Generative AI in finance: what actually ships.

The production line, drawn honestly

Strip the hype and generative AI does three things well in finance: it reads documents at scale, it drafts structured output from them, and it checks work when you make two independent passes agree. Every production use case is a combination of those three: diligence is read plus check, commentary is read plus draft, cash application is read plus check plus post-with-approval.

What it doesn't do is judgment. No production system we've shipped decides anything: whether the addback is legitimate, whether the variance matters, whether the deal closes. The systems compress the hours between raw documents and a decision-ready draft, and leave the decision where it belongs.

Numbers from the field

From our own builds: a diligence workflow cut from 3 months to 2 weeks. 40 hours of monthly reconciliation removed. 80 percent of incoming checks auto-matched with dual-model verification, ties exact to the cent. These are shipped systems, not projections.

The industry-level numbers explain the gap between hype and results. McKinsey found 78 percent of organizations use AI somewhere but only around 1 percent describe the rollout as mature. Gartner projects over 40 percent of agentic AI projects canceled by 2027 on cost and unclear value. The difference between those cancellations and the builds that stick is boring: one measurable workflow, a verification layer, and an owner.

What generative AI consulting for finance covers, week by week

Most generative AI consulting in finance sells the map and leaves before the territory. You get a use-case matrix, a maturity model, a phased roadmap, and a pilot that nobody owns in month four. We work the other way around: the deliverable is a running system, and the strategy work is whatever is needed to pick which system goes first.

Week one is the audit. We sit with the people doing the work and time the actual workflows: the close checklist, the diligence request list, the underwriting file review, the report someone rebuilds every month. Each candidate gets two numbers, hours per month and error exposure, and we rank by payback rather than by how impressive the demo looks. Some candidates get ruled out here, and saying so early is cheaper for you than a pilot that dies quietly.

Weeks two through six are the build. A finance system earns trust through its verification layer, so that gets built alongside the feature, not after it. Extracted numbers carry a link to the source page. Computed figures get re-derived by an independent pass, and anything that fails to reconcile blocks with a flag instead of publishing. Anywhere money or an external message leaves the building, a person approves it, and that stays until measured error rates earn autonomy.

Handover is the part worth checking on any consultant you talk to. You get the repository, the prompts, the evaluation set, and a working session with whoever will run it. Renting your own close process back from an adviser is a bad trade for a finance team that plans to keep operating.

One honest limit. This works when the workflow is document-heavy and the before state is measurable in hours. It does not work on the judgment calls: whether the addback is legitimate, whether the covenant breach matters, whether to fund the deal. If your bottleneck is judgment rather than assembly, we'll tell you on the first call, and you can test any of this for free from our library before a contract exists.