What Problem Are You Actually Solving?
Key takeaways from our SF Fintech Week Agentic Finance Summit
Plot twist: this week’s post is different. We typically stick to signals and market analysis, but after hosting an Agentic Finance Summit with 150 people actively deploying AI in financial services, we had to share what we actually learned.
What emerged was a clear pattern that contradicts much of the market noise: the companies winning in enterprise AI are the ones solving the infrastructure data problem. And most vendors don’t even know what that problem is. What follows are some key takeaways from the conversations.
What Vendors Often Get Wrong
“A lot of these vendors and especially young startups tend to focus a lot on just kind of going too broad in terms of the sort of the problems that they want to go solve for and they don’t appear to be very focused on the specific problem they want to solve.” - from a Director of AI at a large financial institution
Most vendors pitch transformation, but enterprises live in complexity. A typical financial institution has decades of acquisitions, migrations, legacy systems, and mainframes running alongside cloud infrastructure. APIs return data that looks clean but contains hidden fraud codes, legacy error messages, and fields that mean something completely different from what their names suggest.
The hard part isn’t deploying AI. It’s making sense of data quality issues that prevent AI from being useful. Vendors building for an ideal enterprise fail immediately. The ones getting traction come with a prescriptive thesis: “Here are three specific problems we solve for you in KYC or AML within wealth management.”
The Real Moat: Three Layers
Layer 1: Process Intelligence Observe how work actually happens before deploying AI to change it. Create a “digital twin” of core operations. Only then deploy agents reliably.
Layer 2: Compliance-First Architecture Design for governance from day one, not as an afterthought. When you build AI for auditability and explainability from the start, compliance becomes a foundation, not friction. You get systems where every interaction is monitored, where policy changes can be tested offline, and where you can A/B test approaches.
Layer 3: Systems Modernization Use AI to accelerate the modernization of legacy systems. Compress what normally takes years into weeks.
All three solve the same problem: you don’t deploy AI and hope. You fix infrastructure so AI can work reliably.
The Beachhead Playbook
Success requires going deep on one wedge before going wide. Start with a specific problem, build a solution that scales, and respect governance.
What Makes Pilots Succeed:
Use real production data, not sandboxes
Get to UAT environment, production-ready
Pick one use case and demonstrate mastery there
The Three Boxes to Check: Prove the tech works with their data in their environment. Do it resource-efficiently for both parties. Ensure genuine executive sponsorship—someone whose career is better if the pilot succeeds, not someone checking a box for board pressure.
The Wrong Play: Start Simple. Conventional wisdom says start with low-hanging fruit. This is backward. Proving you can pick easy problems doesn’t prove you can handle hard ones. Start with a genuinely complex process. Build infrastructure that handles complexity. Easy wins come later.
When pilots actually move into broader deployment, they’re the ones who went after legitimately difficult cases. They proved something real.
What Everyone Gets Wrong
Misconception #1: One-Shot Deployment AI deployment is iterative. Month one might be 30%, month two 60%, approaching 100% over time. If you judge on month-one performance or aim for 100% from day one, you’ll fail.
Misconception #2: Accuracy as Success The fixation on 99% accuracy stops deployments prematurely. The question should be: what did you learn? What applies elsewhere? 90% accuracy with human-in-the-loop is often good enough to go live.
Misconception #3: Agents Are Too Risky for Regulated Work Compliance teams see hallucination and drift risk. These are real. They’re not blockers, they’re design problems. With proper guardrails and monitoring, you can manage the risk.
What This Means
For founders: pick one part of the enterprise AI problem and solve it completely. Understand the messy reality your customer lives in. Build for that reality, not the ideal. Get one wedge right with real data and real stakes.
For enterprises: you’re not looking for the best AI. You’re looking for the deepest understanding of your infrastructure problem. Does this vendor understand your legacy systems? Your governance? Your actual workflows? Have they solved this for companies like yours, or are they applying a generic solution?
The vendors who deploy are the ones who understand infrastructure. Everyone else stays in the pilot sandbox.