Level AI’s Playbook: How Amazon’s Customer Obsession Principles Scale in Enterprise Software

Level AI’s Playbook: How Amazon’s Customer Obsession Principles Scale in Enterprise Software

Most enterprise software companies claim to be customer-obsessed, but few can demonstrate it in their daily operations. In a recent episode of Category Visionaries, Level AI founder Ashish Nagar revealed how his experience at Amazon shaped a truly customer-centric approach to building enterprise software.

The Amazon Education “Those two years at Amazon was one of my best business education and entrepreneurial education,” Ashish explains. During his time leading Alexa’s conversational AI initiatives, he internalized three core principles that would later shape Level AI’s approach.

Principle 1: Bias for Action “There’s a lot about bias for action is one of the key values at Amazon,” Ashish shares. “That’s essentially this idea of planning, but measuring all teams and all individuals by the impact they create and the speed at which they create that impact.” This principle transformed how Level AI approaches product development and customer service innovations.

Principle 2: Small Teams, Big Impact Amazon’s famous “two pizza team” philosophy proved invaluable for Level AI. As Ashish notes, “My small team of 15-20 people when I initially joined, was servicing a quarter of all of Alexa’s conversations around whether local search and so on.” This experience shaped Level AI’s lean approach to building enterprise solutions.

Principle 3: Customer Centricity as Decision Framework At Amazon, “An SVP or a product manager or the lowest level employee can question any decision based on customer centricity.” Level AI adopted this radical approach to customer-focused decision-making, allowing customer needs to drive product development and company strategy.

Applying Amazon’s Principles to Enterprise AI These principles shaped Level AI’s distinctive approach to the crowded AI market. Instead of chasing technology trends, they focus on enduring customer needs. As Ashish explains, “Ask yourself in your particular space what will not change in the next ten years… For Amazon, it’s like what will not change is prices. People will always want low prices.”

This focus on fundamentals helped Level AI cut through the AI hype cycle. “The Twitter noise doesn’t really impact them,” Ashish notes about their customers. “What impacts them is, again, like, can you solve my problem for which I have $100,000 budget any better than anybody else.”

Customer Obsession in Practice Level AI’s approach to analyst relations illustrates how they implement customer obsession. Rather than taking an ideological stance on working with firms like Gartner, they simply asked what customers wanted: “When our customers told us like, hey, we check out Gartner about these things, they were like, sure, if you check out Gartner, then we are in Gartner.”

This pragmatic customer focus extends to their core product strategy. While many AI companies push full automation, Level AI recognized that their customers needed augmentation more than replacement. “If I had to pick a number, somewhere between 50% to 70% of the work would still be done by humans,” Ashish predicts.

For B2B tech founders, Level AI’s story demonstrates how Amazon’s customer obsession principles can be successfully translated to enterprise software. The key lies not in blindly copying Amazon’s practices, but in understanding the underlying principles and adapting them to solve real customer problems in your specific market.