Q1. Chaitanya, when you look at industrial operations in 2026, what do you see that most people are missing?
A: Most people still see industrial AI as a smarter alert system. They talk about dashboards, alarms, and predictive models that tell you what might break next week. What they miss is that the real frontier is decision architecture—who or what makes which decision, under what constraints, and with what level of autonomy. We’re moving from “Can we predict this failure?” to “Who should decide what to do about it: a human, an AI agent, or both together—and how do we design that collaboration?” That’s where competitiveness will be decided over the next decade.
Q2. You use the phrase “progressive autonomy” a lot. What does that mean in a factory context?
A: Progressive autonomy is the idea that industrial operations should not jump from manual control straight to full autonomy. Instead, they move through deliberate stages. First, AI observes and reports. Then it predicts and recommends. Only after building trust and transparency does it start taking bounded actions on its own—within clearly defined guardrails. In a plant, that might mean an AI agent can autonomously adjust compressor speed by a few percent, but anything impacting safety, quality, or major economics still requires human validation. Over time, as the system proves itself, those bounds expand in specific, low-risk domains. It’s an evolution, not a switch.
Q3. How is this different from the usual Industry 4.0 narrative we’ve heard for years?
A: Industry 4.0 conversations often stop at buzzwords: digital twins, IoT, cloud, predictive maintenance. The narrative is usually about connectivity and visibility. What I’m interested in is operational agency—how AI agents perceive, decide, and act in real time, and how they collaborate with humans. Instead of saying “we connected 5,000 sensors,” I care about questions like: How many decisions did those sensors help automate? How many false alarms did we eliminate? How much human time did we free for higher‑value problem solving? The shift is from technology for visibility to technology for intelligent action.
Q4. You advocate for multi‑agent AI systems instead of one central ‘brain’. Why?
A: If you look at how high-performing plants actually run, they’re driven by specialized teams: maintenance, production, quality, safety. Each team has its own expertise and responsibilities, but they coordinate. Multi‑agent AI reflects that reality. Instead of one monolithic model trying to do everything, you deploy specialized agents—one focused on equipment health, another on scheduling, another on quality, another on safety. Each agent is world‑class at its niche and they negotiate with each other when decisions have trade‑offs. This makes the system more interpretable, more resilient, and much closer to how human organizations already think.
Q5. Many operators and engineers worry that AI will replace them. What do you see on the ground?
A: On the ground, where deployments are working well, I see the opposite. In plants where agentic AI is implemented thoughtfully, operators move from “firefighting mode” to “strategic oversight mode.” AI agents handle the 200 micro‑decisions per shift—tiny parameter tweaks, routine optimizations, low‑risk anomalies. Humans focus on the five decisions that actually require judgment: unusual process behavior, complex trade‑offs, customer-critical situations. Satisfaction goes up because people spend less time acknowledging alarms and more time solving meaningful problems. The fear is about replacement; the reality, when done right, is role elevation.
Q6. You talk about “human–AI teaming models.” What does a good teaming model look like in practice?
A: A good teaming model is explicit about who does what. For example, AI monitors assets continuously, flags issues, recommends specific actions with clear reasoning, and can execute certain low‑risk actions autonomously. Humans define the guardrails, approve or override high‑impact decisions, and provide contextual knowledge the AI doesn’t have—like upcoming customer commitments, political factors, or unique process quirks. The key ingredients are transparency (the AI must explain why), reversibility (humans can override easily), and learning (the system improves based on those overrides). When that loop is tight, you don’t get “AI vs human”; you get a shared control system.
Q7. Where do you see the biggest value right now: alerts, recommendations, or autonomous actions?
A: The sweet spot today is recommendation systems with bounded autonomy. Pure alerts create fatigue; people drown in notifications. Full autonomy is only appropriate in well‑defined, low‑risk domains. Recommendations sit in the middle: the AI analyzes millions of datapoints, proposes a concrete action, quantifies the impact, and lets a human decide. Over time, some of those recommendation domains graduate into bounded autonomy because the data shows the system is consistently right. So if you ask me where most manufacturers should focus in 2026, it’s building high‑quality recommendation layers that are trustworthy and explainable.
Q8. You often contrast “process mining” with “process intelligence.” How are they different?
A: Traditional process mining is like a forensic report. It tells you what happened last week or last month: where the bottlenecks were, where orders got stuck. That’s useful, but it’s backwards‑looking. Process intelligence is live. It continuously ingests event data from different stages, identifies where the current constraint is right now, and prescribes what to do about it—reroute work orders, reallocate resources, adjust parameters. One is a rear‑view mirror; the other is a real‑time co‑pilot. In hyper‑competitive industries, that difference is measured in millions of dollars of recovered throughput.
Q9. Edge AI seems to be a recurring theme in your work. Why is running AI at the edge so important?
A: In industrial environments, physics sets the rules. If a defect is created in 200 milliseconds on a paint line, a model sitting in a distant cloud that needs 800 milliseconds round‑trip is simply too late. Edge AI puts intelligence at the point of action—on the line, on the crane, on the compressor. It can analyze sensor or camera data in tens of milliseconds and make micro‑adjustments before problems become scrap or downtime. The cloud still matters—for heavy training, cross‑site learning, long‑horizon optimization—but the moment‑to‑moment, latency‑critical decisions belong at the edge. Getting that split right is one of the most expensive mistakes I see manufacturers make.
Q10. Digital twins are everywhere in marketing. You’ve argued many “twins” are actually just “shadows.” What’s the difference?
A: A digital shadow is a visual mirror: it shows you what is happening or has happened, often very beautifully, but it doesn’t actively help you decide. A true digital twin is interactive and prescriptive. It ingests real‑time data, simulates what could happen under different scenarios, and recommends the optimal action. If your system can’t answer questions like “What if I delay this maintenance by 48 hours?” with quantifiable risk and economic impact, you don’t have a twin—you have a dashboard. The real leap in value comes when the digital model becomes a decision partner, not just a visualization layer.
Q11. If a plant leader wants to start this journey tomorrow, what are the first three moves you’d recommend?
A: First, map your decisions, not your assets. List the critical decisions made daily and classify them by risk and complexity—that will tell you where AI can help first. Second, pick one or two processes where latency, cost impact, and data availability align—for example, quality inspection on a single line or maintenance planning for a specific asset class—and build a tightly scoped pilot with clear metrics. Third, design the human–AI contract upfront: who owns which decisions, where autonomy is allowed, how overrides work, and how success is measured. If you get those three right, the technology pieces—models, sensors, infrastructure—become enablers rather than the main story.
Q12. Looking ahead to 2030, what does a mature AI‑driven factory look like to you?
A: By 2030, I expect roughly half of operational decisions in mature factories to be made by AI agents. That doesn’t mean humans are sidelined; it means humans are operating at a different altitude. Plant leaders will focus on strategy, innovation, and navigating ambiguity. AI agents will coordinate execution: scheduling, parameter tuning, energy optimization, routine maintenance, real‑time quality control. You’ll see multi‑agent operating systems where engineers manage policies, constraints, and objectivesrather than individual alarms. The plants that win won’t be the ones with the most models deployed, but the ones that design the most effective collaboration between human intent and machine intelligence.
Q13. Finally, on a personal level, what drives your focus on this intersection of AI and industrial operations?
A: I’ve always been fascinated by complex, noisy environments where small decisions have huge consequences—cement kilns, steel mills, chemical reactors. These are places where human expertise is deep, culture is strong, and mistakes are expensive. AI, done well, is a way to honor that expertise, not erase it. It can capture patterns humans can’t see, but it still needs human judgment to define what “good” looks like. What drives me is the idea that we can build systems where operators and engineers are not overwhelmed by data and alarms, but augmented by agents that make them more effective, more strategic, and frankly, more fulfilled in their work.
Q14. You’ve written about the shift from “audits to intelligence” in manufacturing processes. What does that mean for plant leaders?
A: Traditional audits are episodic and backward-looking—they tell you, once a quarter, where your processes deviated from the ideal. Process intelligence is continuous and forward‑leaning: it watches live flows, detects emerging bottlenecks, and recommends interventions before they become chronic problems. For plant leaders, that means you stop treating process excellence as a periodic project and start running it as an always‑on capability, where the system itself surfaces the next best improvement every day.
Q15. You also write frequently about predictive maintenance. What are the biggest misconceptions you encounter?
A: The first misconception is that predictive maintenance is just about avoiding breakdowns; in reality, it’s about designing reliability as a competitive advantage. The second is that success is sensor‑first, when in practice the winning plants start with clearly quantified pain—unplanned outages, scrap, safety risk—and work backward to the data and models they actually need. The third is that accuracy is a “nice to have”; done properly, you treat every prediction like a forecast you would bet millions on, and you measure it with the same rigor you’d use in finance.
Q16. In your view, what separates plants that ‘experiment’ with AI from those that actually scale it?
A: Plants that experiment tend to run isolated pilots—one line, one asset, one model—with vague success criteria. The plants that scale AI treat it like an operating system: they standardize data foundations, build shared agentic capabilities, and align every deployment to business outcomes like uptime, throughput, or energy intensity. Crucially, they invest as much in people—training, change management, frontline champions—as they do in models and infrastructure, so adoption becomes cultural, not just technical.
Q17. You often mention packaging and sustainability as an area ripe for prescriptive AI. What draws you to that space?
A: Packaging is a perfect example of multi‑variable complexity: cost, quality, regulations, circularity, consumer preference, supply risk—all moving at once. It’s no longer enough to ask, “Is this material recyclable?”; you have to ask, “Is it compliant across markets, scalable in supply, economically viable, and aligned with brand and ESG commitments?” Prescriptive AI is uniquely suited to that problem because it can simulate trade‑offs in real time and give decision‑makers an evidence‑backed recommendation instead of a long list of static options.
Q18. System integrators are playing a bigger role in data‑led manufacturing. How do you see their mandate evolving?
A: Historically, system integrators were judged on how cleanly they could connect machines, lines, and software. Now, the best integrators are becoming outcome partners: they don’t just wire systems; they co‑design architectures where every integration is measurable in terms of uptime, yield, and working capital. In that world, prescriptive and agentic AI become native to integration projects, and integrators are as fluent in data contracts and operating KPIs as they are in PLCs and networks.
Q19. With so many AI buzzwords in the market, how should an executive evaluate what’s real and what’s hype?
A: I usually suggest three filters. First, ask how the solution ties to hard outcomes—downtime reduced, scrap avoided, energy saved—and demand credible baselines and timelines. Second, look for operational fit: does it work with your people, assets, and workflows, or does it assume a “greenfield” plant that doesn’t exist? Third, test for explainability: if the vendor cannot clearly explain how decisions are made and governed, you’re buying opacity, not intelligence.
Q20. You’ve worked across geographies in manufacturing. What common patterns do you see in leaders who succeed with AI‑driven transformation?
A: The most successful leaders share three traits. They are brutally clear on the specific problems they’re solving and the economics behind them. They are humble enough to co‑create with their teams, using frontline feedback to shape deployments instead of dictating from the boardroom. And they are patient with systems but impatient with proof—they expect early pilots to show directional value fast, even if the full transformation is a multi‑year journey.
Q21. What advice would you give to young professionals who want to build a career at the intersection of AI and industrial operations?
A: Build a dual fluency. Learn the language of plants—maintenance practices, process flows, safety culture—just as deeply as you learn models, data engineering, and AI tools. The people who will be most valuable over the next decade are those who can stand on a shop floor in steel‑toe boots, understand the constraint in front of them, and translate it into an AI‑driven solution that operators actually trust and use.
Who is Chaiitanya Bulusu?
Chaiitanya Bulusu is an industrial AI and manufacturing intelligence leader whose work focuses on progressive autonomy, predictive maintenance, and prescriptive decision‑making for complex operations across sectors like cement, steel, mining, and packaging. He serves as Senior Vice President and Head of Americas partnerships and Growth ecosystems at Infinite Uptime, where he helps manufacturers unlock measurable gains in uptime, reliability, and sustainable growth through AI‑driven PlantOS, prescriptive maintenance, and production‑outcomes‑as‑a‑service models.
https://www.linkedin.com/in/bulusuchaiitanya/
