Agentic AI

Led a 0-to-1 subscription, advertising, and engagement demand forecasting data product. When you’re streaming 50k+ events per year, you cannot possibly forecast demand without an automated, scalable data infrastructure and continually improving ML-based forecasting models. You also should be able to dynamically compare alternative portfolio scenarios, such as decisions to add a new rights contract, to optimize revenue and profitability. In our second iteration after our initial Streamlit application, I led the development of a Sigma-based AI and data application that projects signups, subscribers, subscription and ad revenue, and engagement at the contract level across all of our sports verticals. The underlying Python forecasting engine uses a cohort-based retention framework, historical plan mix, and viewership and live minutes 1P data to allocate revenue across signup, engagement, inactive, and dormant components. Ad revenue layers in based on forecasted engagement and inventory monetization, producing contract-level base forecasts that can also be compared dynamically against alternative portfolio scenarios. ...

With all of the investment in and implementation of enterprise AI, one would think that AI should be transformative for so many aspects of work. AI will drive higher revenue, increase efficiency, and improve productivity while transforming the toil of labor into a more enjoyable existence for knowledge workers. However, as has become common knowledge at this point, despite the seeming panacea that AI transformation is touted to be, the majority of businesses continue to not see a return on their AI investments. ...