Dan Furrow and Luis Atencio of Wesco on breaking data silos, orchestrating systems and delivering real plant-floor value. Why the right questions — not just new technology — determine success on the plant floor.
In the modern manufacturing environment, there is more technology than ever before. Engineers are working with smarter controllers, connected sensors, AI-powered analytics and dashboards promising operational clarity. But despite all this technology, many digital transformation efforts still struggle to get off the ground.
In Episode 10 of the Ctrl+Alt+Mfg Podcast, Dan Furrow, senior vice president and general manager for Wesco’s U.S. industrial global accounts and international markets, and Luis Atencio, solutions architect for Wesco’s smart manufacturing team, attempt to get to the bottom of that dilemma. According to Furrow and Atencio, technology isn’t really the problem. It’s more about orchestration and asking the right questions.
Modernization is not transformation
One of the first misconceptions Furrow addressed is the tendency to treat modernization and digital transformation as interchangeable terms. While they are interconnected, they are far from the same thing.
“They’re often used interchangeably,” Furrow said, “but really one does enable the other.”
Modernization, he explained, is about building the physical foundation — upgrading equipment, deploying smarter hardware and enabling greater data collection — and is infrastructure focused. Digital transformation, by contrast, is about leveraging that data to drive measurable business outcomes.
“Digital transformation is when you start to get to the phase where you can properly aggregate and leverage all that data that your new modernized infrastructure is capable of creating,” Furrow said.
Without modernization, transformation efforts lack data depth. Without transformation, modernization simply creates islands of information. That gap between infrastructure and insight is where many manufacturers stall.
Embracing digital transformation friction
Change can be difficult everywhere, especially in manufacturing environments, but it shouldn’t be feared.
“Friction is a good word,” Furrow said. “Friction in and of itself is not necessarily a bad thing.”
Manufacturers encounter resistance at every stage, including capital budget approvals, ROI concerns, siloed systems, workforce adaptation and integration complexity. Even successful projects generate organizational tension as responsibilities shift.
“You’ve moved a lot of cheese in the operation,” Furrow said.
Yet friction often signals progress. The real risk is not resistance. It’s fragmentation.
Data silos remain one of the most persistent obstacles. Disparate systems across production, maintenance, quality and enterprise functions prevent data from becoming actionable intelligence. Furrow emphasized that true transformation requires enterprise-wide orchestration.
“When you talk about proper digital transformation, you’re talking about a layer that is enterprise wide,” he said. “Plugging that all into a common backbone, that’s where it becomes a little more art than science.”
Outcome before algorithm
If Furrow framed the structural challenge, Atencio focused on mindset and cultural shift.
“One key difference between modernization and digital transformation is the drivers that are behind them,” Atencio said.
Modernization is often reactive, addressing factors like obsolescence or cybersecurity risks. Transformation, however, must be outcome driven. His guiding principle: “Outcome before the algorithm.”
Too often, organizations start with technology rather than business need. Atencio described a common misstep: “Sometimes we hear, ‘We need AI. What can we use it for?’” That approach, he warned, “leads to proof of concept without impact, orphan models, demos that never scale.”
Instead, manufacturers should begin with metrics that matter — overall equipment effectiveness (OEE), mean time to repair (MTTR), scrap reduction, energy efficiency or knowledge retention — and work backward to determine whether AI or advanced analytics are appropriate tools.
“Asking the better question, which is, ‘What is the business outcome that we’re addressing?’” Atencio said, is what separates sustainable initiatives from stalled experiments.
Data is abundant; governance is not
Manufacturers used to struggle to generate data, but the real struggle now is managing the avalanche of data that is produced every day
“Data is already there,” Atencio said. Most organizations fall somewhere between isolated automation and partial integration. The problem is not collection but governance.
Who owns the data? Maintenance? Quality? Production? Without accountability, data remains underutilized.
Atencio advocates adopting a DataOps approach early in the transformation journey. That includes defining governance structures, assigning ownership and establishing processes for delivering reliable data to internal stakeholders.
“If I work in a quality organization, I might benefit from a prediction,” he said. “But if I work in maintenance, I would probably benefit from the same prediction for triggering an automatic work order.”
The same data can support multiple use cases if it is structured and accessible.
AI as knowledge buffer
Beyond performance optimization, AI is also emerging as a tool for workforce continuity.
“Most companies are dealing with similar labor-related issues — aging workforce, renewing workforce,” Furrow said. That loss of tribal knowledge presents operational risk.
“There’s never a full replacement for tribal knowledge,” he said. “However, when you have the level of data coming in … and you can have a very strong AI layer of inference on top of that, that’s the closest you can come to creating a real buffer.”
Atencio shared practical examples from recent demonstrations. In one scenario, an AI troubleshooting tool embedded in a human-machine interface (HMI) allowed operators to input an issue, such as a joint collision, and receive step-by-step guidance. If needed, the system could trigger a maintenance work order with a single click.
In another example, condition-monitoring data from a sensor automatically generated maintenance requests when thresholds were exceeded. The goal is not to produce flashy dashboards, but to reduce unplanned downtime and improve responsiveness.
“Instead of relying on manual checks or dashboards, the equipment or systems can automatically alert teams when something drops below a performance threshold,” Atencio said.
Integration is make or break
Technology complexity makes orchestration critical. Digital transformation often involves multiple vendors, software layers and hardware platforms. Poorly coordinated integration can disrupt production.
“We say this often: Any integration is make or break, but breaking is not an option,” Furrow said.
Manufacturers must balance transformation with operational continuity. Implementation without downtime requires planning, contingencies and experience.
Furrow stressed the importance of selecting partners with vertical expertise. Even companies attempting standardized rollouts across multiple facilities often find each site presents unique challenges.
“You’ll have a vision of, ‘We’ve got 20 sites. We want to do a cookie cutter integration,’” he said. “It ends up as 20 incredibly different integrations.”
Practical starting points for transformation
For manufacturers beginning (or restarting) their digital journey, Atencio offered concrete guidance. First, follow established IT/OT convergence frameworks to ensure scalable and secure architectures. Reference models such as converged plantwide Ethernet (CPWE), co-developed by Rockwell Automation and Cisco, provide structured guidance for resilient network design. Second, prioritize governance early.
“Adopting a DataOps approach early is beneficial,” Atencio said. Organizations must understand “how are we accountable for data and how are we going to govern data in our organization.”
Finally, start with the right question. Technology alone does not drive transformation. It takes clear objectives and an understanding of the business need. As Atencio put it, digital initiatives succeed when they focus not on the tool of the moment, but on the measurable outcome that matters most.
In an era saturated with AI announcements and modernization projects, that may be the most transformative insight of all.
The Ctrl+Alt+Mfg Podcast
Make sure to check out other episodes of the Ctrl+Alt+Mfg podcast, where hosts Gary Cohen and Stephanie Neil discuss a range of digital transformation insights.
Ep. 1: Resetting and Rethinking Manufacturing
Ep. 2: Uniting Disparate Data With John Lee, Matrix Technologies
Ep. 3: Rethinking OT Security With Leah and Jeremy Dodson, Piqued Solutions
Ep. 4: Making Digital Transformation Real With Alicia Lomas, Lomas Manufacturing
Ep. 5: Reducing MES Project Risk With Ryan Crownover, Vertech
Ep. 6: Digital Transformation – Hype, Reality & What’s Next With Mike Ouellette, Engineering.com
Ep. 8: Inside the 2026 State of Automation Report, with Mark Hoske, Control Engineering
Ep. 9: Ctrl+Alt+Mfg Ep. 9: When cyberattacks go physical, with Ian Bramson of Black & Veatch
