From building side projects to exploring behavioural economics, Amit Rana brings an insatiable curiosity to his role as Syncron’s VP of Data & AI. Based in Bengaluru, he leads the global team responsible for transforming real-world aftermarket challenges into intelligent, scalable solutions. In this edition of our leadership series, we sat down with Amit to explore how data & AI is reshaping the aftermarket, and why Syncron is such a compelling place for tech talent in India.
For me, it’s all about translating strategy into execution. That might mean improving forecast accuracy, optimizing inventory, or automating pricing decisions—anything that moves the needle on performance and profitability.
What really excites me is the nature of the problems we’re solving. The aftermarket has this fascinating mix of complexity, scale, and data richness. It’s not about experimentation for its own sake—it’s about applying advanced techniques to areas like parts availability, pricing, and inventory where decisions have a direct impact on uptime, revenue, and customer trust.
When a customer shifts from reactive to proactive decision-making, and you know your work helped make that possible, that’s incredibly rewarding.
For us, value starts with the business problem, not the model.
Unless AI improves accuracy, reduces effort, or accelerates a decision, it’s just noise. When we look at a new use case, we ask simple questions: Will this improve forecast accuracy? Will it reduce manual effort? Will it speed up decision-making? If the answer isn’t a clear yes, then it’s not the right investment.
In the aftermarket especially, we focus on where intelligence can reduce cost, unlock revenue, or improve service levels. That might be better pricing strategies, faster inventory decisions, or smarter demand signals.
We’re also intentional about trust and transparency. Pricing and inventory decisions are high-stakes, so explainability matters. We’re disciplined about proving value early, protecting customer data, and avoiding technology for technology’s sake. That mindset helps us focus on what matters and deliver AI that actually works in the real world.
We’re seeing a fundamental shift. What used to be cutting-edge—predictive forecasting, failure prediction, pricing optimization—is becoming the new baseline. Today’s OEMs expect smarter, faster insights across their operations, and AI is rising to meet that demand.
At the same time, service supply chains are evolving. They’re moving from reactive firefighting to proactive planning, driven by real-time signals and better visibility across systems. OEMs and dealers want unified, cloud-based data platforms to eliminate silos and accelerate decisions. And generative AI is starting to change how people interact with information, from intelligent troubleshooting to faster parts identification to context-rich pricing recommendations.
But the real game-changer is autonomy. Imagine a system that can continuously adjust forecasts, pricing, and inventory based on what’s happening on the ground without needing human intervention at every step. That’s where we’re headed. And that’s where the biggest value will come from.
What sets us apart is how deeply we understand the aftermarket. We don’t treat AI as a generic capability; we build it around real-world constraints like long-tail parts, multi-echelon networks, and slow-moving inventory. These are complex, high-impact challenges that require a nuanced approach.
We also embed AI directly into the decision-making process. It’s not just about showing a score on a dashboard; it’s about enabling action. Whether it’s pricing, forecasting, or inventory optimization, we design our solutions to deliver intelligence right where it’s needed.
Another thing that sets us apart is how we’ve built our data platform. It’s designed to be secure, flexible and built for scale, so customers know their data is being handled responsibly and used in ways that create value, not risk. That trust is essential when you’re embedding AI into decisions that directly impact performance and profitability.
And finally, we don’t believe in a one-size-fits-all approach. We use classical machine learning, optimization techniques and GenAI together, depending on what the problem calls for. That flexibility allows us to deliver end-to-end decision automation, not just predictions.
At the end of the day, our goal is simple: turn intelligence into action and make a real difference for our customers.
Syncron is unique in combining the scale of an established enterprise with the agility of a fast-moving SaaS company. For someone in data and AI, that’s a rare and rewarding mix.
On one hand, you get to work on complex, high-value problems that matter—things like dynamic pricing, predictive parts planning, and inventory optimization. These challenges stretch your skills and have a real impact on customers across industries, from automotive to construction to industrial manufacturing.
At the same time, there’s a genuine culture of experimentation. We encourage exploring modern ML techniques, building with GenAI and pushing the boundaries of what our platform can do. And we do all this within an engineering culture that values ownership, performance and craft.
But beyond the tech, what makes Syncron special is the mission. We’re working on solutions that reduce waste, improve uptime, and extend the life of critical equipment. It’s exciting work, but also meaningful. That combination is what makes Syncron such a compelling place to build your career.
I’ve always been drawn to ideas that go beyond technology. Fields like behavioural economics, systems thinking, and product strategy help me think about problems in new ways. Books like Thinking in Systems and The Innovator’s Dilemma have all influenced how I approach complexity, decision-making, and long-term transformation. I’ve also been fortunate to learn from mentors throughout my career and I continue to draw inspiration from their perspectives and guidance.
I also love learning by doing. Whether it’s tinkering with a new ML framework, building side projects or exploring data visualizations, I try to stay hands-on and curious.
And then there are the things that help me recharge—travel, wildlife photography, music, and regular fitness. Those moments away from work help create the space I need to think creatively and return with fresh energy.
I’m a big believer in what I call “combinatorial innovation”—connecting ideas across disciplines and recombining existing concepts in thoughtful ways. Often, that’s where the most unexpected and valuable breakthroughs happen.