Why AI-Native Commerce Will Kill Legacy Retail Systems | Ankush Goyal on Building Fabric

Key Takeaways

From Delhi to Purdue: Learning Under Constraints

Growing up in 1990s Delhi without computers or internet, Ankush developed resourcefulness and first-principle thinking. At Purdue, his computer engineering degree gave him empathy for systems design and the fundamentals of how machines work, skills he would carry throughout his career.

Orbitz and the Lessons of Scale

At Orbitz, Ankush learned what it meant to build and operate at global scale. From funnel optimization to early machine learning deployments, the experience showed him how data and scale shape consumer experiences and technology decisions.

RelateIQ and the Birth of AI-Native CRM

Joining RelateIQ in 2014, Ankush helped build an AI-native CRM long before the term was popular. By automating workflows instead of layering on rules, the company challenged traditional CRMs and was eventually acquired by Salesforce, where its technology still powers AgentForce today.

Narvar and the Realities of Commerce

As head of R&D at Narvar, Ankush spent six years building post-purchase platforms used by the world’s largest retailers. He saw firsthand the razor-thin margins, the complexity of fulfillment and returns, and the opportunities for AI to make commerce more efficient and customer-centric.

Fabric and the End of Bolt-On AI

At Fabric, Ankush is pioneering an AI-native commerce operating system. He argues that legacy platforms relying on static rules or “chatbox add-ons” are relics, and that the future belongs to systems that dynamically learn, adapt, and act in real-time against business goals.

The Convergence of Product and Technology Leadership

Ankush believes the old division between product and engineering is collapsing. In an AI-first world, leaders must blend technical depth with product vision to build systems that not only work but win in the market.

Broad T Skills and the Future of Engineers

According to Ankush, coders alone won’t thrive in the AI era. Engineers must become “broad T” deep in one specialty but fluent across customer needs, business models, and product design. It’s this breadth, coupled with adaptability, that will define future leaders.

Advice for the Next Generation

His message is blunt: become AI-native or be left behind. For young engineers and professionals, Ankush emphasizes experimenting across AI models, writing evals, and cultivating broad skills alongside deep expertise. Adaptability, he insists, is the only constant.

Listen on your Favorite Platform