not to be builders. We didn’t want all of our engineers to spend all of their time building new calculations and new dashboards.”

The problem with traditional dashboards is that engineers love building them. Each creates their own version, then leaves the organization, forcing the next engineer to start over. This cycle wastes enormous resources on redundant development rather than value-generating activities.

With SPC as the front door to action, Albemarle layered in anomaly detection using principal component analysis (PCA). Here, the magic is the template. Albemarle created standard unit-operation templates (with derived versions for reactors, columns, filters, etc.) tied into the PI Asset Framework. Once contextualized, “we could build a machine learning model and… have it deployed across hundreds of operators all in one hour.”

They now run approximately 1,200 PCA models, typically trained on 2–12 months of relevant history for golden-batch-like behavior rather than strict predictive analytics. Standardization plus context equals speed.

A lot of manufacturers assume the historian is the heavy lift. For Albemarle, the harder part was building the semantic model over time and across generations of assets and drawings.

“The challenge for us, it wasn’t getting data into the system, it was contextualizing it in the right way,” Jonathan explains. Automation of P&IDs and tag lists fell short because “not all of our P and IDs and our tag lists were accurate. They were done by different standards over a different time.”

This contextualization challenge created unexpected resilience benefits. The true test of Albemarle’s system came during significant workforce turnover at one facility. Traditionally, losing multiple operators, engineers, and managers simultaneously would trigger weeks of downtime as institutional knowledge walked out the door.

“We had this system in place and in the past when that would’ve happened, that would’ve been just unbelievably disruptive to the manufacturing sites. But we didn’t, they didn’t experience that.”

The standardized analytics infrastructure maintained operational stability despite the knowledge drain. More importantly, it created a higher baseline performance level that doesn’t degrade when people leave. In a world of rotating contractors, generational change, and tight labor markets, this resilience is strategic.

While generative AI dominates current conversations, Jonathan maintains focus on traditional machine learning techniques that deliver measurable ROI. His principle component analysis models have prevented costly equipment failures by detecting correlation changes that human operators miss.

Albemarle is experimenting with genAI (like code acceleration in Microsoft Copilot), and the time savings can be stunning. But Jonathan is candid about the challenge: “The challenge that people are having with generative AI… is being able to specifically describe the business case and the ROI,” beyond soft savings.

“I think with the advances in generative AI right now, semantic models are king and context is king. ‘Cause we’ve actually done some stuff with generative AI, just dumping a bunch of data from here into there and kind of crossing our fingers and praying that it will understand the information that we give it and it doesn’t.”

As a result, genAI is “less than 10% of my time” today—while the team continues to scale SPC and machine-learning methods that tie directly to measurable process outcomes. Jonathan’s team uses generative AI where it adds clear value while continuing to expand traditional ML applications that directly impact manufacturing performance. This balanced approach avoids both “shiny object syndrome” and technological conservatism.

Jonathan Alexander’s journey at Albemarle demonstrates that successful AI at scale requires patience, standardization, and relentless focus on manufacturing fundamentals. Their approach—treating AI as an enabler for proven continuous improvement methodologies rather than a revolutionary replacement—offers a practical roadmap for other manufacturers.

The key lessons for practitioners: Anchor on variability and value through SPC at scale. Template everything to turn months into hours for model deployment. Invest in the semantic model because ingestion plumbing is necessary, but semantics is decisive. Design for turnover with standard visuals and thresholds. Keep genAI grounded with clear business cases tied to the semantic layer.

Jonathan sums up the ethos well: don’t let “the AI tail wag the operations excellence dog.” Technology is “just an enabler” to move faster through proven continuous improvement methods.

The lesson extends beyond AI to digital transformation broadly: sustainable change comes from solving universal problems through systematic approaches, not from deploying impressive technology demonstrations. Organizations that build their “analytics highway” first create the foundation for whatever technologies emerge next.

The highway is built one mile at a time, but once complete, it transforms how quickly you can reach any destination.

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In this episode of Practitioners Unplugged, we dive into the future of secondary packaging automation with Jason Head, Controls Development Engineer at Fallas Automation. Broadcasting from the floor of the Automate Show in Detroit, hosts Dante and Sree sit down with Jason to uncover how Fallas is addressing shifting demands in speed, flexibility, and data-driven design.

Fallas Automation builds case packers and secondary packaging systems that sit between primary packaging and end-of-line palletizing — a critical but often overlooked link in the packaging value stream. Jason brings over a decade of hands-on experience in installation, troubleshooting, and now R&D, giving him a front-row seat to how digital transformation is reshaping OEM roles, customer expectations, and machine intelligence.

Here are the five key takeaways from our conversation with Jason:

Gone are the days when a machine’s job was to “just run.” Today, end users demand data-rich systems capable of delivering performance metrics, predictive alerts, and operational visibility in real-time. Jason notes a dramatic uptick in customer requests for specific data structures, not just raw data points.

Each enterprise seems to have its own “custom standard,” requiring OEMs like Fallas to build tailored data delivery mechanisms. This complexity can slow deployment timelines and increase engineering lift — especially when aligning with proprietary IT/OT frameworks on the customer side.

“Each big corporation kind of has their own customized standard that we have to tailor our equipment to… and that definitely holds up getting machines out quickly.”

The packaging equipment of tomorrow must be natively data-integrated, not just sensor-ready.

One of the biggest themes in manufacturing today is reducing unplanned downtime. While predictive maintenance has been a buzzword for years, Jason sees real traction emerging now — particularly in sensor-based component monitoring.

Fallas is embedding smarter sensors that help detect part failures before they occur. By moving from scheduled downtime to condition-based maintenance, customers can avoid costly shutdowns and better plan maintenance cycles around actual usage patterns.

“We want to incorporate sensors to detect component failures… so our customers can do predictive maintenance instead of run-it-till-it-breaks.”

For manufacturers, that means longer machine life, higher uptime, and better OEE across the board.

With turnover rising and skill levels varying widely across facilities, OEMs must build machines that are easy to operate — no matter who’s on shift. Jason emphasizes the importance of designing systems that don’t require robotics expertise or specialized knowledge to run efficiently.

“We want to make it where you don’t have to be a robotics expert to package snack foods.”

This push toward human-centered HMI design and intuitive machine behavior is a direct response to the transient nature of today’s industrial workforce. The goal is clear: empower operators, reduce the learning curve, and standardize performance regardless of background.

When one of Fallas’s largest customers asked for a 50% increase in line speed, Jason and his team were forced to rethink everything — from software logic to end-of-arm tooling. Meeting that ask wasn’t just about better hardware; it demanded smarter engineering and faster iteration.

“They said, ‘We want to run 50% faster. Can you do it?’ Not only can you do it, but tell us when you’ll be able to deliver it.”

The ask was extreme — but not unrealistic. Jason’s team delivered. It required months of planning, iterative tuning on the customer site, and a coordinated effort across upstream and downstream systems. The lesson? Speed and flexibility are no longer opposing forces. The next-gen OEM must excel at both.

In a standout moment at the end of the episode, Jason delivers a powerful reminder for engineers and manufacturers alike:

“Don’t be an island.”

Jason reflects on his tendency — common among engineers — to want to solve every problem independently. But he’s quick to point out that vendors are an untapped resource, especially when it comes to understanding broader market trends and unlocking creative solutions.

“Vendors can be a great resource… they see market trends from a lot of different angles. So you can get a lot of insight about how to build a better machine.”

Collaboration with customers, integrators, and technology providers isn’t optional anymore — it’s essential to staying competitive in a fast-evolving market.

Jason’s insights offer a compelling snapshot of how OEMs are evolving from equipment builders to system partners. Fallas Automation’s future-ready mindset — one that blends data integration, intuitive design, rapid deployment, and collaborative innovation — is a model for the new industrial era.

Whether it’s designing machines that adapt to new packaging formats, embedding remote diagnostics and AI vision systems, or building intuitive UIs for transient workforces, Jason and his team are tackling Industry 4.0 challenges in real time.

The key message? The future belongs to fast-moving, flexible OEMs who put usability, connectivity, and collaboration at the core of their design philosophy.

As Fallas Automation proves, it’s not just about packing more products — it’s about packing more intelligence, adaptability, and service into every machine.

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In the latest episode of Practitioners Unplugged, recorded live from the bustling Automate show in Detroit, we cut through the noise of cutting-edge technology to spotlight a crucial, yet often overlooked, aspect of digital transformation: the human element. We’re joined by Jim Mayer, a passionate leader with 25 years of industry experience, who delves into the cultural side of change management in manufacturing. As Dante and Sree, our hosts, point out, while AI and robotics dominate the headlines at events like Automate, the true success or failure of digital initiatives often hinges on how people adapt and embrace new ways of working.

Jim, a self-described “resource provider” who started his career in industrial distribution after a stint as a carpenter, found his calling in machine shops, appreciating the camaraderie and practical nature of the people there. His journey through various roles, including advocating for machine shops at the NTMA and ultimately launching his own company focusing on “the humans of the industry,” reflects a deep understanding of the challenges and opportunities at the intersection of technology and the workforce. He emphasizes that while asset performance management has been a focus for years, the “fuzzy” human side has often been avoided, despite people being considered a company’s most important asset.

For manufacturing leaders grappling with implementing new technologies, Jim’s insights offer a vital roadmap for preparing their teams, fostering adoption, and sustaining long-term cultural change. Here are key takeaways from our conversation:

A common pitfall in digital transformation is the top-down imposition of technology without employee input. Jim recounts countless instances where owners attend trade shows, purchase impressive technology, and then instruct employees to implement it, often without asking if it’s truly needed or if it’s the right solution. The result? Expensive equipment gathering dust under tarps for years. Jim’s passion for people stems from realizing that technology’s true power emerges only at the intersection of human needs and technological capability. Successful technology adoption happens when employees are involved in the selection process, often by bringing them to shows or even sending them in leadership’s stead.

The financial world often views people as a cost item rather than an asset on the balance sheet. This leads to incongruencies in how human performance is measured. Jim points out that companies often measure employee turnover (when they leave) but not retention (when they stay), unlike how they measure the current ROI of machine tools. This reluctance to quantify “soft skills” like empathy, communication, or culture makes it scary for organizations accustomed to tangible metrics like machine depreciation or production output. However, Jim contends that the shift where AI impacts white-collar jobs, just as automation impacted blue-collar roles, is leveling the playing field, making the human aspect undeniable.

A common fear among the workforce is that AI and automation will replace their jobs. Jim, however, offers a powerful counter-narrative: AI won’t replace people on shop floors; rather, people who learn how to use AI, automation, and digital tools will replace those who don’t. He draws a parallel to the introduction of CNC machines – those who adopted the new programming methods thrived, while others either adapted to manual work or left the industry. This shift means the nature of jobs changes, often leading to upskilling and a more engaging career path. From machinists to programmers to robotics engineers, manufacturing offers a clear career trajectory for those embracing advanced skills.

Jim strongly asserts that “humans hate change” is a myth. People love change, but only when they author it – when they have a say, when it feels substantial, and when they can write what that change looks like. The 70% failure rate in digital transformation efforts often stems from owners buying technology without employee input.

Successful change initiatives involve:

For digital transformation to stick, KPIs must be aligned across the entire organization. If a machinist is measured on scrap rate, the owner should also be measured on scrap rate; if the floor is measured on OEE, the whole organization is. This fosters a “team win or team loss” mentality, connecting individual efforts to overall organizational success. Resistance to new, often lower, OEE metrics from automated systems can be overcome if performance management is a continuous process of regular conversations, not a surprising event. This consistent, small-scale effort leads to extraordinary long-term results, fostering a culture of accountability and continuous improvement.

Jim Mayer’s powerful message is clear: while technology accelerates our evolution, the future of manufacturing hinges on human-centered leadership and a proactive approach to change management. Success in digital transformation isn’t an afterthought; it must be at the forefront of strategy. By leading with empathy, inviting employees to co-author change, and fostering a culture of continuous learning and celebration, manufacturers can build resilient, innovative operations. Jim emphasizes that while change is never easy, it becomes significantly smoother when the entire team is engaged and supportive, rather than resistant. His insights underscore that the most advanced technologies will only reach their full potential when rooted in a strong, adaptable human culture.

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In the latest episode of Practitioners Unplugged, we took our microphones live to the vibrant Automate show in Detroit. This special setting offered a unique backdrop for our in-depth conversation with Daniel Harr, CEO of Delta Systems. With over two decades of deep industry experience, Daniel offers a pragmatic and insightful look at how custom automated solutions, especially for the demanding food industry, are undergoing a profound evolution. He cuts through the hype, revealing how a blend of necessity, technological advancement, and a strategic embrace of data is not just changing, but fundamentally reshaping the landscape of modern manufacturing.

At its core, Daniel’s insights center on the imperative for digital transformation to drive both efficiency and resilience in industrial operations. He unpacks how the recent pandemic dramatically accelerated the push for automation, transforming it from a mere competitive edge into a critical survival mechanism for many businesses. This was particularly true in the face of widespread labor shortages and rapidly shifting consumer demands. This new era of manufacturing demands a holistic view, requiring seamless integration of systems, valuable data, and a flexible, forward-thinking mindset at every level of an organization. Daniel emphasizes that success now hinges on adapting quickly and intelligently to an ever-changing environment.

For operations leaders, plant managers, and technology strategists navigating this increasingly complex landscape, Daniel’s conversation provides several critical takeaways, offering both challenges and actionable opportunities:

The COVID-19 pandemic served as far more than just a global health crisis; it was a powerful and unexpected accelerator for automation across the industrial spectrum. Manufacturers, particularly those in the essential food sector, swiftly moved from merely considering automation as a long-term investment to recognizing it as an immediate operational necessity. Daniel highlights how Delta Systems found itself directly instrumental in helping food manufacturers maintain crucial supply lines even as they grappled with severe labor force challenges. This period unequivocally demonstrated a clear trend towards machinery that requires significantly less manual intervention and increasingly leverages real-time data for unprecedented levels of optimization. The drive for automation wasn’t just about cutting costs; it was about ensuring continuity and resilience when traditional labor models faltered.

Daniel draws an important distinction between what he calls “intelligent machines” and the emerging frontier of truly “smart” or autonomous systems. He explains that intelligent machines, while highly sophisticated, largely operate based on high-end controls and meticulous pre-programmed logic. They execute tasks with remarkable speed and precision, maintaining efficiency by consistently normalizing processes and reacting according to their coded parameters.

The true leap, however, lies in the development and adoption of autonomous machines, where Artificial Intelligence (AI) plays a pivotal, transformative role. These advanced systems will possess the capability to analyze vast amounts of live data, make predictive adjustments on the fly, and even self-correct without human oversight. This revolutionary shift profoundly reduces the need for constant human intervention in repetitive or easily automated decision-making processes. The ultimate goal, Daniel asserts, isn’t to eliminate the human element from the factory floor. Instead, it’s about elevating the role of human workers to more high-value, complex problem-solving, and innovative tasks. This transition represents a significant cultural shift towards systems that can largely manage themselves, thereby freeing human operators to focus on strategic engagement and continuous improvement.

Traditional industrial automation has historically prioritized sheer efficiency and maximum throughput. However, Daniel argues that today’s rapidly evolving market demands a critical new imperative: agility. Consumer preferences shift quickly, product lifecycles shorten, and seasonal demands intensify, all of which necessitate automation systems designed for inherent adaptability.

Daniel stresses that building flexibility into automation systems right from the initial design phase is not just beneficial, but crucial, and far more cost-effective than attempting complex retrofits later on. This foresight allows manufacturers to seamlessly adapt to diverse materials, accommodate product variations, and respond to emerging market trends without requiring complete line overhauls. He references a strategic concept gaining traction among large Consumer Packaged Goods (CPG) companies: the “launch factory” model. These are highly agile operations, often outsourced to specialized co-packers, designed for rapid testing and introduction of new products. This distinct operational model enables swift market experimentation before successful items are brought in-house to highly optimized “cost factories.” This strategic separation allows companies to innovate aggressively without compromising their core operational efficiency.

Effective system interoperability and intelligent data utilization are no longer luxuries; they are paramount to sustained success in modern manufacturing. Daniel explains how the seamless flow of data between disparate systems enables crucial functions like predictive maintenance, significantly reduces costly downtime, and ultimately boosts overall operational efficiency. This critical capability has also spurred the escalating need for robust remote support and comprehensive lifecycle services. These offerings allow Original Equipment Manufacturers (OEMs) like Delta Systems to provide immediate, highly efficient assistance and ensure continuous, optimized operation for their clients, often preventing issues before they arise and dramatically reducing the need for costly on-site visits.

Daniel insightfully explores the critical topic of the manufacturing workforce. He posits that the factory workforce isn’t shrinking; instead, it’s undergoing a profound and necessary transformation. As operations become increasingly digital and automated, the demand shifts away from purely manual labor towards a highly skilled workforce adept at interacting with sophisticated digital systems. Training initiatives are evolving, moving beyond basic computer literacy to teaching the intricate nuances of complex industrial processes and advanced data interpretation. Daniel envisions a future where factory jobs are characterized more by intellectual engagement, strategic problem-solving, and technology management rather than traditional physical labor. This evolution, propelled by ongoing technological advancements and generational shifts in skill sets, is actively creating higher-quality, more engaging roles, ultimately helping to bridge the persistent labor gap that has challenged the industry for years.

Daniel Harr’s perspective is unequivocally clear: the future of manufacturing is not a solitary journey for any single company or technology. It is, at its heart, a collaborative endeavor. By fostering strong, strategic partnerships across companies, between diverse technologies, and within industries, manufacturers can collectively and strategically leverage innovation to stay not just competitive, but truly ahead of the curve. This collaborative approach ensures that industrial operations are not only highly efficient and resilient in the face of disruption, but also proactively prepared for whatever complex challenges and exciting opportunities the future industrial landscape presents. His insights from Automate offer an invaluable roadmap for anyone aiming to thrive in the rapidly evolving and increasingly interconnected world of Industry 4.0.

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