Where AI actually helps in finance today
Adoption is real but uneven. In Gartner’s 2025 survey, 59% of finance functions said they use AI, with knowledge management, accounts payable automation and error or anomaly detection as the top uses — yet most reported only low or moderate impact so far. The gap is rarely the tool. It is picking the right workflows and wiring them into the close properly.
- Month-end close — drafting accruals and prepaid schedules, flux commentary and close checklists for a human to review.
- Payables and receivables — reading invoices, coding them to the chart of accounts, matching to POs and chasing overdue customers.
- Reconciliations — bank, payment-gateway and intercompany matching, with exceptions surfaced instead of buried.
- GST — matching purchase records to supplier data before accepting or rejecting invoices in the GST Invoice Management System, so input tax credit isn’t lost.
- FP&A — driver-based forecasts, scenario runs and variance explanations in plain English.
- Board and investor reporting — first drafts of the monthly investor update from your MIS.
- Anomaly checks — duplicate payments, unusual vendors, out-of-pattern journal entries.
What we do
Finance AI review
Map your close, AP/AR, tax and reporting workflows, your data and your tools. You get a ranked list of where AI saves the most time at the least risk.
Build & pilot
Set up two or three workflows on the tools you already use — Zoho Books, Tally, Xero, QuickBooks, Sheets — with review steps built in.
Run it for you
Fold the workflows into an ongoing Virtual CFO engagement: AI does the first pass, a CA reviews and signs off.
CFO support for AI startups
Durgesh has been Finance Head / Virtual CFO at Stimuler, an AI-driven EdTech company, since 2022, and led its USD 3.75M Pre-Series A end-to-end. AI companies have finance questions of their own:
- Gross margin with compute in it — tracking model and cloud costs per customer or per usage unit, so pricing keeps up with inference costs.
- Usage-based pricing and revenue recognition for credits, tiers and enterprise pilots.
- Capitalising development costs — under Ind AS 38, research is always expensed and development is capitalised only when all the recognition criteria are met.
- Selling abroad from India — export of services under a GST LUT, foreign-currency receipts and the documentation that keeps exports zero-rated.
- A US holding company for US investors and customers — see US company registration and the Delaware flip.
- Fundraising — investor-grade models where the AI cost curve is explicit. See fundraising advisory.
Controls we build in
- Audit trail: Indian companies must keep accounts in software that records an edit log of every change, which can’t be switched off. AI-posted entries go through the books, never around them.
- Human sign-off: AI drafts; a named person approves before anything is posted, filed or sent to investors.
- Data protection: under India’s DPDP Act your company stays responsible for personal data it sends to an AI tool, so we choose tools and settings that keep customer and employee data out of model training and under contract.
- Source-linked numbers: every figure in an AI-drafted report ties back to a ledger, bank statement or model cell.
More detail in the guide: AI in the finance function — a practical playbook.
How an engagement works
Most companies start with a fixed-scope review and a pilot of two or three workflows, then move to a monthly Virtual CFO retainer. Engagements run remotely for clients in India, the UAE, the US, Singapore and the UK.
We also help businesses get found in AI answers — AEO services (answer engine optimization).