AI Workflows for Accounting: Three Real Examples You Can Copy

The Accounting Time Squeeze: Why Manual Workflows Are Breaking Teams

If your accounting team still spends its afternoons typing invoice data line by line, you're not alone — and you're not imagining the pressure. Despite years of automation hype, industry statistics show that 66% of accounts payable teams still manually enter invoice data, at a cost of $13.54 per invoice versus $2.98 for automated processing.

The squeeze comes from both sides. On one side, the work keeps piling up: the US has 340,000 fewer accountants than in 2019, and the Bureau of Labor Statistics projects 124,200 annual openings against roughly 55,000 degrees awarded each year. On the other side, the tools meant to help keep multiplying, and trust in them lags behind. A MIT Sloan study found that 62% of accountants worry about AI accuracy — a concern that's well founded when chatbots get users "only 90% of the way" and facts still need verification.

What makes this moment different is that AI adoption has already crossed the tipping point. The question is no longer whether accounting teams will use AI, but how well:

• 98% of accounting professionals globally now report using AI, with 46% using it daily — up from 18% in 2023
• 64% of firms plan AI investment this year, up from 57% in 2024
• AI already frees roughly 8.5% of accountant time from routine data entry for higher-value work
• Professionals save an average of 5.4 hours per week, and advanced AI users save 71% more time than beginners

Yet adoption is uneven and often frustrating. Only 34% of solo practitioners have a dedicated AI strategy, compared with 72% of Top 100 firms, and 52% of employees say AI has actually increased the number of tasks expected of them. As one practitioner put it in the Journal of Accountancy: "There are so many new tools... It's nice to see, but it's overwhelming."

That overwhelm is exactly why generic advice ("just adopt AI") doesn't help. What works is copying proven, specific workflows — the kind real accountants have already built, tested, and measured. The Journal of Accountancy profiled six CPAs doing exactly that, with concrete costs and payoffs: a mail-automation system saving four hours a week at zero added cost, a memo bot that cuts drafting from four hours to thirty minutes, and AI-assisted coding that replaced outsourced work costing up to $150 an hour.

The three sections that follow break down the most copyable of these workflows step by step — including where human review stays non-negotiable. And because building custom automations takes time and skill (as one builder put it, "it's not cheap to get these things done"), we'll also point to shortcuts, including the free agent skills directory and MCP connector that SkillzBench is building to help teams assemble and test AI workflows without starting from scratch. If you want to go deeper on vetted agent skills now, you can join the SkillzBench waitlist while the platform is in development.

ctaText: Browse free agent skills for accounting workflows on SkillzBench — join the waitlist to be first in when benchmarks and the MCP connector go live. socialProofText: SkillzBench verifies, security-scans, and benchmarks every agent skill it lists — with Lift results published separately and money never touching scores.

Workflow #1: Snail Mail Automation at Zero Added Cost (Saves ~4 Hours/Week)

Snail mail is the least glamorous part of any accounting operation — and that's exactly why it's the best place to start with AI. Karl Spanbauer, an accountant at Capital Area Food Bank, built a fully automated mail-handling pipeline on software his organization already paid for, and it now saves roughly four hours of staff time every week at zero additional cost.

The workflow is simple enough to sketch on a napkin. Physical mail gets scanned, and the Journal of Accountancy documents each step in sequence:

• Power Automate saves each scanned document to a shared drive automatically.
• An image analyzer extracts the text from the scanned page.
• A secure LLM summarizes what the document is and what it requires.
• A Jira ticket is created so the right team member picks it up and responds.

The payoff shows up in how the team works. Instead of slogging through paper piecemeal, staff now handle all incoming mail in focused 20-minute "Mail Mondays" sprints — everything else has already been triaged, summarized, and routed by the pipeline. Because the whole thing runs on existing Microsoft Power Automate licenses, there's no new line item to justify, which makes this the rare AI project where the ROI conversation takes about thirty seconds.

Spanbauer's key insight is worth quoting directly: "AI can't interact with a piece of paper sitting on my desk. I had to get that piece of mail into that ecosystem." The lesson generalizes — the hardest part of most AI workflows isn't the AI, it's getting analog information into a digital format the tools can act on.

One checkpoint keeps this trustworthy: a human reviews the LLM's summary before anyone acts on it. That matters, given that MIT Sloan research found 62% of surveyed accountants worry about AI accuracy, and that accountants tend to follow AI suggestions even when the AI is uncertain. The review step turns a fast draft into a reliable decision.

If you want to go further, agent skills can automate additional steps of a workflow like this — SkillzBench is building a free directory of vetted, benchmarked agent skills plus a free MCP connector, and you can join the waitlist to browse and test them as they launch.

Workflow #2: A RAG Memo Bot That Cuts Technical Memos from 4 Hours to 30 Minutes

When Glenn Hopper of Eventus Advisory Group finished building his technical accounting memo bot, a task that once consumed four hours took 30 minutes — including human review. The trade-off: roughly 60 hours of expert build time up front, plus a few dollars per run.

The workflow itself is a classic retrieval-augmented generation (RAG) pattern that any technically capable team can replicate. Hopper's approach, profiled in the Journal of Accountancy, breaks down into five steps:

• Collect historical technical memos and chunk them into smaller passages.
• Vectorize the chunks so they can be searched by meaning, not just keywords.
• Store the vectors in a vector database.
• Build a bot using the OpenAI Assistants API that retrieves relevant passages and drafts new memos in the firm's established style.
• Route every draft through mandatory human review before anything leaves the building.

That last step isn't optional decoration. Research shows 62% of accountants worry about AI accuracy, and MIT/Stanford work found accountants tend to follow AI suggestions even when the AI proposes diverging categories for uncertain transactions. Hopper controls hallucinations the same way: "I think we've minimized it a lot by giving it very specific examples to refer to, very specific instructions."

The economics deserve an honest look. Beyond the ~60 hours of build time, running costs include per-query fees of several dollars and $20–25/month subscriptions for the underlying tools — ChatGPT runs $20/month for individuals or $25/user for teams. Hopper is candid about the build burden: "It takes time to do, and you need to have a certain skill set to do it. It's not cheap to get these things done." For many firms, he notes, waiting for vendor products may be more cost-effective than building in-house — a fair assessment given the skill gap, since only 34% of solo practitioners have dedicated AI strategies versus 72% of Top 100 firms.

So who should copy this workflow? Firms with in-house technical talent, a deep library of prior memos, and steady memo volume get the clearest payoff. The 4-hours-to-30-minutes math compounds quickly for anyone writing these documents weekly. Firms without those ingredients should treat this as a template to recognize in vendor tools rather than a weekend project.

One practical shortcut if you do build: don't hand-assemble every component. Agent skills — instruction packages that plug into coding agents like Claude Code or Cursor — can automate pieces such as document chunking and retrieval logic. SkillzBench is building a free directory of vetted agent skills with security scanning and benchmarking, plus a free MCP connector to wire skills into your agents; the waitlist is open now if you want to evaluate what's coming before committing your own 60 hours.

The takeaway: RAG-based drafting works, but it rewards firms that pair the technology with specific instructions, specific examples, and a reviewer who never signs off on an unverified draft.

Workflow #3: AI-Assisted Coding That Replaced $150/Hour Outsourcing

What if you could replace a consultant billing up to $150 per hour with a $20 monthly subscription? That's exactly what Don Tomoff of Invenio Advisors has done since January 2023, using ChatGPT, Claude, and Gemini to write Excel and VBA code he once outsourced.

Tomoff's workflow is refreshingly simple in concept. He describes what he needs in plain English — the analysis he wants, the data structure he's working with — and the AI generates working code. When something breaks or falls short, he describes the problem and iterates. Development work that once took days now takes hours, eliminating outsourced coding projects that previously ran as high as $2,000, according to a Journal of Accountancy profile of practicing CPAs.

The key isn't the tools — it's the philosophy. Tomoff's advice: "You don't want to eat the elephant in one effort... It's iterative." Rather than demanding a finished product in one prompt, he works in stages, refining output with each pass.

A similar staged approach comes from Brianne Smith at Auburn University at Montgomery, who teaches a four-phase framework:

• Ask the question — define exactly what the analysis must answer
• Master the data — understand and clean what you're working with
• Perform the analytics — let AI handle the code and computation
• Share the story — translate results into a decision-ready narrative

Breaking work into separate steps keeps each AI interaction small and verifiable, which matters given the accuracy concerns in the profession. A MIT Sloan study found 62% of accountants worry about AI errors, and that accountants tend to follow AI suggestions even when the model flags uncertain transactions. Generated code deserves the same skepticism — test it on known data before trusting the output.

The payoff math is hard to ignore. Advanced AI users save 71% more time than beginners — 79 minutes a day versus 49 — and skill compounds. The more fluent you become at describing problems precisely, the more the workflow pays.

For readers who want to push this further, agent skills — reusable instruction packages that extend coding agents like Claude Code, Codex, and Cursor — can automate more of the pipeline around these workflows. SkillzBench is building a free directory that benchmarks and security-scans agent skills so you can see which ones actually lift performance, with its waitlist currently open.

Start with one small, annoying Excel task. Describe it to an AI, iterate until the code works, and bank the hours. Then tackle the next elephant — one bite at a time.

Copy These Safely: Human-in-the-Loop Rules and a Faster Path with Agent Skills

Every workflow in this article works because a human stays in the loop. That's not caution for its own sake: MIT and Stanford research found that 62% of accountants worry about AI accuracy, and that accountants tend to follow AI's suggestions even when the model proposes diverging categories for uncertain transactions. AI drafts; you verify and sign off.

Before copying any of the three workflows, set a simple review rule: nothing leaves the desk — no memo, journal entry, or client response — without a person reading it. Glenn Hopper, who built the RAG memo bot, kept hallucination risk low by giving the model very specific examples and very specific instructions, then reviewing every output. That discipline is what turned a four-hour memo into a 30-minute task without sacrificing quality.

It's equally important to start small. Don Tomoff's advice is to not "eat the elephant in one effort" — build iteratively. Brianne Smith's four-phase framework breaks any analytics workflow into manageable chunks:

• Ask the question — define exactly what decision the output supports.
• Master the data — get information into your system first, as Karl Spanbauer did before automating mail intake.
• Perform the analytics — apply AI to one narrow step, not the whole process.
• Share the story — deliver the result to a human audience, with review built in.

Expect a realistic payback window rather than overnight transformation. Industry benchmarks put small business AI payback at 6–12 months, with AP automation around 9–10 months per Gartner. Hopper is candid that custom builds "are not cheap" in time or skill — his memo bot took roughly 60 hours of expert build time — and for many firms, waiting for vendor products may be more cost-effective than building from scratch.

That's where prebuilt components change the math. Agent skills — instruction packages that extend coding agents and chat-app agents — can automate the remaining steps in these workflows, from document extraction to drafting. The hard part is knowing which skills are trustworthy: published research found roughly 26% of skills carry at least one vulnerability. SkillzBench is building a free, independent directory that security-scans and benchmarks every skill, pinning versions and publishing performance lift so you can compare options before installing anything. It's also building a free MCP connector that lets you attach chosen skills to any agent, with per-agent loadouts so the agent only sees what you select.

You don't need to build everything yourself, and you shouldn't install anything unvetted. Join the SkillzBench waitlist to browse, test, and equip verified skills as the directory goes live.