Cultivating an AI-driven enterprise
summary
Large enterprises report 55% of Fortune 500 firms investing over $50M in AI and spending grows ~44% year over year, yet 90% of AI projects hit operational obstacles and only about 1% of production models get monitored. Successful adopters use DataRobot-style platforms, prioritize automation-first workflows, build an MLops and governance center for deployment and continuous retraining, and run workshops that surface surprisingly many opportunities (one team found 69 use cases) to focus high-value models.
AI-written summary · about
chapters (13)
- 0:00Intro
- 1:00Lindsays background
- 2:00AI adoption
- 3:00AI challenges
- 8:00Use cases
- 9:00Projects that get started
- 10:00foundational enablers
- 11:00valuefocused automation
- 12:00tied to value
- 14:00AI transformation strategy
- 15:00AI embedded into DNA
- 16:00AI enablers
- 17:00Outro
related talks
21mCutting through the hype. How enterprises can apply a strategic lens to leveraging AI to achieve ...
37mThe AI Factor: The Blueprint for Intelligent Deployment
29mAI Operationalization: Where the AI Rubber Hits the Road for the Enterprise
50mRealities & Misconceptions about Enterprise Applications of AI/ML and Data
22mHow to operationalize AI
10mCan great teams and company culture outperform great AI technology?
21mCreating an AI Culture in the Enterprise
19mScaling & Industrialization of AI