CEO Insights Magazine

Most enterprises know they need AI. The problem is rarely the technology; it’s the data underneath. Fragmented systems, unstructured data spread across emails and documents, and decades of accumulated technical debt make it nearly impossible for organizations to deploy AI solutions that actually stick. Poorna Reddy, Founder of Amotion, has built his career solving exactly this problem: bridging the gap between where enterprise data is today and where it needs to be for AI to deliver measurable results.

An IIT Madras alumnus, Poorna spent his early career in operations at Amazon and GAP, where he developed a practical understanding of how large organizations actually run. He went on to lead national expansion for “Uber for Trucks” unicorn BlackBuck.

He subsequently led operations at another logistics startup, doubling their revenue and headcount within one year that later led to startup’s acquisition in an all cash deal, then he built multiple logistics startups in the US that he grew to over $2M in revenue, raising nearly half a million dollars in capital from marquee investors along the way.

Today, through Amotion, he works with Microsoft’s enterprise channel to deliver AI solutions across India, Singapore, the Middle East, and Europe - helping organizations move from AI experimentation to production systems that their own teams can maintain and extend.

As a new-age leader, what motivates you in your daily routine?

Two things. First, we’re in a window where someone with operations and sales experience can understand and build AI solutions, identify high-value use cases and bring them to market. That wasn’t true five years ago. I work closely with my tech and research team, while also spending time with clients, understanding their pain points, and figuring out where AI can cut real cost or time and justify the ROI for enterprises.

Second, every client engagement is a puzzle. A logistics company might have their shipment data in SAP, their customer communications in Outlook, and a lot of other data in Excel sheets that three people maintain differently. Making that work together so AI can actually do something useful - that’s what gets me up in the morning. The problems are concrete, and you can measure whether you’ve solved them.

How can leaders balance innovation with responsible AI governance?

You build the guardrails before you build the AI - not after. In practice, that means role-based access control, clear data-usage policies, and deciding upfront which data the model can and cannot touch. We've implemented solutions where the AI only accesses pre-approved data sources through controlled APIs. That kind of architecture decision early on actually accelerates delivery because you're not retrofitting security after the fact or stuck in months of compliance reviews.

The other piece leaders miss: AI governance shouldn't sit with IT alone. Every AI use case needs a business owner who understands the domain and takes responsibility for how it's used. Without that, you end up with technically sound systems that nobody trusts or adopts.

What guidelines do you follow to lead your team effectively?

I hire people who don’t need to be managed. That sounds simple, but it changes everything about how you build a team. I look for people who complement what I can’t do, who take ownership of their work, and who come to me with solutions rather than questions. My role is to remove blockers and check in frequently enough to catch problems early, not to supervise work.

During my time at Amazon, I had the freedom to decide and implement the best course of action that ensured customers received their orders on time. That kind of independence to operate and perform is why Amazon is what it is today. I carry that lesson into how I ran every business and how I run Amotion today - small team, high trust, clear accountability. Everyone knows what they own, and they have the freedom to execute it their way.

What future roadmap have you envisioned for yourself for the next five years?

Enterprise data is the bottleneck. Every company I work with has the same fundamental problem: their data exists across databases, documents, emails, voice records, and messaging platforms, and none of it talks to each other. Until you fix that, your AI projects will keep stalling after the pilot phase.

My five-year focus is positioning Amotion as the company that solves this, helping enterprises standardize and structure their data, enable no-code access to large datasets, so enterprise teams can build and maintain AI solutions independently. 

What is the one piece of advice that you’d like to pass to the upcoming leaders?

The biggest blocker to AI adoption isn’t technology or data, it’s the personal willingness of leaders to change how they work. I’ve seen organizations where the tools are ready, the data is available, but leadership is comfortable with existing processes and won’t commit to transformation. Meanwhile, their competitors who adopt early are able to operate at 2x or 3x the efficiency.

My advice is straightforward: stop waiting for AI to be perfect. Start with a small, measurable use case, prove the value, and build from there. The organizations that will lead in five years are the ones making these bets now and carrying out the required data and security transformation. Today, two developers are able to build in one week what took 30 developers one month 5 years ago.

Poorna Reddy, Founder, Amotion

Poorna is an enterprise AI leader with 14 years in operations and supply chain. He has built and scaled startups across India, the US, and the Middle East, and now delivers enterprise AI solutions through Microsoft’s partner ecosystem.

Hobbies: Studying sales psychology to sharpen client strategies, traveling to reset.
Favorite Book: Animal Farm by George Orwell, Art of war by Sun Tzu.
Favorite Cuisine: South and North Indian — especially Mughlai. For international, Japanese.
Favorite Travel Destination: Georgia (the country) and Munnar in India.