Deepsha Menghani

Deepsha Menghani is Director of AI at Sprout Social, where she works on enterprise AI strategy, intelligent systems, and agentic AI for social intelligence. Her career combines product strategy, applied data science, and the organizational challenge of turning experimental AI into dependable software.

Menghani studied electrical and electronics engineering at the Birla Institute of Technology and Science, Pilani, and earned an MBA from the University of Virginia’s Darden School of Business in 2016. She spent approximately eight years at Microsoft, moving from product marketing and customer analytics into applied science. Her work included Azure Databricks, anomaly detection, recommendation models for low-code products, and frameworks for evaluating business investments.

She also created open-source Quarto workshop materials and a Bigfoot-themed Shiny application, using approachable examples to teach practitioners how to communicate and publish technical work. Her subsequent writing explored retrieval-augmented generation with local vector storage and how document-chunking strategies affect retrieval quality.

After Microsoft, Menghani joined Sprout Social. She also teaches AI transformation leadership alongside Rossella Blatt Vital. Their shared approach centers on several practical ideas:

• AI-first product transformation: Redesign customer experiences around connected workflows while continuing to deliver the existing roadmap. Their AI Engineer World’s Fair session used Woofwell, an illustrative puppy-health application, to show how separate features can contribute to one coherent answer.
• Learning-focused MVPs: Treat prototypes as instruments for testing assumptions and reducing uncertainty, and evaluate internal processes by whether they improve decisions and unblock teams.
• Organization-wide AI fluency: Combine technically deep, cross-functional builders with self-service capabilities that help product and engineering teams experiment without relying exclusively on AI specialists.
• Human-centered AI adoption: Preserve usability, reliability, trust, customer needs, and human judgment as software organizations incorporate increasingly capable AI systems.