AI investments can fall short because of gaps in the underlying accounting and finance foundation, not because the technology itself is lacking.
The pressure is building. Your peers are talking about it. Your board's asked about it. In fact, you might have already sat through a demo or two. You know you're supposed to be doing something with AI. Yet, you haven't actually pulled the trigger on it.
You're not alone. Only 36% of CFOs surveyed by Gartner said they're confident they can drive real impact with AI at the enterprise level. Gartner also found that poor data quality and gaps in technical skills are two of the biggest barriers to AI adoption in finance.
The hesitation often comes down to uncertainty about whether your operation, particularly the foundation, is prepared for AI in the first place. AI doesn't fix a broken process. It just amplifies whatever you feed it: good or bad, fast or slow, clean or messy. A broken process automated is still a broken process. AI can speed it up, allowing errors to multiply before you catch them.
Before you move forward, it's worth understanding exactly what you'd be speeding up with your current processes and foundation to become AI-ready.
Imagine a fast-growing, multi-location franchise business. Invoices come in daily from dozens of vendors across hundreds of locations. Someone has to sort every single one to the right place. Right now, that someone might be using spreadsheets to match invoices to locations by address. An invoice might list “123 Main Street,” while the company’s records show “123 Main ST.” Small differences like “Drive” versus “DR” can cause the formula to miss an otherwise identical address. Across hundreds of locations, those mismatches add up quickly, leaving someone to correct them by hand, one at a time.
You might think AI would be the obvious fix. If you feed it enough examples of Street versus ST, Drive versus DR, and every other variation, it'll close that matching gap quickly. Problem solved, right?
Not quite. Suppose three different franchisees operate three different brands out of the same address, like a gas station that also houses a coffee shop and a sandwich chain. The address will match the location and get sales or expenses posted somewhere, but not necessarily to the right franchisee. A franchisee could get paid for sales that weren't theirs or, worse, get stuck with someone else’s bill. A smart algorithm doesn't solve the underlying problem: the address still produces a match, so nothing gets flagged as an error, even though the invoice landed with the wrong franchisee.
The foundational fix isn't AI. It's having a unique identifier for each franchisee, something consistent that vendors and internal systems agree on, so there's never a question of who the invoice belongs to. That's a process decision, not a technology one. If you skip it, no amount of AI sophistication will save you. You'll just get the wrong answer and no reason to question it.
Now consider, if a single address field can throw off your entire matching logic, what other gaps in your finance function should you address before adding AI?
“Ready” starts with agreeing on a single source of truth.
Say, for example, your sales team tracks performance in one system and your finance team in another. Every month, both teams want to know: Why doesn't the sales system match what the financials show? Instead of analyzing what the numbers mean, you're reconciling two versions of the same story. That's a structural problem that existed long before AI showed up. If you layer AI on top of disconnected systems, you've just automated the confusion.
This is also where clean, consistently categorized data comes in. It's the difference between a finance team spending most of its time validating the numbers and one that can spend more time explaining what they mean.
Remember when it seemed like QuickBooks Online made everyone a bookkeeper? Data entry got easier, but that didn’t mean the books were always right, because typing numbers into the right box and knowing which box is right aren't the same skill. AI can create a similar risk if the underlying data and processes aren’t sound. Readiness also means having the internal controls to catch what AI misses. Someone has to ensure the output makes sense before you act on it or use it in an investor update or a board presentation. That review keeps stale, wrong or mismatched data from shaping important business decisions.
To prepare your finance function for AI, start with the fundamentals: standardized processes, one source of truth and real oversight. Once those are in place, you can start getting more value from AI. Instead of waiting three weeks for a close to find out how you're doing, dashboards can show you what happened yesterday or an hour ago. You're making decisions from real-time data instead of a gut feeling and you're addressing problems before they show up in next month's numbers instead of after. That's what foundation-first looks like.
You may have thought closing that gap meant a choice between buying an AI tool or hiring more people to keep up. Another option is to plug into a model where the processes, AI and experienced people already work together. Building it yourself means finding and retaining people who understand both the technology and accounting well enough to know when the two are working in tandem or against each other. That's a rare combination. It’s expensive to hire for and takes time to assemble when you’re trying to run the business.
Human judgment is necessary, wherever it sits. Suppose your AI flags an invoice for review because it can't code it or it's never seen the vendor before. Someone has to figure out what happened. An experienced eye can glance at it and know the issue immediately. For instance, it might be that the company switched janitorial services recently. AI can surface the exception and help investigate it, but experienced accounting judgment is still needed to determine what happened and whether the output makes sense.
When Excel was introduced, everyone assumed accountants would become obsolete. Instead, accountants’ work changed and their judgment became more important. AI is likely to have a similar effect. It doesn't replace the person who understands your business. It raises the bar on what that person can do and where their judgment adds the most value, whether that expertise sits in-house or with an outsourcing provider.
If that judgment isn't in place, nobody's double-checking the output before it reaches the CEO or the board. By the time anyone notices a mistake, you may have made a decision based on incorrect data. Now you're looking in the rearview mirror instead of ahead.
Getting your finance function AI-ready on your own is possible, but it may take longer, cost more and be harder to staff, because you need people who understand both your industry and the technology. Meanwhile, you're still trying to run and grow the business.
That's the gap outsourcing is built to close by putting standardized processes in place before adding AI. Outsourcing typically costs less than staffing the same capabilities in-house. Between global resources working around the clock and the built-in redundancy of a team instead of a single hire, some companies can see savings north of 50% once a transition is done.
You're not the one worrying about turnover, retraining or having a key person leave with your institutional knowledge in their head. What you get instead is a faster close, visibility into your cash position and forecasts you can trust, without spending the next few years building the capability that gets you there. Build it yourself and it becomes a multiyear infrastructure project. Borrow it, and it's already running.
Preparing your accounting and finance function for AI starts with strengthening the processes and data beneath it. Learn how our Accounting Outsourcing services can help you build that foundation and get more value from AI.
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