At Motive, we’re building AI that doesn’t just live in the cloud—it delivers in the real world. Our technology powers fleets, logistics teams, and industrial operations, helping them stay safer, run more efficiently, and grow more sophisticated every day. At the center of that effort is Gautam Kunapuli, Director of AI, who leads a global team creating some of the most advanced and practical AI in the industry.
We sat down with Gautam to learn more about what makes Motive’s approach to AI different, why accuracy isn’t just a goal but a mindset, and where he sees AI heading in the future of physical operations.
Q: Tell us about your role at Motive.
I’m the Director of AI at Motive, where I lead a global team of wildly talented engineers building AI for the Physical Economy. I’ve been at Motive for three years, and in that time, I’ve focused on building AI that sees, understands, and acts in the real world. Not just your typical cloud-bound AI—ours is embedded, rugged, and road-tested.
We work across the full AI stack, from research to deployment. Our team develops intelligent systems that power Motive’s products with computer vision, telematics, machine learning, and generative AI. It’s AI that doesn’t just recognize patterns—it makes fleets safer, operations more efficient, and businesses more profitable.
Q: How is Motive’s approach to AI different from others in the industry?
At Motive, our approach is full-stack: people, processes, and pipelines. Most companies optimize for the obvious, the middle of the bell curve. We focus on the long tail—the rare, high-impact, high-ambiguity edge cases that truly affect safety and performance.
We obsess over these scenarios: a distracted glance, a foggy intersection, a near-miss no one else caught. Our datasets are curated with surgical precision, our models are multi-tasking, and our evaluation pipelines are built to withstand real-world chaos. Why? Because in safety, the middle of the curve won’t kill you. The edge cases will.
Q: What are the industry’s biggest misconceptions about AI?
One major misconception is that AI is some kind of surveillance tool. In reality, our AI is more like a Guardian Angel with a dashcam. Privacy matters deeply to us, but safety comes first. Our systems are designed to flag risks before they become disasters, and to earn the trust of drivers through accuracy and consistency.
Drivers who were skeptical at first come to rely on our AI like a co-pilot. Trust is earned, not assumed—and our job is to deliver that through rock-solid, reliable AI.
Q: How has your view of AI evolved in recent years?
We’re in the middle of a revolution in AI. With the rise of generative and agentic AI, we can now simulate rare edge cases and train our models more effectively than ever before.
We’re also using AI to transform fleet operations, not just driver safety. Our tools like AI Assistant, AI Analytics, and AI Coach provide real-time insights, coaching, and trend analysis to empower both drivers and back-office teams. And we’re just getting started.
Q: How do we make our AI more accurate than others in the industry?
Accuracy is our obsession. We curate datasets rigorously, audit edge cases thoroughly, and test models relentlessly before they ever go live. It’s a team sport: engineers, scientists, and domain experts all working together to ensure our AI is precise, actionable, and trusted.
Every false positive we prevent is a distraction avoided. Every true positive we detect is a risk mitigated. We don’t settle for “good enough”—we aim for excellence because lives are on the line.
Q: What advice would you give to those evaluating AI for physical operations?
Don’t just look for features—look for trust. The best AI is tested in real-world environments, not only in the lab. It should deliver accurate detections and insights that teams rely on.
The right AI goes beyond safety. It improves operations, reduces overhead, and boosts profitability. It’s not just a tool—it’s a partner.
Q: What does the future of AI in physical operations look like?
We’re especially excited about Agentic AI and Physical AI. Think of decision-making teammates that take real action: dispatching crews, generating coaching plans, and optimizing job site operations.
Physical AI goes one step further—systems that not only detect risks but respond in real-time. Imagine warehouse AI that adjusts lighting and traffic flow based on forklift movement, or safety systems that auto-correct dangerous behaviors on construction sites. AI won’t replace people, but it will superpower them.
Q: Why is AI especially useful for enterprises and large businesses?
Because scale amplifies complexity. More vehicles, more routes, more variables. AI thrives here—identifying patterns, automating decisions, and standardizing performance across large organizations.
For enterprises, AI turns noise into insight, chaos into clarity, and complexity into competitive advantage.
Interested in building AI that makes a real-world difference? Join our team and help us shape the future of safety and operations: gomotive.com/careers
