Key Takeaways
- AI can automate repetitive finance work, reducing manual touches across invoicing, matching, reconciliations, and other transaction-heavy workflows.
- Outsourced finance and accounting teams extend that automation with skilled professionals who manage exceptions, apply judgment, and maintain accountability.
- A continuous-close model reduces month-end pressure by processing and reviewing financial activity throughout the month instead of concentrating work at period-end.
- The goal is to redesign how finance work gets done, giving skilled professionals more capacity for analysis, risk management, and better business decisions.
Every month, our outsourced finance and accounting teams at Connext support close cycles for more than 100 clients. One lesson from seeing that work month after month: the close should not depend on skilled finance professionals spending hours on work technology can already handle. CFOs have an opportunity to redesign that operating model now, not after the next ERP upgrade.
What I have seen in operations is the same pattern almost everywhere. Skilled people spend hours processing invoices, matching transactions, reconciling accounts, reviewing expenses, compiling variances, and moving information from one system to another. The work is necessary, but necessary is not the same as valuable.
I have always pushed back on the word “bookkeeper” because it undersells what a modern accountant does, and the same logic applies here. Keying and matching transactions by hand is not a good use of a professional’s time. Your best finance people should be interpreting the numbers, challenging the business, managing risk, and helping executives make better decisions.
AI gives us a real opportunity to make the shift, and CFOs should be moving now. For outsourced finance and accounting teams, that means using automation to absorb repetitive transaction work while keeping skilled professionals focused on exceptions, controls, analysis, and business judgment.
The Opportunity Is Bigger Than Automation
A lot of the AI conversation still circles one question: will it replace people? Wrong question. The better one is what happens when your best finance people stop spending their days on the most repetitive work.
Picture an AP process where invoices are captured automatically, matched against purchase orders and receipts, and routed to a person only when something doesn’t add up. Now picture reconciliations where the system matches the transactions, isolates the exceptions, and hands the controller the five items needing judgment instead of the 5,000 that don’t.
Picture expense reports checked against policy before reimbursement, not discovered months later during an audit. Picture a cash forecast refreshed every day from AR aging, AP commitments, and bank positions instead of rebuilt every Friday afternoon.
None of this is science fiction. These are the workflows where AI is already changing the economics of finance.
One point gets missed often. AI does not need to be perfect to create value; it needs to beat the manual process it replaces. Most manual processes set a pretty low bar.
Start Where the Money Is
If I were running a middle-market finance organization today, I would skip the grand AI transformation program. I would start with the work consuming the most hours and requiring the least judgment: accounts payable and receivable, reconciliations, expense management, close activities, and cash flow inputs.
These processes have volume, rules, structured data, and measurable outcomes. Those four traits make them strong candidates for AI. The objective stays simple: fewer touches, fewer errors, shorter cycle times, and more of your people working on exceptions and decisions.
The close shows why this matters. When a finance team takes a ten-day close down to five, the win is not five days of saved accounting labor. Management gets five extra days of visibility into the business.
Running 100+ closes a month has taught me one thing above all: the close gets better when it gets boring. One calendar, standard workpapers, one named owner per account, and exceptions surfaced early make the difference. AI makes boring even easier to repeat, because the matching and pre-close checks are done before anyone sits down on Day 1. Faster information means faster decisions, and faster decisions compound.
The RSM Data Should Get Every CFO’s Attention
The broader market is already voting. RSM’s Middle Market AI Survey 2026, based on 1,030 senior business leaders across the U.S. and Canada, found 86% of organizations have partially or fully integrated AI into their operations, and 97% report satisfaction with their AI investments. More than half (54%) say their AI investments exceeded ROI expectations, and 58% plan to invest $1 million or more in AI this fiscal year.
Those numbers change the conversation. Most companies already believe in AI, so the real question is whether they are turning the investment into an operating advantage.
Here the picture gets more interesting. Only 36% of respondents have AI fully embedded across core processes. Inside finance, CPA Practice Advisor’s coverage of the survey points out 45% of finance AI investments lean toward productivity, while just 20% lean toward decision quality. There’s the gap, and it’s also the opportunity.
The companies closing this gap first will earn an advantage nobody would call glamorous, but it matters a great deal: the same finance operation run with fewer manual touches, faster information, and more productive people. The advantage shows up every month and compounds every quarter. A slower competitor finds it very hard to catch up.
AI Alone Is Not the Answer
Buying AI software does not transform a finance function. Point it at a bad process and you get faster inefficiency, not transformation.
Gartner’s research backs this up. In a Gartner survey of 183 CFOs, 84% of finance organizations had implemented or were planning to implement AI, yet only 7% reported a high or very high impact.
In my experience, three things have to be right. First is the data. Dirty vendor records, inconsistent account mappings, and systems disagreeing with one another will not fix themselves because you added AI, and garbage in still matters.
Second is the process, because the process behind the outcome matters. Before automating anything, know how it works, who owns it, where judgment enters, and what counts as an exception. If nobody can explain the process, nobody should automate it yet.
Third is the people, and here is where many companies get it wrong. Employees hear “AI” and think “job cuts.” Without leadership explaining the new model, resistance is guaranteed.
The better model is straightforward. Eliminate the repetitive work and increase the responsibility of the people doing it. Turn accountants into reviewers and problem-solvers instead of transaction processors, and give controllers more time to manage the business instead of managing spreadsheets.
Gartner’s advice to CFOs points the same direction: in the near term, upskill the existing workforce to close digital capability gaps and get more value from the tools already in place. A finance team built this way is a much better finance organization.
The Offshore Multiplier
There is one more opportunity middle-market companies should not overlook. AI gets far more powerful when you pair it with a well-run offshore finance team.
I moved to the Philippines in 2008, and I have spent most of the years since working alongside finance professionals here. What I have learned is simple: the model works when each layer has a clear job. People handle volume, AI handles pattern recognition and exception flagging, and onshore finance leaders handle judgment.
An offshore team manages recurring transaction processing, reconciliations, AP and AR activity, and reporting preparation. With Connext, offshore finance and accounting professionals work in your systems, on your schedule, and under your direction.
AI automates the first layer of work, checks transactions, spots anomalies, and routes exceptions. Our finance and accounting teams are trained on platforms from NetSuite to SAP and QuickBooks to Xero, and they support automation and AI-enabled solutions to improve speed and accuracy.
The onshore controller stays responsible for accounting judgment, controls, approvals, and decisions. Three layers, three different jobs, each doing what it does best.
Here the economics get interesting. AI by itself does not eliminate the need for finance talent, and offshoring by itself does not eliminate repetitive work. Together, they change the productivity equation.
A CFO should not be asking, “How much can I outsource?” Nor should the CFO ask, “How much can I automate?” The better question is how the entire finance operating model should be redesigned now, with both options on the table, and it’s a much more consequential one.
Waiting Is Also a Decision
I understand why CFOs hesitate. The technology changes quickly, systems are fragmented, data is messy, and people are nervous. Nobody wants to make a large investment in something obsolete six months later.
The argument has a flaw, though. You do not need to predict the future to automate today’s repetitive work. You need to know what your finance team does today.
When a person spends hours matching transactions following predictable rules, you have an automation opportunity. When a controller spends half a day assembling information already sitting in three systems, you have another one. When finance professionals spend the first week of every month preparing information instead of analyzing it, you have an operating model problem.
You do not need perfect AI to fix those problems. You need a willingness to change.
Waiting has a cost, too. Every month you delay, your people keep spending time on low-value work, your close stays slower, and your managers get information later. Your finance organization absorbs more volume by adding more labor, while competitors keep learning.
The learning matters, because the first implementation is rarely the best one. Gartner found 63% of finance organizations said AI implementation was slower than expected in 2025. Every one of those teams now knows something a team still waiting does not.
Companies starting now learn where their data breaks, where their processes need redesign, where AI performs well, and where humans need to stay in the loop. Companies waiting for certainty enter the market after everyone else has already built up the experience.
The Question I Would Put on Every CFO’s Desk
Forget the AI strategy deck for a moment. Pull up the team’s calendar, look at what people actually do, and ask one question: what percentage of our finance team’s time goes to work requiring no human judgment?
Under 10%, and the opportunity is modest. At 20%, it deserves attention. At 30% or more, I would act, and at 40%, 50%, or higher, I would treat waiting as a bigger risk than starting.
The goal is not to have the most AI. The goal is to have the most productive finance function, with fewer manual touches, fewer errors, a faster close, better visibility into cash, and more effective controls. Most importantly, it means more of your best people doing work only they can do.
Here is what AI should mean for finance. Not fewer accountants, but better accountants doing better work.
The companies getting this right early are not the ones with the best technology. They have something more important: a better economic model for running finance, and a close boring enough to scale.
Final Takeaway
AI in finance is not a technology project. It is an operating model decision, and the CFOs who treat it as one get results. Start with the high-volume, rules-based work, fix the data and the process before you automate, and give your people bigger jobs instead of smaller ones.
The strongest model I have seen puts each layer where it performs best. AI handles the matching and the exception flagging, an outsourced finance and accounting team handles the volume inside your systems and under your direction, and your onshore leaders keep ownership of judgment, controls, and decisions. When those three layers work together, the close gets faster, the numbers get cleaner, and finance finally has time to be the business partner it was always meant to be.
My advice is simple. Look at your team’s calendar this week, find the hours going to work without judgment, and pick one process to redesign this quarter. Boring, repeatable progress beats a perfect plan every time, because boring scales.
Frequently Asked Questions
Start with high-volume, rules-based work that requires limited judgment, including accounts payable and receivable, transaction matching, reconciliations, expense management, recurring close activities, and cash flow inputs. Financial process automation works best when the underlying data is structured, ownership is clear, and exceptions can be routed to qualified finance professionals for review.
AI can handle repetitive activities such as matching transactions, identifying anomalies, and flagging exceptions, while outsourced finance and accounting professionals manage recurring workflows, investigate exceptions, and keep work moving throughout the month. Internal finance leaders retain responsibility for accounting judgment, controls, approvals, and business decisions. That division of work supports a more continuous close instead of concentrating activity at month-end.
Yes, but the operating model matters. Finance & accounting outsourcing can provide skilled capacity for reconciliations, AP and AR activity, reporting preparation, and other recurring close processes, while automation handles predictable steps within those workflows. Companies looking to automate financial close should first define the process, clean up the underlying data, and decide where human review and approval still belong.
Offshore accounting services can extend a finance team’s capacity by supporting transaction processing, reconciliations, reporting preparation, exception management, and other recurring activities throughout the month. The strongest model keeps the offshore team integrated into the company’s systems and processes, with internal leaders maintaining visibility and authority over material accounting decisions.
Outsourced accounts payable can be a practical starting point because invoice processing, matching, coding, and exception routing are typically high-volume and process-driven. When those workflows are clearly documented, automation can handle predictable activity while skilled accounting professionals review exceptions and ensure the process continues to operate correctly.
It should not be. A well-designed model gives external or offshore professionals ownership of defined execution work while controllers, CFOs, and other internal leaders remain responsible for judgment, controls, approvals, analysis, and business decisions. The goal is to give experienced finance professionals more capacity for the work that genuinely requires their expertise.
Is Your Finance Model Keeping Up?
If automation is changing how finance work gets done, it may be time to reassess the team and operating model behind it. Connext helps companies build co-managed finance and accounting teams around the skills, processes, and oversight needed to create a faster, more scalable close.
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