Beyond the "Garbage AI": Why Purpose-Built Tools Are the Only Way to Close in 3 Days

Karl Reichert doesn't mince words.

As Controller at Capital Area Food Bank, he's seen his share of AI hype. "There's a lot of what I call garbage AI products being shoved into financial products," he said during a recent FloQast webinar on AI transaction matching. "If you have financial software, there's probably some chatbot or AI feature that got added, and you know it's not useful."

His example was an AP software chatbot that never gives correct information when you ask it to make a report. "You can just run the report yourself," he said.

But when FloQast released AI Transaction Matching in early 2025, Karl immediately signed his team up. Admittedly, he's "a huge nerd about these types of things" (his words), but also because he recognized something different: purpose-built AI that actually solves a real accounting problem.

The results speak volumes. Capital Area Food Bank slashed their bank reconciliation time from 10 days down to three or four days, with 95% of transactions matching at the click of a button. More importantly, Karl's team now maintains the tool themselves, using it as hands-on training for the AI skills they'll need throughout their careers.

This is the trust gap in action. Generic large language models (LLMs) might offer some interesting features, but when it comes to the precision accounting demands, you need AI built specifically for the task at hand.

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The Trust Gap: Why Generic AI Fails in Accounting

Accounting isn't a field where "close enough" works. A single mismatched transaction can lead to hours of investigation. A hallucinated data point undermines an entire reconciliation.

This is where most AI tools stumble. LLMs excel at generating human-like text, but they're fundamentally probabilistic. They predict the most likely next word, not the definitively correct number. For matching thousands of financial transactions, it’s a dealbreaker.

FloQast's AI Transaction Matching sidesteps this problem entirely. When you create a matching rule, the system converts plain language instructions into actual code on the backend. This means:

• No AI hallucinations: Rules execute deterministically, the same way every time
• Full transparency: Accountants can review every match the system proposes
• Human oversight: Teams act as reviewers, not blind trust-givers

"This is not a black box," explained McKenzie Ek, Solutions Consultant at FloQast and former two-time FloQast customer. "We want accountants to be very involved in the process even if they're getting benefits from using AI for automation. We want to elevate them from preparers into reviewers."

How Purpose-Built AI Actually Works

The difference becomes clear when you see it in action. During the webinar, McKenzie demonstrated matching 33 ACH batch payment transactions totaling $1,480,000. The process took seconds:

1. Select the GL transactions sharing a common reference number
2. Find the corresponding bank transaction
3. Click "Create AI Rule"
4. FloQast generates a plain-language rule you can review and edit
5. Save and apply—FloQast immediately found 21 additional matches using the same logic

That's purpose-built intelligence designed around how accountants actually work.

The system handles complexity that would slow down manual processes or simple rule-based systems. Many-to-one matches? No problem. Many-to-many? Still works. Transactions that arrive within a three-day window? Easily configured.

And, once you set up these rules, they keep working month after month. They don't forget. They don't get tired. They don't make copy-paste errors at 4:47 p.m. on a Friday.

The Real-World Transformation: From Annual to Three Days

When Karl joined Capital Area Food Bank three and a half years ago, the organization essentially had no close process. Bank reconciliations hadn't been completed for nearly a year.

A year.

"Not super ideal," Karl noted.

The first victory was getting bank recs completed within 10 days of month-end. Still lengthy, but far better than annual. However, 10 days wasn't sustainable for a growing nonprofit that distributes 60 million meals annually.

The bottleneck was manual clicking. NetSuite's bank reconciliation module required staff to manually match thousands of deposits each month. The built-in rules weren't customizable enough. Mismatches meant starting over.

"The reason it took so long was literally just the manual clicking," Karl explained.

AI Transaction Matching eliminated that bottleneck. After spending time setting up initial rules in April 2025, the team achieved a 95% match rate right out of the gate. Bank recs now close in three or four days.

But the transformation ran deeper than time savings. The team completely reimagined their processes:

• Proactive prep: Instead of waiting until month-end to hunt down autopay invoices from HR and other departments, they capture them throughout the month in real time
• Data consistency: Journal entry memo fields follow standardized formats so AI rules can digest them reliably
• Continuous improvement: The team tracks manual match rates as a performance metric, investigating any month that dips below 95%

That strong reconciliation foundation enabled Capital Area Food Bank to consistently close their books in under 20 days. Now they're targeting 15 or fewer.

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Why Data Quality Is Your Best Friend

Here's the part nobody wants to hear: AI can't fix garbage data.

Karl was blunt about this reality. "I have found that AI is no substitute for garbage data quality and inconsistency, at least not yet."

If you record revenue as one big summary entry one month, then break it out the next, no AI tool will magically reconcile that. The system needs consistency to create effective matching rules.

This doesn't mean your data needs to be perfect before you start. FloQast offers data normalization agents specifically designed to clean up messy bank and credit card transaction data. But the better your upstream data hygiene, the higher your match rates will be.

Karl's team benefited from having already standardized much of their data before implementing AI matching. That preparation paid dividends in a smooth, effective adoption.

The lesson? Think of data quality as an investment that compounds. Every hour you spend establishing consistent naming conventions, standardized memo fields, and reliable journal entry formats multiplies into hours saved during every subsequent close.

The Outcome: Strategic Accountants, Not Data Entry Clerks

Let's talk about the benefit Karl called his "favorite part" that doesn't show up in traditional ROI calculations.

AI Transaction Matching gives his staff hands-on training with AI tools that actually work. Not chatbots that hallucinate.

"My team gets experience with prompt engineering," Karl noted. "That prompt engineering is very transferable to any AI tool."

The team learned that asking AI to help create FloQast prompts works remarkably well. "AI knows how to prompt AI the best, as it turns out," Karl observed.

This creates a virtuous cycle. Team members experiment with new rules. They track match rates. They investigate exceptions. Karl’s team self-drives.

"This is one of the exceptions where they just kind of ran with it, and they have self-motivation to go and make it more efficient, to make it work better," he said.

That's the true transformation. Accountants at Capital Area Food Bank aren't spending their days clicking through thousands of transactions. They're becoming FloQast Certified Accountants (FCAs) who understand AI, who think strategically about process improvement, who bring more value to the organization.

When you turn staff into strategic thinkers rather than data entry clerks, that's when closing in three days becomes possible.

Your Next Steps

If Karl's story resonates, here's how to start your own AI matching journey:

Assess your current state: Where do transaction matching bottlenecks actually live? Bank recs are the obvious candidate, but subledger-to-GL matches, credit card reconciliations, and clearing accounts all qualify.

Prioritize for impact: Use your reconciliation completion data to identify which accounts hold up your close. Start there.

Get your team involved early: The accountants doing the work know where the pain points are. Let them test, experiment, and build the rules.

Track the right metrics: Manual match rates tell you where rules need refinement or where data quality needs improvement.

Invest in learning: FloQast's Certified Accountant program offers 20-plus CPE credits and teaches you how to use AI in accounting. Your team's skills grow alongside your efficiency.

Want to see AI Transaction Matching in action? Watch the full on-demand webinar where Karl, McKenzie, and Jason Andrews walk through real examples, customer stories, and practical tips for implementation.

Or explore the AI Agent Demo Library to see purpose-built agents for everything from credit card reconciliations to sales commission calculations.

The future of accounting isn't about replacing accountants with AI. It's about replacing tedious manual work with intelligent automation so accountants can focus on what actually matters: strategic analysis, process improvement, and closing the books before the weekend starts.

As Karl proved, that future is already here. It just requires tools built for accounting, not generic AI dressed up in accounting software.

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