From Pilot to Profit: Capturing Tangible Value with Generative AI in Finance

Gartner’s survey of finance leaders found 59% of finance functions were using AI in 2025. The difference between demos and durable value is whether Gen AI is embedded into governed workflows that produce repeatable outcomes, evidence, and controls.

We are seeing measurable value from operational finance controls that increase control coverage and effectiveness without requiring a proportional increase in headcount.

The myth: Automate the journal posting and the control problem goes away.

The reality: As processes digitise, control risk concentrates in fewer, higher leverage steps. Gen AI does not remove the need for controls. It can, however, make controls more effective, more testable, and less labour intensive.

Many finance teams do the work twice: once to operate the control, then again to evidence it for audit. Gen AI can reduce this duplication by generating consistent control packs as the control runs.

Examples:
• Automated complex control execution where Gen AI considers management assertion based testing of journal evidence presented.
• Sign-off narratives that reference the actual supporting artefacts and checks that were performed.

The objective is not simply to make audits easier. It is to shorten the close, reduce late adjustments, and strengthen reliance on management information.

Second, we are seeing measurable value in redesigning invoice processing by replacing legacy, exception heavy workflows with a simpler Gen AI enabled operating model.

The myth: "We have OCR; our invoice processing is automated"

The reality: Legacy OCR is brittle. It breaks when a supplier changes a font or a multi-entity tax code gets complex. This creates an "Exception Tax"—manual work that scales linearly with volume.

Forrester has explicitly framed this space as an evolving market where AI driven automation is reshaping accounts payable. The point is not vendor selection. The point is that the centre of gravity is shifting from happy path processing to better handling of exceptions and edge cases.

Examples observed:

• Ambiguity Resolution: Move from simplistic 'PO is missing' flag towards suggesting of the likely GL code based on historical intent and procurement policy.
• Less frictionless in approvals: Moving away from "Please see attached." AI generates a 2-sentence message: "This is £4k over budget because of X; approving this now maintains our supplier discount."

Thirdly, we observe the transition where finance teams confidently move from data aggregators to business insight generators.

Myth: "A dashboard with red and green arrows is real-time insight"

Reality: Data is cheap; synthesis is expensive. A 10% variance isn't an insight, it is a flag for more work is required. The true bottleneck in most finance functions isn't identifying the variance; it’s the three days of data-chasing and budget-holder conversations required to explain it, only to produce a generic "timing issue" comment by Friday afternoon.

Instead of teams spending the first week of the month manually stitching spreadsheets, Gen AI acts as a reasoning layer across the ERP, CRM, and external market feeds. It doesn’t just report that margins are under pressure, but it creates a plausible narrative connecting a spike in logistics costs to a specific regional strike while simultaneously factoring in the sales pipeline's ability to offset the hit.

How it actually works: This isn't just a chatbot sitting on a spreadsheet. The example use case deployed a RAG architecture that securely connects to structured financial data alongside a connector to unstructured context (email and market news). The AI checks the ERP to pull actuals, queries the CRM for the pipeline, and cross-references to the latest industry news report.

The monthly performance review is no longer a fact-finding mission or a defensive exercise in data validation. It becomes what it was intended to be, a decision-making forum where the narrative is already established.

Reach out to the Klarus team for a conversation on how you can better leverage AI in finance.