What It Really Takes to Be Ready for Operational Intelligence, Part 2
Lessons From the Plant Floor
“Go live, to me, is kind of just the start of where the value gets created.”
It’s not what most people expect to hear about a technology project, and it’s one of many ideas from Rob Bansek and Devon Keane’s podcast conversation that challenge how organizations think about AI readiness. In Part 1, they explained why technology is often the easier part of operational intelligence (AI). In Part 2, they take those ideas to the plant floor with stories of what happens when readiness is missing, and what it takes to get it right.
When Smart Isn’t Enough
Devon’s first story centers on an R&D group he calls “some of the smartest and most impressive people who I’ve ever worked with.” They built their own sensing kit and a machine learning model to guide frontline operators, exactly the kind of human-centered approach most organizations say they want. Yet the project still ran into two problems that no amount of talent or technology could solve, and anyone who has led a project on the plant floor will recognize them right away.
Rob’s story comes from the water municipalities InSource works with. An AI model recommends moving water first thing in the morning, when energy prices are lower. It’s a perfectly logical recommendation, until you’re in the South and an afternoon thunderstorm rolls in. “There are brilliant people that run these models, way smarter than me, that understand the math behind them,” Rob says. But without the context of the process and the environment, the model doesn’t have the full picture.
The episode also looks at a new AI recommendation engine for air traffic controllers and what it takes to build recommendations people can trust when the stakes are that high. In every case, the lesson is the same: a model only knows what it’s been given, and the context around it matters just as much as the math.
Choosing Where to Start, and What Comes After
Since joining InSource, Devon has been leading use case discovery workshops that bring leadership and the frontline into the same room to decide which problems to solve first. That means weighing value and effort, but also data readiness, compliance, competitive advantage, and what the business needs most right now. As Devon puts it, “If you’re going to invest some dollars into a new technology, into a new process, you better have a return on investment. Otherwise, that technology is probably going to get cut.”
Still, the biggest number on the board isn’t always the right place to start. Sometimes the use case with the highest potential ROI is the one an organization is least ready to take on, and a smaller, well-defined win builds the foundation needed for bigger projects later.
Choosing the right problem is only the beginning, which is where Rob’s go-live comment comes from. He has watched organizations define the problem and feel ready, only to be caught off guard by the effort and change that follow implementation. Many teams treat go-live as the finish line. For Rob, it’s where the real work starts, and bringing rigor to that stage is often what separates a successful project from a pilot that quietly fades away.
Manufacturers Aren’t Short on Data
Devon calls data probably the largest technical challenge behind today’s AI solutions. Trying to modernize everything at once can feel like trying to “boil the ocean,” so he recommends tackling data readiness one use case at a time, solving real problems while the data strategy takes shape.
Much of the market implies manufacturers don’t have good data, but industrial organizations have been collecting it through historians, MES systems, and other tools for 40 or 50 years. He’s just as skeptical of one-size-fits-all answers, whether a vendor is pushing a unified namespace, a data lake, or the cloud. Start with the use case, and the right approach becomes much clearer.
That experience is part of what drew Devon to InSource, which works across the entire stack, from HMI and SCADA to historians, MES, CMMS, and the analytics and AI layer on top. It’s also where Rob decides to make the sales pitch, “just because I can.” After roughly 30 years in business, InSource serves more than 8,000 active manufacturing and industrial customers. The new practice builds on that foundation, helping organizations define the problem, understand its value, and get ready to solve it. And maybe even “bring some joy” along the way.
Different Styles, Same Mission
Rob can take a single point “five different directions,” while Devon keeps the conversation grounded in defining success before anything gets built. Together, they sum up their philosophy with a simple analogy: readiness is like building the foundation for your house. “If you’re not rigorous, you’re going to get some cracking in the wrong places.”
Next time, they’ll dig into real problem statements from real customers, the good and the bad. As Devon puts it, that’s “where the rigor starts.”a vendor is pushing a unified namespace, a data lake, or moving everything to the cloud. Start with the use case, and the right approach becomes much clearer.
Hear the Full Conversation
Hear Rob and Devon’s full conversation on what it really takes to make operational intelligence work, from the shiny tool to the people who make the final call.