Moving from insight to automated action remains rare in a space crowded with advisory level insights.
By Jennifer Shine, Principal Solution Engineer, Imubit
Industrial AI is having a moment—and for good reason. Across refining and manufacturing, companies are focusing on digital transformation, betting that data and algorithms can unlock new levels of performance. But if you’ve spent time trying to make sense of the market, you’ve likely walked away more confused than confident.
LNS Research recently published the blog “Five Ways Industrial AI is Shaking Up Manufacturing” which breaks the incredibly broad Industrial AI market into more manageable, use case driven bites. The piece categorizes more than 40 technology providers into one or more sub-categories including asset monitoring, process analytics, process control, productivity, and safety. Yet one of these categories is not like the others. While many of the vendors mentioned capture value by providing advisory insights to be actioned, only 6 players mentioned in the ‘process control’ sub-category are providing full closed-loop automation of identified insights. That’s a staggering gap—and it speaks volumes.
Why is that? Why are the majority of “AI” providers still operating in open-loop advisory mode, offering dashboards and insights, but stopping short of truly optimized control? The short answer: closed-loop is hard. The longer answer is even more important—and it’s where the future of the industry is heading.
If It Were Easy, Everyone Would Do It
Let’s be clear: it’s not a lack of ambition keeping companies from closed-loop AI. It’s the sheer complexity of the task. Industrial plants are among the most secure, high-stakes environments in the world. Any AI system that closes the loop—making real-time decisions that affect throughput, emissions, or safety—has to meet a high bar.
You’re not just dealing with data science. You’re integrating with a range of systems—distributed control systems (DCS), programmable logic controllers (PLCs), existing MPCs or legacy APCs, and any possible combination of the aforementioned—all while navigating strict cybersecurity protocols, meeting rigorous process safety requirements, and demonstrating deterministic behavior to some of the most technically demanding and skeptical stakeholders in the industry. That’s before you even get to change management or operational readiness.
Most AI vendors stop short of closed-loop control because they simply don’t have the stack—or the domain knowledge—to cross that threshold. They may offer strong analytics or elegant user experience, but when it comes to operationalizing those insights into autonomous plant action, they hit a wall.
Advisory-Only AI Leaves Value on the Table
The first thing to examine when evaluating how much value an AI product can deliver is whether it depends on a human to act. I’ll be the first to acknowledge that domain experts are absolutely essential. They understand the plant, the context, and the goals in a way no model ever can. But let’s hypothesize that we can build an AI model that can accurately predict and prescribe what should be done to best optimize the plant. To do so requires your console operator to move a dozen variables every five minutes. That’s not a realistic ask for even your best operator.
This is the trap of open-loop advisory systems. They offer insights—sometimes very sophisticated ones—but no reliable path to act on them consistently, completely, and continuously. The more complex your plant and your economic objectives, the more complex the optimal solution—and the more essential it becomes to automate execution. Put simply: the value of AI is in the doing, not just the knowing. True transformation requires closed-loop optimization, where the model doesn’t just advise—it acts.
Solving the Hardest Problems in Closed Loop
At Imubit, we specialize in what others shy away from: delivering real-time, closed-loop AI optimization (AIO) in the world’s most complex industrial environments. Our technology platform was built from the ground up to meet the rigorous demands of the process industries. We integrate directly with plant control systems, operate securely within your industrial network, and continuously optimize entire units.
We’ve developed a proprietary AIO Engine that learns a plant’s true economic and physical behavior, even in places traditional models break down. It’s fully engineer-configurable, auditable, and designed for ongoing adaptation in a live plant environment. Imubit doesn’t just tell your team what could be better—in a fraction of the time it would take for the insight to make it down the communication chain to the control room, Imubit has already made the adjustment. It executes the optimized strategy, continuously adjusts to changing conditions, and makes sure value is captured, not left waiting on emailed targets or a screen.
Technology is only half of the Imubit story—adoption, trust, and impact are what truly make it work. A team of former process engineering and process controls SMEs guide clients through a robust delivery process, involving plant engineers and operators at every step.
Don’t Settle for Less Than Actionable
The Industrial AI space will continue to be noisy, but it doesn’t have to be confusing. When evaluating vendors, look beyond the dashboards and ask: Is this actually delivering plant-level impact?
Learn more about what sets Imubit Closed Loop AI Optimization (AIO) technology apart in Demystifying Industrial AI by Imubit CTO Nadav Cohen. Download the report.
