Enterprise AI at Scale
summary
SAS describes concrete deployments: a northern European tax authority lowers fraud by over 650 million pounds and raises tax revenue by 7 billion pounds using SAS analytics and AI; Georgia Pacific runs 15,000 models across 140 plants, cuts unplanned downtime ~10% and targets a 5–7% forecast gain (~$350M); HSBC sees 500% higher lending conversions and 10x faster marketing execution; Posten reports 100x query and table-join speedups. Benchmarks claim ~4x developer productivity and algorithms running ~30x (up to 326x) faster, and models are being embedded to run inside SingleStore clusters.
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chapters (11)
- 0:00Introduction and welcome
- 1:48Data landscape challenges
- 4:17Customer success stories
- 9:19SAS platform overview
- 11:07Developer tools for AI
- 15:04Pre-built AI solutions
- 16:50Models as a product
- 18:39SAS and SingleStore partnership
- 20:04Posten case study
- 22:51Performance productivity trust
- 24:13Ethical AI and trust
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