AI in Accounting: What’s Actually Working Today
I hear the hype too, the breathless press releases promising “fully autonomous accounting departments.” Let’s be candid: general-ledger automation is still at the crawl stage. The ledger itself remains a rules-driven, highly deterministic system, and AI hasn’t rewritten that DNA.
So where is artificial intelligence already pulling its weight? In three very targeted pockets:
1. Assistance With Data Categorization
Picture the daily bank feed. Ninety percent of those lines are mind-numbingly predictable: AWS charges, Zoom subscriptions, the weekly espresso run. Today’s classification models, usually a thin LLM wrapper around a rules-first engine, can suggest the right GL code for many transactions on the first pass.
- Where it shines:
- High-volume cash basis transactions (SaaS tools, recurring ACH pulls).
- Quickly learning from user corrections.
- Where human judgment reigns supreme:
- Exceptions (“Why is Stripe depositing negative $213?”)
- Transactions with timing differences between accounting impact and cash movement (prepaid subscription fees, equity investments).
The upshot? Your staff accountant reviews suggestions instead of keying from scratch, resulting in significant time savings.
CFO’s Guide to Modern Multi-Entity Accounting
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2. Financial Analysis on Tap
Ask a modern language model, “Explain the $54 K jump in COGS last month,” and you’ll get a coherent first-draft explanation tied to line-item movements. That’s not magic; it’s a blend of SQL templating plus an LLM that translates the query output into plain English.
- Best-fit use cases:
- Variance analysis worksheets.
- Quick-turn “What changed?” questions from the CFO.
- Spotting simple anomalies before the monthly close.
- Limitations:
- Garbage data = garbage narrative. If your COA is a mess, the commentary will be too.
- Models are surface-level; they don’t know your pricing strategy or supplier negotiations.
Think of it as a junior analyst who can draft the first paragraph but still needs your business context to nail the story.
3. Drafting Financial Reports & Footnotes
Generative text shines at rote, boilerplate language: SOC-2 disclaimers, ASC-606 footnote headers, even the preamble to your MD&A. Feed it the right numbers and a style guide, and it will spit out a decent first version.
- Where it helps:
- Reducing the copy-paste drudgery of assembling quarterly packets.
- Enforcing a consistent tone across multiple contributors.
- What to watch:
- Citations: auditors will ask, “Where did that figure come from?” Keep sources intact.
- Regulatory nuance: SEC-registered filers still need a savvy human reviewer.
Treat it like spell-check for finance prose, not a substitute for judgment.
What Hasn’t Happened Yet
- End-to-end autonomous close. We’re nowhere near a bot that journals, reconciles, and signs off.
- Self-auditing statements. There is exciting innovation within the financial audit industry, but auditors still rely on evidence, not model confidence scores.
And that’s okay. Stability and traceability are table stakes in accounting; disruption will be slow by design.
Getting Pragmatic Value Today
- Start with a clean chart of accounts. AI can’t categorize what it can’t interpret.
- Deploy in review-mode first. Let humans approve every suggestion until accuracy is proven.
- Log every prompt and output. You’ll thank yourself during audit season.
- Educate the team. A one-hour workshop on “how to talk to an LLM” pays back fast.
- Iterate quarterly. The tech evolves monthly—bake experimentation into your close calendar.
CASE STUDY
Automating Multi-Entity Financial Reporting
G-20 saved time and cut reporting delays by integrating approval workflows with their financial platform—reducing manual errors and streamlining multi-entity reporting.
How We’re Building at SoftLedger
SoftLedger’s DNA is still “real-time, API-first accounting.” AI layers on top, narrowly and transparently:
- Categorization assist in the bank-feed screen—nothing posts without your sign-off.
- Ask-me-anything analysis tied to live ledger data, always showing the underlying query for auditability.
- Report-writer drafts that keep your existing controls intact.
No moonshots—just targeted helpers that shave hours, not introduce risk.
Bottom line: AI is an assistant, not an autopilot. Use it for categorization, quick analysis, and drafting language, then layer on human expertise where it counts. The firms that embrace these modest wins today will be best positioned when the deeper breakthroughs finally arrive.
Have a story about AI saving (or costing) you time in the close? I’d love to hear it.
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