Jonathan Hansing, co-founder of Wallabi, joins Stu Willson to unpack what it actually takes for a mid-market company to move from AI experimentation to AI execution. Drawing on his background in Army intelligence, Salesforce/Tableau product leadership, and two-plus years of AI transformation work with CEOs and CIOs, Jonathan covers why "sprinkling AI" is simultaneously a signal of opportunity and a warning sign, how to assess whether a leadership team has the appetite for real change, and what separates companies that use AI from companies that are genuinely AI-native. He also walks through a detailed case study: a media and events company whose initial request for an email automation workflow revealed — layer by layer — a need for full data architecture overhaul, ultimately delivered in 8 weeks using Snowflake, dbt, Claude Code, and the Wallabi platform.

Chapters:
00:00 – Intro
00:37 – What Jonathan hopes listeners take away: 10,000 prompts is the new 10,000 hours
02:16 – Who is Jonathan Hansing / what is Wallabi
04:00 – The Wallabi platform: forward-deployed coaches backed by AI
06:30 – A machine learning case study: 18-month problem solved in a week
07:03 – The journey: West Point → Army → Narrative Science → Salesforce → Wallabi
10:08 – "Sprinkling AI": green flag and red flag
12:35 – How Wallabi assesses appetite: the 2-week sprint approach
17:03 – Data philosophy: use case first, architecture second
18:20 – What's happening beneath the surface for pressured CEOs
20:48 – The J-curve: from 20 years to 2 years
25:03 – Dual Transformation: managing two change curves at once
25:49 – AI-native vs. companies that use AI
28:00 – Case study: media & events company — the layered problem
33:09 – What they built on top: Snowflake Intelligence, Tableau, reverse ETL
34:15 – Claude Code and context management: shared learning across repos
36:04 – The ROI of AI is the ROI of WiFi
38:09 – The bifurcation: who becomes unstoppable vs. who plateaus
41:03 – One thing to remember: 10,000 prompts is the new 10,000 hours

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