Managing a $20M+ enterprise portfolio spanning financial services, healthcare, CPG, and TMT, the challenge wasn't deployment — it was value. Customers had licensed the platform, but most struggled to connect AI capability to outcomes that showed up in the business.
Use cases were scattered, prioritization was unclear, and the path from model to measurable impact was rarely defined.
I led AI transformation strategy account by account, working directly with business and technical stakeholders to identify where AI would actually move the needle. That meant separating high-value bets from science projects, stress-testing feasibility against available data and organizational readiness, and building the business case for what to build first.
At a top-3 global investment bank, core use cases included trade data anomaly detection for risk management, automated ETF construction, and quantitative applications across fixed income and FX, including yield curve forecasting, credit spread prediction, and execution cost optimization. At a Fortune 10 pharmacy and healthcare retailer, the focus was labor optimization, forecasting staffing demand across front-of-store and pharmacy operations. At a top-3 U.S. telecom carrier, core use cases included churn prediction, next best offer, and predictive maintenance, covering customer retention and operational efficiency.
From there, I drove selected use cases through to production across predictive, generative, and agentic AI, managing the cross-functional work needed to go from proof of concept to something running in the business.
The work spanned cost reduction, revenue uplift, and risk mitigation depending on the account and use case, with individual programs generating tens of millions in annual value. In parallel, the GenAI expansion strategy created $2M+ in new pipeline, moving customers from initial platform adoption into the next generation of AI capability.