A short course covering the vocabulary, risks and controls you need before your first pilot.
Key takeaways
Before evaluating any vendor, it helps to understand the difference between a tool that classifies or extracts information, one that drafts text for review, and one that makes an autonomous decision. Almost everything worth using in a regulated institution falls into the first two categories.
This lesson gives compliance and operations leaders a shared, plain-language vocabulary so vendor conversations don't get lost in marketing terms like 'agentic' or 'autonomous', which mean very different things depending on who is using them.
The risk in most AI deployments isn't the model getting something wrong occasionally; every process does that. It's the absence of a clear process for catching and correcting errors before they reach a customer or a filing.
This lesson walks through common failure points, missing review steps, unclear accountability, and vendor contracts that don't specify data handling, so leaders know what to ask for before signing anything.
Financial institutions need to know where client data goes once it enters a tool, how long it's retained, and whether it's used to train models beyond your own use. These are contractual questions, not technical mysteries.
This lesson gives a short checklist to bring into any vendor negotiation, so data handling terms are settled before a pilot starts, not renegotiated after a concern comes up.
The final lessons cover how to design a proportionate control environment, sign-off checkpoints, logging, escalation paths, and how to scope a first pilot narrow enough to evaluate properly within a quarter.
By the end, leaders leave with a one-page framework they can apply to any AI proposal that lands on their desk, rather than evaluating every vendor pitch from scratch.
A 30-minute call is enough to tell you whether AI pays for itself here.
