How CFO Advisor transforms financial analysis and storytelling with AI

Our client’s month-end revenue reports required manual data gathering, manipulating spreadsheets, driver identifying, drafting, and reviewing materials using more than five different tools. This often resulted in over 40 slide decks being created before final approval. 

These tasks regularly caused time pressure, delayed decisions, and led to inconsistent commentary between business units. Executives depended on technical teams to extract and format data, which lengthened turnaround times and increased operational risk. 

To fix these issues, the organisation wanted to standardise commentary and accelerate month-end close process. This would free finance teams to concentrate on analysing why performance changed, instead of merely reporting what happened.

We deployed CFO Advisor (or Chief Financial Officer Advisor) with two complementary capabilities:

• Auto-analysis and commentary: This codifies business logic, thresholds, and key performance indicators (KPIs) to generate variance explanations automatically, producing a structured first draft that finance teams can review and refine.

• Chat with your data: This enables natural-language questions over finance data, so users can retrieve targeted answers in real time without specialist support.

We firstly launched a four-week rapid prototyping and scoping programme to validate feasibility, set goals, and benchmark output quality. Next, we built a proof of concept (PoC) GenAI pipeline, iterated prompts and data transformations, and designed a human-in-the-loop review flow so controllers and financial planning and analysis (FP&A) could edit and approve drafts. 

With confidence established, we moved to a minimum viable product (MVP) - pilot, integrated the experience into the reporting layer, and simplified the workflow by consolidating tools.

This staged approach leveraged existing reporting structures (for speed and adoption), established consistent thresholds and drivers, and reduced manual drafting effort while increasing reporting consistency across entities. 

The process now completes in a few hours instead of several days, with report outputs consolidated into fewer pages using a single tool. Automating approximately 20 analysis types has reduced variance analysis effort by about 90%, saving roughly 10 full-time equivalents (FTE) close hours annually. 

Finance and leadership gained faster access to consistent narratives, enabling earlier intervention, and better, data-driven decisions. After initial success with revenue commentary, the platform is expanding to ad-hoc Q&A and more reporting areas. This is following a defined development roadmap on every stage, from analysis and scoping through PoC and MVP to rollout phase until taking over to maintenance.