Old order is not being overturned, but reconfigured by AI, says Alex Molinaroli
With the advent of artificial intelligence, the industrial world is undergoing a major transformation. As it moves from complex automation to intelligent operations, the new industrial world may focus on enhanced productivity, sustainability, and faster decision-making. And yet, veteran industry leader Alex Molinaroli, former CEO of Johnson Controls, believes the old order is not being overturned so much as reconfigured. He reasons that the machinery is still there - chillers, controls, fire systems, energy networks - but it now hums with a second layer of intelligence. In this environment, he sees the shift as a leadership challenge rather than a technological leap for a big organization like Johnson Controls, and that the deeper transformation will lie in how leaders think about systems - how they design them, govern them, and, crucially, earn trust through them. Companies that can combine intelligent systems with consistent performance and customer confidence may be better placed to lead, he predicts.
When Molinaroli led Johnson Controls, the promise of building technology was rooted in efficiency and integration. Systems were becoming connected, but they were still largely deterministic, designed to respond to predefined conditions. Today, the landscape is more fluid. AI in infrastructure has introduced adaptability. Systems can learn from patterns, anticipate failures, and optimize performance in real time. The difference is subtle but profound: earlier, systems executed logic. Now, they generate it. Asked if he were advising leaders at Johnson Controls today, what would be his top strategic priority in the AI era, Molinaroli said, “Johnson Controls has well over a hundred years of customer experiences, millions of interactions, and quite possibly billions of solutions that they have provided for their customers. Each of these experiences has been captured within delivery and service tools, building automation systems, and company databases. With AI tools, all of these seemingly unique experiences and solutions can be easily mined and utilized to provide powerful and exact solutions for each and every customer interaction. The tens of thousands of daily interactions with customers can bring with them the benefit of this organizational knowledge for each interaction.”
Alex Molinaroli’s priority would not be to “adopt AI” in the abstract, but to embed it meaningfully into products and services. He would likely ask a simple but demanding question: Does AI improve outcomes for the customer, or merely add complexity? In practical terms, this means turning every interaction into a moment of compounded intelligence. His second priority would center on operational systems. AI’s greatest industrial value may not lie in futuristic applications, but in disciplined execution - optimizing energy efficiency, improving uptime, and refining performance across portfolios. Buildings, in this vision, do not operate in isolation. They learn from one another. A solution discovered in one facility can inform another, even across geographies. Over time, this creates a network effect of a distributed intelligence layer across the built environment.
Most importantly, Molinaroli prioritizes restraint. As systems grow more capable, they also grow more complex. Alex has long emphasized disciplined deployment, and that instinct would only sharpen in the AI era. Not every capability should be activated everywhere. Early adoption, he would argue, belongs with sophisticated customers willing to experiment, iterate, and co-develop. Innovation, in other words, is not just about speed. It is about learning.
AI has introduced a paradox for industrial companies, he says. On one hand, scale matters more than ever. Large organizations like Johnson Controls possess proprietary data and the resources to deploy advanced systems, advantages that position them as early beneficiaries. On the other hand, those same technologies lower barriers to entry. Smaller firms can access tools, insights, and delivery models that were once out of reach. Remote diagnostics, automated workflows, and AI-driven insights compress the gap between incumbents and challengers. Alex Molinaroli views this not as a threat to be resisted, but as a condition to be managed.
Asked if AI will strengthen or erode competitive advantage, Molinaroli’s answer is both. “The large companies will do better to innovate new solutions and embed solutions within their products to fight off being commoditized. The market will change quickly, and if large companies don’t stay ahead of technology, they will find it difficult to compete,” he says.
Nowhere is the leadership challenge more acute than in balancing innovation with reliability, Molinaroli believes. Building technology operates in critical environments - hospitals, airports, data centers - where failure is not an option. Introducing AI into these systems raises the stakes. Molinaroli frames this as a deployment question rather than a technological one. The solution lies in segmentation, he says, by deploying new capabilities first with forward-leaning customers who understand the risks and are willing to participate in refinement. “Working together helps manage team members’ concerns about their future role as they get prepared and helps the organization get ready for a changing workforce and service delivery model,” he says. In this model, innovation becomes collaborative. Customers are not passive recipients but active participants in the development process.
If there is a single thread running through Molinaroli’s perspective, it is trust. “Relationships built on trust exist within the industry. Many large and sophisticated institutional customers often partner with industrial solution providers for new product development and product testing. Usually, these customers are quite capable and are actually eager to be a partner in the development of new solutions and products,” he says. Molinaroli emphasizes open communication with teams adapting to new roles, with suppliers navigating shifting expectations, and with customers integrating increasingly complex systems into their operations. Trust is not simply about reliability in performance. It is about predictability in behavior, he says. Despite the pace of change, Molinaroli’s leadership principles remain strikingly consistent. Transparency. Collaboration. Discipline. These are not relics of a pre-AI era, but conditions that make advanced systems workable at scale.
What emerges from Molinaroli’s reflections is less a retrospective and more a framework. AI in infrastructure will continue to evolve. Smart buildings will become even more smarter, perhaps even interconnected in ways that blur organizational boundaries. Data will grow richer, and systems more autonomous. But the essential questions will endure - how to create value, how to manage risk, and how to sustain trust. In that sense, the future of building technology is not just about intelligence embedded in systems. It is about judgment embedded in leadership. And that, Alex Molinaroli suggests, is the one capability that cannot be automated.
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