Most organizations are sitting on fragmented data from dozens of sources with no fast way to normalize it, query it, or get answers. MERGE had the same problem — and what they built to fix it internally became a product their clients needed too.

In this conversation, Jason Dittmer, SVP of TechOps at MERGE, explains how his team built an automated pipeline on Google Cloud (BigQuery, Looker, Gemini, Google SecOps) to solve their own data fragmentation problem — cutting normalization time from weeks to minutes. Then they heard the same pain from clients and shipped it as a marketplace product. One healthcare client now runs 30 disparate data sources through the same pipeline. If you're thinking about how to productize your own internal AI work, this breakdown of moving from efficiency to revenue is worth reading alongside the episode: https://www.insight.com/en_US/content-and-resources/blog/from-efficiency-to-revenue-productizing-enterprise-ai.html

Jason also breaks down MERGE's "Drink Your Own Champagne" philosophy — the idea that you should prove a solution internally before ever bringing it to a client. He shares what it takes to move past the pilot phase, and how MERGE's Humanity Suite puts the human factor at the center of AI-powered marketing. If you're still sorting out what AI agents can actually do in this context, the AI agent cheat sheet is a good companion: https://www.insight.com/en_US/content-and-resources/guide/the-ai-agent-cheat-sheet.html

This is the second episode in a series on organizations building AI solutions from the inside out. In the first, Joseph Schultz at JE Dunn Construction explains how field workers with no coding background are building their own AI-powered tools: https://youtu.be/rf8MyG22FnA?si=bs2jyJ2u_1_PO66N

Book an Insight AI Readiness and Governance Workshop because you'll get a clear framework for moving your AI projects from pilot to production: https://www.insight.com/en_US/content-and-resources/solution-briefs/ai-adoption-with-ai-readiness-governance-workshop.html

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Chapters (5–12)

00:00 — Welcome and introduction

01:32 — What MERGE does and what's on Jason's desk

03:17 — Drink Your Own Champagne explained

05:06 — The internal data problem that started it all

08:27 — What made this solvable on Google Cloud

10:50 — Three months from idea to internal product

12:14 — Surprising insights from contextually aware data

13:13 — The Humanity Suite and infinite individualism

16:07 — Could this have happened a year ago

17:30 — How AI perception changed inside MERGE

18:30 — What's next for MERGE

19:22 — Advice for leaders stuck in the pilot phase #AIDataPipeline #GoogleCloud #EnterpriseAI #AIPilotToProduction #InsightOn