Super Data Science: ML & AI Podcast with Jon Krohn
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Your snips
[02:31] Modular Platform Enables Rapid Customization
[03:31] Start With ROI And KPIs
[06:04] Include Services In Predictable Pricing
[10:03] Fortune 10 Observability Transformation
[12:41] One Generic Block Scales Across Use Cases
[13:52] Keep Founders Close To Customers
[17:23] POC Failure Stems From Missing Business Focus
[19:07] Pick Use Cases By Adoption And Data Access
[20:34] Adopt A Hybrid Build-Buy Strategy
[23:39] Prove Value Fast With Mimicked Integrations
[25:05] Set Concrete Evaluation Benchmarks
What I learned from ๐ฃ๐ผ๐ฑ๐ฐ๐ฎ๐๐ Today ๐๏ธ
I just finished listening to:
๐ฃ๐ผ๐ฑ๐ฐ๐ฎ๐๐: Super Data Science: ML & AI Podcast with Jon Krohn
๐๐ฝ๐ถ๐๐ผ๐ฑ๐ฒ: Should You Build or Buy Your AI Solution? With Larissa Schneider
๐๐ฒ๐ ๐ง๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐
Most AI POCs fail because teams focus on the tech instead of the business outcome. Before you build or buy, define what success looks like: adoption rate, cost savings, response time. If you can't measure it, you can't manage it.
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Companies are sitting on backlogs of hundreds of AI use cases but lack the capacity to execute. A hybrid build-buy strategy lets you move fast on routine tasks while protecting IP on what differentiates you.
๐ฅ๐ฒ๐ณ๐น๐ฒ๐ฐ๐๐ถ๐ผ๐ป ๐ง
This hit home for me. I've seen teams spend months on POCs that never ship because no one agreed upfront what good looks like. Now I ask: what metric moves if this works? If the answer is vague, we're not ready to build.
To get the full insight, check out the podcast!
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