AI Operationalization: Where the AI Rubber Hits the Road for the Enterprise
summary
Non-digital-native enterprises report concrete progress and remaining friction: Clorox runs one model in production (a marketing forecasting model) while Palo Alto Networks has several live models for support, marketing and sales, and J&J splits roughly half/half between deployed models and backlog. Teams migrate infrastructure from on‑prem to cloud, expose models via APIs, battle manual refresh mode maintenance, and build a reinforcement‑learning style recommender that collects click/feedback for continuous improvement. 2021 priorities are systematic productionization, reliability, debug tooling and reusable templates to solve the last mile.
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chapters (7)
- 0:00Introduction and panel
- 1:25AI in legacy companies
- 6:23Current AI maturity levels
- 9:37Challenges in productionization
- 13:13People process technology
- 18:262021 priorities and scaling
- 21:58Closing remarks and Q&A
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