Former Johnson Controls CEO Alex Molinaroli for leadership reset to cross the AI moat
In an era where AI is rewriting the rules of business, former leaders from pre-AI landscape defined by complex supply chains, legacy engineering and capital-intensive growth may seem an anachronism.
But not Alex Molinaroli, former CEO of Johnson Controls, who has made himself relevant in his post-retirement years by investing his time, energy and money in the education of future tech leaders. With unflinching clarity, he reflects how scalable AI could supercharge supply chains, demand planning, and scenario modeling, turning global disruptions into unbreakable competitive moats. For today’s business leaders, his insight and hindsight offers a masterclass in retrospective strategy — a blueprint for building antifragile institutions.
As industrial giants race to digitize, Molinaroli stresses that AI isn’t just technology; it’s a profound leadership reset demanding new talent, governance, and vision.
Though AI was merely emerging during his tenure leading Johnson Controls, Molinaroli imagines what he could have done differently. “It’s hard to say definitively, but I see the opportunity in three lanes. First, I would capitalize on getting AI tools into our employees’ hands quickly, using our data to challenge them to be more effective and productive in their jobs and domains — clearly optimizing workflows. Second, I would explore embedding AI applications into our products and services, making them more capable and valuable for customers. Third, I would likely halt or pause all enterprise-wide initiatives until I fully understood AI’s disruption, reimagining the opportunities,” he says.
Asked whether he would have created a Chief AI Officer role early or embedded AI capability inside every business unit, Molinaroli says both would be equally important.
“I would also separate internal AI use from customer-focused activities like productization. Although the technology platform would be similar, the applications would serve unique purposes — for instance, internal tools might focus on streamlining engineering workflows, while customer-facing ones enhance building management systems with real-time predictive capabilities,” he reasons.
Capital allocation would follow suit, transformed by pointed questions to his teams: How deeply is AI woven into every future product, making them smarter and more tailored through predictive analytics and machine learning? “I’m not sure I have the full answer from where I sit today, but capabilities like predictive analytics and machine learning are logical extensions for Johnson Controls’ products. I’m certain AI tools would make demand planning and operational execution more precise, enabling robust scenario planning to best assure capital execution,” he says.
Reflecting on where AI would have delivered the most immediate efficiency gains for Johnson Controls, which operates in industrial systems, energy, and building technologies, Molinaroli reasons that as a systems company, AI would help the company’s products and services create value for customers’ enterprises, supporting their effectiveness and efficiency. Yet Molinaroli warns of some risks to large incumbent companies like Johnson Controls: “There’s a real risk of commoditizing offerings. Access to worldwide facility data and easy algorithm creation will empower owners and less capable providers to disintermediate established market leaders.”
Asked how AI-driven data analytics would have reshaped supply chain management during global disruptions, Molinaroli says, “I would assume the ability to better anticipate disruptions and preplan — scenario planning would be much less costly, more robust and current. For instance, during events like the pandemic or chip shortages, AI could ingest real-time global data on shipping routes, supplier risks, and demand signals to simulate multiple futures, allowing us to pivot inventory and sourcing proactively rather than reactively.”
He believes organizations will flatten over time. Project teams will gain significant capabilities for execution and communication, completing projects faster and boosting employee throughput and effectiveness. “Oversight might initially rise to guide ethical AI use and integration, but ultimately diminish as empowered teams self-manage with AI-assisted decision-making,” Molinaroli says.
On the type of talent he would have hired differently, Molinaroli says there would be an even higher value for individuals with strong critical thinking skills. “Solving complex engineering problems or writing code will become commodities. Determining the right questions to ask, coupled with strong interpersonal skills and high EQ, will be premium — think hires who can frame strategic problems, collaborate across silos and lead humans alongside machines,” he says.
He admits that AI would force restructuring of jobs and some jobs will disappear, like coding or basic analytics, and new ones will emerge, such as AI prompt engineers or hybrid human-AI system designers. “Retraining would focus on upskilling for oversight, ethics and creative problem-solving, with restructuring into agile, cross-functional pods blending engineers, data scientists and domain experts,” he adds.
Asked if AI had been embedded deeply during his tenure, how differently might Johnson Controls be positioned today in the smart buildings and energy optimization space, Alex Molinaroli says Johnson Controls has a deep dataset from decades with millions of customers and facilities, and AI accessing this data would be invaluable and a true differentiator. “Each customer engagement would bring the totality of those experiences to every problem, actualizing a powerful brand promise,” he says.
Claiming that AI will accelerate the transition toward increasingly sustainability-driven solutions, Molinaroli’s vision isn’t nostalgia — it’s a call to action. In racing to cross the AI moat, he believes leaders must reset not just tech stacks, but entire playbooks.
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This story was originally published March 10, 2026 at 11:43 AM.