India
Most enterprise AI failures are not technical. They are failures of decision ownership. As AI systems move from pilots into core workflows, the risk shifts from model accuracy to accountability, escalation design, and executive oversight. I advise Boards and C-Suites on the governance architecture behind AI adoption, clarifying: • Who owns AI-driven outcomes when decisions materially affect customers or markets • Where autonomy begins and where human intervention must remain explicit • How escalation paths operate before regulatory or reputational exposure • What accountability actually means inside complex, high-stakes environments In regulated and high-consequence contexts, AI is not just a technology shift, it is a leadership systems shift. My work focuses on designing the operating model around AI before it becomes visible in headlines. I work closely with executive teams to define escalation clarity and decision accountability before exposure forces reaction. If you are navigating AI decisions where accountability, escalation clarity, or executive exposure feels unresolved, I’m open to a discreet conversation.
1. Owned architectural and governance decisions for domain-specific GenAI systems operating under regulatory and reputational constraints. 2. Defined retrieval, escalation, and failure-handling strategies that reduced policy risk while improving response precision. 3. Advised leadership on irreversible design choices impacting trust, auditability, and long-term operating cost.
1. Led adoption decisions for GenAI-driven customer intelligence under data residency and brand-risk constraints. 2. Balanced model capability against customer trust and operational reliability in production-facing AI systems.
1. Advised senior stakeholders on analytics and AI initiatives where early technical choices materially impacted cost, adoption, and long-term maintainability. 2. Developed merchandising analytics platform, boosting sales by 15%. 3. Implemented recommendation engine, increasing cross-sell and upsell opportunities by 12%.
1. Worked on large-scale enterprise systems where analytical decisions directly impacted revenue leakage and operational risk.
1. Integrated bank transaction and consumer credit behavioral data to deliver reporting and analytic insights with focus on providing end users with affordability and credit risk underwriting strategies which which resulted in reducing money leakage by 1.7%. 2. Enriched bank transaction data and credit risk modeling, guided design and integration of bank transaction data, categorization services and affordability metrics to increase tower revenue by 2.6%.