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Your CEO saw the agentic analytics demo. Now someone has to make it work.
Leadership wants an AI agent answering every data question, and the CFO is already counting the analyst headcount it will save. You're the one who has to explain what the demo left out.
Ross Katz, Principal and Data Science Lead at CorrDyn, argues that an AI agent does an analyst's job without knowing what actually matters to your business. So the real work moves into your semantic layer and context, plus the feedback loop that keeps them accurate. You'll leave with a way to scope a first prototype, the questions to put to any vendor, and a straight answer for the CFO.
Jason Bradwell puts the questions to Ross on what agentic analytics is, how the vendors stack up, and where lock-in hides in your data stack. This one is for heads of data and analytics weighing build vs buy as AI disruption reaches BI. If you want a ranked vendor list, look elsewhere.
Key Takeaways
- Your semantic and context layers decay over time. Anthropic hit this building its own agentic analytics system, and it changes the headcount math.
- Ross has one vendor category he'd steer clear of, and one type of company where he'd say go for it anyway.
- Keep your documentation in version control and you keep the option to migrate later, even inside a platform built to hold on to you.
- A $10,000-a-day AI bill is believable. The bigger question is how nobody caught it sooner.
Chapter Markers
00:00 Why agentic analytics is everywhere right now
01:29 Defining agentic analytics in plain terms
04:03 From Excel exports to self-serve BI
08:09 Is the agent really an AI analyst?
10:46 Why humans still curate what matters
13:36 Pushing back on an all-in executive
16:23 The semantic layer and the feedback loop
22:36 Data as an argument about priorities
23:25 Vendor tour: hyperscalers and data warehouses
28:16 Keeping your options open on lock-in
29:42 BI tools with AI built in
30:37 The trouble with all-in-one platforms
34:32 The questions to ask any vendor
35:45 When all-in-one does make sense
39:02 What to tell the CFO about headcount
43:56 What We're Watching: the $10,000-a-day AI bill
47:26 Guardrails for token spend
49:51 Wrap-up
Useful Links & Resources
- Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
- Ross Katz on X: https://x.com/brosskatz
- Platforms mentioned: Snowflake Cortex, Databricks Genie, BigQuery, Amazon Bedrock, Microsoft Fabric, MotherDuck, Looker, Omni, Rill, dbt
- More from CorrDyn: https://corrdyn.com
- Related show: Data in Biotech, also from CorrDyn
Connect With the Show
- CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/
- Website: https://corrdyn.com
- Listen on Apple Podcasts and Spotify
Has your company put an agent in front of its data yet? Tell us what broke first, or which vendor question you wish you'd asked before signing. And if you think Ross is wrong about all-in-one platforms, make the case in the comments.
If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com.
#EventualConsistency #AgenticAnalytics #DataEngineering #SemanticLayer #CorrDyn
