Mo Bhasin

Mo Bhasin is an enterprise AI product leader and co-founder of OutoftheBlue.ai, an anomaly-detection startup. A former Google data scientist, he builds systems that turn large collections of business documents and organizational knowledge into measurable improvements in professional-services work.

Originally from Mumbai, Mohit “Mo” Bhasin studied mechanical engineering at Texas A&M and materials science and engineering at the University of California, Berkeley, before earning an MBA from the University of Chicago Booth School of Business in 2015. At Google, he worked in data science and product analytics; his writing about moving from business school into data science emphasizes defining useful business questions, selecting meaningful metrics, and communicating findings. He subsequently co-founded OutoftheBlue.ai with Gurudev Karanth, developing technology for anomaly detection. A University of Chicago profile traces that transition from Google to entrepreneurship.

By 2025, Bhasin was director of AI products at AlixPartners, where he co-led development of an internal generative-AI platform for consulting teams. His work included classifying company and vendor records, retrieving information from extensive document collections, and connecting language models to licensed external data. He also co-authored an AlixPartners analysis of AI in private-equity due diligence that pairs advanced models with expert judgment.

Defining technical convictions

• Enterprise productivity over individual convenience. Bhasin distinguishes making one employee faster from improving organizational performance. He measures deployments through adoption, return on investment, and user satisfaction, and uses regular demonstrations and business-team partnerships to build sustained trust.

• Business-aware text classification. He combines structured model outputs, North American Industry Classification System taxonomies, and web-connected tools to categorize unfamiliar companies and vendors. Domain specialists validate the results because probabilistic outputs require business context and accuracy checks.

• Retrieval as enterprise infrastructure. His approach to retrieval-augmented generation spans presentations, spreadsheets, documents, and proprietary databases. Teaching models to call licensed data-provider APIs can remove organizational bottlenecks, while conventional retrieval still cannot automatically reason across every document in a large collection.

• Reliable components before ambitious agents. Bhasin prioritizes dependable classification, retrieval, and tool-use steps before combining them into autonomous workflows. His AI Engineer World’s Fair session with Kevin Madura grounds that position in consulting environments where validation, organizational cooperation, and measurable outcomes determine whether AI earns lasting adoption.