AI Bookkeeping: How We Recommend Using It

Why We Tried It

Like many accountants, we're constantly hearing the buzz: AI is here to revolutionize everything. We wanted to know if that promise held up in the trenches of day-to-day bookkeeping. The potential upside is obvious: What if AI (like ChatGPT or other large language models) could clear the backlog of uncategorized transactions, saving us hours of manual data entry?

We decided to put it to the test. We weren’t looking for miracles, but we were hopeful. Could it speed up categorizing bank and credit card transactions, assign vendors, add memos, and suggest rules?

The bottom line: It landed around 60% accuracy on our dataset. This was impressive in some areas, but not nearly reliable enough to run unattended.

Before you spend hours designing your own prompts, we’re sharing what we tried, what performed well, where it fell down, and the workflow we actually recommend.

How We Tested

Our test was straightforward. Our "lab" was a dataset of several thousand real, anonymized transactions. This data was pulled from multiple accounts and entities, representing a mix of service businesses, and contractor-heavy operations to ensure a realistic level of complexity.

We tasked the AI with the core bookkeeping functions:

• Assigning expense categories (e.g., COGS vs. OpEx, Travel vs. Meals)

• Normalizing messy vendor names

• Drafting clear, plain-English memos

• Proposing new, recurring bank rules

To measure success, we benchmarked the AI's output against a "gold standard" set of books that had already been categorized and reviewed by our senior team. We also provided the AI with historical CSV mapping tables to give it "few-shot" context, mimicking how a human would learn from past periods.

What Worked (Surprisingly Well)

AI is a powerful pattern-matching engine, and it shined in a few key areas:

1. Pattern Recognition with Examples: This was its strongest skill. When we provided a clean CSV map of historical vendor-to-category pairs (e.g., AMZN MKTPLACE = Office Supplies, DELTA AIR = Travel), it learned those patterns instantly. It not only applied them to new transactions from the same vendors but also made reasonable guesses for similar new ones.
2. Memo Drafting: It was remarkably good at drafting short, clear memos. Given a messy transaction line like "PAYPAL *ZOOMVIDEOCOMM 88879", it could correctly propose a memo like "Payment for Zoom Video subscription." This reduced a significant amount of typing.
3. First-Pass Rules: The AI was helpful in proposing draft bank rules in plain English, such as "IF vendor contains 'Verizon' THEN category = 'Telephone & Mobile', memo = 'Monthly phone bill'." A human could then review and implement this in the accounting system.

Where It Struggled (The Critical 40%)

The AI's successes were in high-volume, low-complexity tasks. It fell down—and fell down hard—as soon as ambiguity, policy, or context was required.

1. Ambiguity and Edge Cases: This was the biggest failure. AI has no real-world context. It can't know if a $50 Uber was to a client meeting (Travel) or a personal trip home (Owner's Draw). It struggled to differentiate between a laptop from Best Buy (Fixed Asset) and printer ink (Office Supplies). It had no way of handling complex split transactions, like a single hotel bill that needed to be split into 'Lodging', 'Meals', and 'Non-Reimbursable'.
2. Vendor Normalization: It was easily confused by messy bank data. It often treated "AMEX PMT 800-555-1212", "AMERICAN EXPRESS E-PAY", and "AMEX*HOTEL_PURCHASE" as three different vendors, breaking its own pattern matching.
3. Policy Adherence: This is a deal-breaker. An AI doesn't remember your firm's specific capitalization policy. You can't tell it once, "We capitalize all assets over $2,500." It needs to be reminded in every single prompt, or it will confidently categorize a $5,000$ equipment purchase as an "Office Expense," creating a significant tax and reporting error.
4. Drift and Overconfidence: The AI always gives an answer. When its examples were thin, it would "drift" and confidently assign the wrong category. This is dangerous: a 100% confident, 100% wrong answer is worse than no answer at all.

The 60% Reality Check

That 60% accuracy number needs context. With no historical data or examples, accuracy was closer to 40%. With our best-case scenario (clean mapping tables, explicit prompts), we peaked at around 60-70% on routine transactions.

The problem is that the "last 40%" of transactions—the exceptions, the splits, the judgment calls—consumed 90% of our review time. The "time savings" we sought were completely erased by the high-stakes work of correcting subtle, confident errors.

If you already maintain a clean vendor list, QuickBooks (or Xero) bank rules are simpler, faster, and more reliable than an AI for 90% of recurring transactions.

The Real Cost-Benefit

• Benefits: It's a great assistant for drafting memos, brainstorming rules, and doing an initial pass on a brand-new client's messy vendor list.
• Costs: The real cost isn't the API fee; it's the senior-level review time required to catch the false positives. The "silent killer" of AI in bookkeeping is the subtle misclassification (e.g., COGS vs. OpEx) that a junior staffer might miss, but which silently destroys your margin analysis and tax position.

The Better Workflow: “Rules First, AI Assist Second”

We quickly realized AI is not an autopilot; it's a co-pilot who can't read all the gauges. The best workflow keeps the human and the accounting system in charge, using AI for specific, helpful tasks.

1. Rules First: Build and maintain your vendor rules inside your accounting system (QuickBooks, Xero, etc.). This is your source of truth.
2. Mapping is King: Maintain a simple mapping table (Vendor → Category / Memo / Class). This is your firm's "brain."
3. Use AI as an Assistant: Now, use AI for discrete tasks:

• • Normalize: Feed it a messy bank export and ask it to "Normalize vendor names based on this mapping table."
  • Draft: Ask it to "Draft memos for these transactions."
  • Flag: Ask it to "Flag any transactions that don't fit our current rules and suggest 3 possible categories."

1. Human Review: All AI output must be routed through human review before it gets posted. Keep AI out of the final-entry process.

Our Recommendation

We do not recommend using general-purpose AI to replace bookkeeping or to auto-post transactions. The risk of errors, the lack of an audit trail, and the non-compliance with firm policy are too high.

We do recommend using AI as a "clerical assistant" to speed up manual, text-based tasks. Use it to reduce typing, not to replace thinking. If you want true automation, look at specialized bookkeeping automation tools that have native integrations, audit trails, and role-based controls.

The Takeaway

AI got us to 60% accuracy, but the remaining 40% required more review time than it saved. For reliability and speed, a clean bank-rule engine beats AI every time.

Use AI as an assistant to draft memos and normalize vendors. Use your accounting system as the engine for all rules and posting. Keep human oversight on anything that touches your financial statements.