A few weeks ago I walked through the framework for the AI transformation journey. This conversation with Lalitha Rao, Chief Risk Officer at Imprint, is the natural next step. It is a real, in-the-field look at what it actually takes when a team commits to scaling agentic AI past the theory stage.

In the nine months since her risk team started building out their agentic AI infrastructure in earnest, they’ve grown their loan book by 150%, increased revenue by 2.5x, and actually lowered loss rates on newer cohorts. All without cutting a single headcount. Lalitha’s team didn’t treat this as a cost-cutting exercise, they treated it as a way to create more value for Imprint's members and partners. And the results speak for themselves.

If you’re further along in your own AI transformation journey and want a real example of what scaling agentic AI actually looks like once the framework meets the field, this is the conversation.

What you'll hear in this episode:

  • Why Imprint's centralized AI platform, built by their CTO's team, turned out to matter more than any individual agent they've built
  • How ARIA, an autonomous credit policy agent, scours bureau and internal data to find early signs of portfolio deterioration and pushes the efficient frontier outward instead of just optimizing it
  • Why agentic AI unlocked something a SQL query or a standalone model couldn't, digesting multiple data sources in different formats into one coherent, repeatable workflow
  • How a residual mining agent continuously finds model errors, builds new features, and retrains models using out-of-time testing to guard against overfitting
  • Why Imprint never measured success by headcount reduction, and what they built instead with the team's freed-up time
  • Inside Project Shield, a suite of ten fraud agents that runs for six hours, costs about $300 in tokens, and saves hundreds of thousands of dollars in fraud losses
  • How a shared skills repository and clear data ownership let a non-engineering risk team build and maintain production-grade agents
  • Lalitha's honest build versus buy framework, and the specific vendors, including Sardine, LexisNexis, and Proof, Imprint still relies on
  • The real hiccups Imprint hit along the way, bloated proposals, false certainty in AI-generated reviews, and how they built guardrails to fix both


You should listen to this episode if you:

  • Are further along in the AI transformation journey and want a concrete look at what scaling actually requires beyond the initial framework
  • Lead a risk, fraud, or credit team and are trying to decide which agentic AI use cases to prioritize first
  • Are weighing build versus buy decisions for AI agents and want a clear mental model for where proprietary data changes the calculus
  • Worry that scaling AI means shrinking your team, and want to hear a real counterexample
  • Want practical detail on how to actually enable non-engineers to build and maintain production-grade AI agents


Rss
Podden och tillhörande omslagsbild på den här sidan tillhör Chen Zamir. Innehållet i podden är skapat av Chen Zamir och inte av, eller tillsammans med, Poddtoppen.