When Amir Kazmi describes his title of Chief Technology and Growth Officer, he does so not as a curiosity but as a thesis. “Splitting tech and growth into different functions sometimes creates artificial distance from customers,” he explained. For Kazmi, who has spent most of his career navigating the gap between what is technically possible and what is commercially adaptable, the combined role is simply good business logic made explicit.
Kazmi holds that title at Ralliant, a newly listed NYSE precision technology company built on a Danaher lineage and spanning brands like Tektronix and Qualitrol. The company operates across electrification, AI infrastructure, aerospace and defense and advanced industrial environments, serving over 90,000 customers in 90-plus countries through more than 14 million installed sensors. It is, in Kazmi’s framing, a company that sits at the intersection of digital intelligence and physical systems, enabling the world’s most critical infrastructure to operate with confidence and precision.
The Founder Mindset at Scale
Kazmi co-founded a nanotech company earlier in his career, and the experience left a permanent imprint. “When you’re the founder, you don’t get to say that’s a tech problem or a sales problem,” he noted. “Every key opportunity or area of focus becomes your problem.” Before joining Ralliant, he held senior roles at Lockheed Martin and WestRock, where he tested whether that founder urgency could survive contact with organizations measured in the billions.
His conclusion: it can, but it requires deliberate design. “Speed is usually a design choice,” he said. “In large enterprises, complexity hides in decisions, handoffs and incentives.” The answer isn’t to add AI on top of broken workflows; it’s to redesign the workflows first, then amplify them.
RBS: The Backbone That Connects Everything
Central to that redesign is the Ralliant Business System, or RBS, the company’s operational framework derived from the Danaher Business System and continuously evolved. Kazmi described it as “dozens of best-standard workflows” covering go-to-market strategy, lean manufacturing, sales process management and innovation funnel management. “RBS gives us a common language,” he said. “The technology allows us to instrument it, measure it and continuously improve it.”
The current frontier is wiring AI directly into those workflows. One practical example: Ralliant is building systems that link daily visual management, the real-time tracking of team workloads and metrics, to automated problem-solving processes. Instead of manually convening a team to diagnose why a manufacturing line or software development sprint has stalled, an agentic layer can surface potential root causes automatically. “You’re automatically moving on to the next phase,” he emphasized. “You’re getting some direction on what the potential root causes could be so you can solution it that much faster.”
AI on the Grid
On the commercial side, the integration is equally concrete. Ralliant’s Qualitrol business makes sensors and monitoring systems for global electrical grids. Recently, the company introduced AI-enabled fault detection that can pinpoint the location of a grid failure in real time, reducing a process that once took hours or days of manual inspection to minutes. “If there’s an outage on the grid, it has pretty dramatic financial impact and a ripple effect,” Kazmi observed. “The ability to localize it really fast changes the equation.”
It is a capability with compounding relevance. Data centers and AI infrastructure are driving electricity demand to levels not seen in decades. Kazmi sees Ralliant positioned on both sides of that trend, helping scale the grid and helping operate it more efficiently. “There’s scale, which is absolutely needed,” he said, “and then there’s the concept of the smartest watt, instrumentation so you can see performance in real time and lean in with precision.”
Governing the AI Frontier
As AI tools proliferate beyond technical teams, Kazmi is focused on what he calls the balance between agility and discipline. His answer is architectural rather than bureaucratic: build governed infrastructure, including AI gateway services, identity and access management and observable model inference, at the platform layer, then unleash citizen-led innovation at the edge. “Platforming non-differentiated capability so you can scale faster remains key,” he underscored. “It’s not a completely new concept, but in the AI era it remains essential.”
The next phase, in his view, is agentic workflows that trigger one another autonomously, a world where daily visual management doesn’t just display data but initiates problem-solving, and where problem-solving doesn’t wait for human scheduling. “The workflows will become more agentic,” he added. “One will trigger the other.”
What makes that possible, he argued, is not primarily the technology but culture. “Humility scales faster than confidence in the era we’re in today,” Kazmi said. “There is extreme learning agility that organizations have to go through.” For a company that has spent decades accumulating precision at scale, learning how to move fast with that precision may be the defining work of this moment.
Peter High is President of Metis Strategy, a business and IT advisory firm. He has written three bestselling books, including his latest Getting to Nimble. He also moderates the Technovation podcast series and speaks at conferences around the world. Follow him on X @PeterAHigh.
