Six months ago, the board approved "building an AI team." They hired two engineers, provisioned GPUs, started building. Today they have a prototype that works on Tuesdays, a roadmap that only grows, and a fixed cost that never stops. And across the street, the competitor who chose to buy is already serving clients. The "build it in-house" math looks good on the slide and rarely closes in practice. Not because building an agent is impossible — models are accessible. It's tha

Leave an AI agent loose, with no brakes, and it will "help." It fires the same message to the entire book on a Sunday. It promises a client something it shouldn't. It executes an action no one asked for — and no one sees it until Monday morning, when the damage is already done. The agent wasn't broken. It was doing what agents do: acting. Just with no one having defined how far. That's the real bottleneck of agentic AI in finance — and it isn't the model. Agents are scaling

A generic AI assistant looked at your client and recommended, with total confidence, a product he already holds — and that doesn't fit his profile. The sentence came out flawless, ready to copy and paste. And it was wrong. The problem wasn't the model. Models have gotten absurdly good. The problem is it knew nothing about that client. It didn't know your brokerage's book, the order history, the suitability profile, or the rule your desk follows. A generic model answers about