Deep Dive into how AI is transforming finance and how you can apply data-driven algorithms to improve financial analysis and more.
Deep Dive into how AI is transforming finance and how you can apply data-driven algorithms to improve financial analysis and more.
AI is the talk of 2026.
Since ChatGPT first arrived in late 2022, agentic AI, machine learning, and large language models have moved from novelty to infrastructure at a pace that most industries weren't fully prepared for. The generative AI market has reached $91.57 billion this year, and 65% of organizations now use it in at least one business function—double the rate from just ten months ago.
Applications for businesses are exploding everywhere, from marketing to operations to customer service to legal. According to surveys, more than 70% of managers believe that generative AI will extend the capabilities of knowledge workers, and 62% of CEOs agree that artificial intelligence will define the next era of business.
Finance is no exception; if anything, finance will be the most affected by this technology.
The core dilemma facing finance remains essentially the same, and has since the beginning of time. What has changed is everything surrounding it.
Data volumes have expanded dramatically, not only financial but also operational, and with the global market for AI expected to grow to around $391 billion and nearly quintuple within the next five years, the pressures are mounting on finance.
The truth is that AI isn't going away, regardless of whether we like it or not, and it's transforming the business environment much faster than many planning processes can accommodate.
Along with rapid developments in the field of technology, expectations have arisen regarding real-time information to allow for efficient decision-making in a continuously fast environment.
While the board needs real-time data to manage their businesses, CEOs are looking for forward-looking advice.
At the same time, auditors seek to see clear information in the financial statements of companies.
In contrast, the FP&A team is manually reconciling the past period's information using a spreadsheet that no one understands anymore.
AI is not something that will come into play in the future. AI is the technology currently revolutionizing finance operations across the globe, from closing periods in mid-market organizations to doing due diligence in private equity firms.
The issue here is not whether AI should be part of the finance process, but rather whether your company is developing its capacity for using AI strategically while others do not.
This guide provides practical information on the usage of AI in finance.
The point is not about automating with bots and replacing humans with machines. The point is about adapting to an environment that has become, let's be honest, too complex for conventional solutions.
Look at the landscape. Modern finance teams are required to analyze information collected from ERP systems, CRMs, operational databases, market feeds, and a dozen other SaaS products — and derive conclusions that are both reliable and prompt enough to provide any practical value.
Close periods that used to take two weeks have been compressed into three-day windows, a shift already underway as automation continues to streamline financial processes across organizations of every size. Reports that were once sufficient on a quarterly basis now need to come out weekly — and they need to mean something when they do.
All of these factors, combined, make the business case for artificial intelligence almost inevitable.
But even then, AI won't solve everything. What it does is expand what's possible without expanding your team — and for finance leaders thinking about where AI-driven financial planning actually fits into their function, that's the more useful frame.
There are so many different specific capabilities where AI can merge with finance implications to deliver measurable value:
The basis of traditional forecasts lies in historical data and adjustment through assumptions. A professional modeler creates a model, inputs all necessary variables, uses the growth rate, and generates a range – a perfectly fine technique – but one that is inevitably based on past experience and will be inaccurate when faced with rapid changes.
The methodology of machine learning forecasting models is quite different from traditional forecasting models. Instead of applying assumptions, the models analyze large amounts of data, identifying patterns such as sales cycles, payment cycles, seasonalities, market indicators, and updating their forecasts constantly.
It means that it provides not only a more accurate forecast but also an evolving model that operates almost in real time without having to wait until the next planning period.
When talking about FP&A, we see how the forecasting methodology based on AI takes into account traditional approaches used in cash flow forecasting while being able to go further and generate predictions in scenarios. Previously, a model had to be created from scratch each time an analyst needed to develop a scenario, but now everything happens automatically.
Accounts payable, accounts receivable, invoice processing, three-way matching, bank reconciliations, period-end close activities—these are the mechanical functions of accounting. Necessary, rule-bound, and historically dependent on human attention to detail.
AI-powered automation handles these tasks with considerably less friction. Intelligent document processing extracts data from invoices regardless of format. Automated matching flags discrepancies for human review rather than requiring a human to find them first. Reconciliation tools identify breaks and suggest corrections based on historical patterns.
The result is a close process that's faster, more consistent, and substantially less dependent on every team member working nights at quarter-end. Finance teams that have implemented accounting automation typically report a meaningful reduction in manual processing time—freeing staff to focus on analysis rather than data entry.
Mitigating fraud and risk in business is possibly one of the most important jobs of the finance and IT functions across any business. Pattern recognition is something machine learning does very well.
Fraud, by its nature, is a pattern deviation.
Checkmate.
The process of risk monitoring using AI technology examines transactions on an ongoing basis to detect abnormal activity outside the norm before such activities become serious problems.
In this respect, the process differs significantly from conventional control processes since the latter usually focus more on periodical reviews. With AI analyzing transactions on an ongoing basis, an anomaly concerning vendor relations or authorization could easily be detected on the same day that it takes place. AI also enables liquidity ratio monitoring.
The least recognized potential application of AI in finance involves going beyond bookkeeping in decision-making processes. This includes analyzing margins, reducing costs, financing, and investment prioritization, among others. The process involves the amalgamation of vast amounts of information to make an informed decision, and this is one area where AI can really add value by improving human judgment.
It can feel overwhelming to implement AI across your business all at once. More so, most finance AI implementations tend to underdeliver, not because the technology fails, but because the organization was not prepared for it. It's important not to skip the foundational work before jumping straight into tools that can help.
Start with the highest impact pain points first. The forecasting cycles that take two weeks, the close process relies on one person who knows where everything is. The reporting that requires three or more days of manual assembly before it reaches the CFO.
These bottlenecks are your highest-ROI starting points, and they're also the places where an AI implementation can demonstrate value quickly enough to build internal buy-in.
The quality of AI technology is contingent upon the quality of the data used for training. While this may not be the most compelling aspect of AI to sell, it is a simple fact that the more fragmented, inconsistent, and poorly managed your data is, the less likely it is that any AI software, however sophisticated, will yield any useful insights.
Take stock of your data infrastructure before choosing your tools. Can your financial systems communicate with one another? Is your chart of accounts standardized within your organization? Does your historical data have sufficient cleanliness for training purposes?
The Market for AI software is expanding rapidly, and honestly faster then more finance teams can even evaluate. The question is not which tool has the best demo, but which integrates the most cleanly with your existing ERP and accounting systems, addresses your necessary bottlenecks, and won’t requireae huge implementation just to get running.
Make sure the tool you are choosing actually adds value to your organization, and be careful of adding AI capabilities that sit outside the core financial stack creates data silos, reconciliation problems, and adoption friction.
Rollout initiatives lack change management and will never succeed. The use of AI by finance departments without the involvement of the IT department leads to issues related to security, integration, and data governance. Without engaging people who manage organizations, there can be no data flow, which is needed for the successful operation of forecasting models.
The participation of the CFO during an AI deployment does not only involve funding. It involves actively ensuring that finance concerns, IT limitations, and empowers the CFO to use change management across the organization.
A phased approach is the best approach. Pilot with one use case, measure those results and then expand. Once your AI is consistently performing, then you can send out more comprehensive rollouts.
This strategy, which takes things slower, limits implementation risk, generates proof points that support broader adoption, and allows the finance team to build competency before the stakes get larger.
If you're unsure where to begin or how to build the implementation roadmap, this is exactly the type of strategic advisory work a Fractional CFO can provide without requiring a full-time hire to lead the initiative.
The tactical advantages of applying AI within the finance function are enormous, but it’s the strategic aspects that ultimately tend to occupy all the conversation during CFO meetings.
At the tactical level, one can expect improved closing cycles, reduced manual errors, improved accuracy of forecasts, and valuable savings in terms of time spent on repetitive actions. All of the above can be easily measured and quantified.
Yet the latter are far more significant. With the mundane aspects managed automatically, CFOs find themselves having more freedom to analyze numbers and make strategic conclusions about where the company is heading. As a result, FP&A shifts from merely generating reports to questioning their findings and making connections. It’s what being a strategic finance leader is all about.
AI also improves short-term cash flow visibility, reinforcing 13-week cash flow planning with dynamic, data-driven inputs rather than static assumptions.
And can we be honest? The hype around AI has created a dynamic where organizations feel pressure to adopt quickly—and in that rush, the genuine risks get undersold.
In the case of training an AI system with incomplete and erroneous financial data, the results obtained would be unambiguously inaccurate, showing no signs of uncertainty. The output would be a single value just as reliable as any other.
If financial analysts begin trusting the AI predictions blindly and bypassing any human verification, this might lead to severe consequences. An AI tool must assist the financial analysis process, but it cannot substitute for the critical thinking involved in the interpretation of its results.
Many ML algorithms provide solutions without any explanation of the underlying decision-making mechanisms. In the world of finance, this is unacceptable due to the governance requirements and regulatory standards.
Most mid-market finance functions are running a combination of legacy systems, newer SaaS tools, and manual processes. Getting AI to work reliably within that environment requires more technical groundwork than vendors typically acknowledge.
Many companies are using AI across many different sectors and use cases, including :
Enterprise FP&A teams are replacing static annual forecasts with rolling, machine-learning-driven models that update as new actuals flow in. The result is a planning function that responds to real-world changes rather than waiting for the next budget cycle to acknowledge them.
Mid-market companies are using AI-powered close automation to compress what were once 10 to 15-day close processes down to 5 to 7 days, with fewer reconciling items and significantly less after-hours effort from accounting staff. Just check out this AI for Accountingg Guide to see how companies are producing financials faster and faster.
Private equity firms are leveraging AI to accelerate financial due diligence, analyzing target company financials, flagging anomalies, and surfacing risk indicators across large data sets in a fraction of the time traditional diligence would require.
High-growth SaaS companies are often already using AI in their daily operations to monitor the financial metrics that matter the most at each growth stage. They’re connecting revenue recognition, churn, and unit economics every day instead of waiting for quarterly reviews.
Just look at how AI empowers finance teams:
Generative AI is bringing another dimension to this development. Alongside prediction and automation, large language models are becoming capable of generating financial narratives, drafting board reports, and analyzing information across multiple sources. Tasks that would previously require an analyst's entire weekend now get completed within seconds.
Ultimately, we all might be right if we predict that finance functions will become increasingly self-sustained—AI managing all transaction-related tasks, monitoring processes, and ensuring compliance; humans will focus solely on interpretation, assessment, and strategy development.
So, the question for today's CFOs is clear: are you ready to develop this capability, enjoying the unique advantages of being a first mover, or are you going to have to urgently upgrade your finance function to meet these requirements in two years' time?
Early adopters of finance AI—those integrating it within FP&A, risk management, and reporting—are likely to become well-equipped to provide the forward-looking financial guidance that boards are increasingly expecting.
The approach that doesn't work: giving AI software to a finance team that hasn't prepared its data environment, lacks a roadmap for implementation, and doesn't have senior financial leadership capable of defining the areas of automation.
McCracken Alliance works with companies that are serious about finance transformation—not the kind that involves purchasing a tool and hoping for the best, but the kind that involves building a finance function capable of competing in a data-intensive, real-time operating environment.
That means strategic advisory on where AI creates the most value in your specific context, financial modeling and forecasting infrastructure that can support AI integration, process optimization that prepares your finance function for automation, and technology alignment that ensures new capabilities work within your existing systems rather than alongside them.
Whether through a Fractional CFO engagement or an Interim CFO during a period of transformation, the goal is the same: a finance function that's built for where business is going, not where it's been.
Connect with McCracken Alliance to talk about how your finance function can start using AI more strategically.
Among the most common examples are financial forecasting, automation of payables and receivables, anti-fraud activities, acceleration of closing processes, and real-time reporting. In general, the use of artificial intelligence typically starts with FP&A and accounting automation since there is an immediate improvement in efficiency that can be seen.
There will be more rapid and accurate forecasting, less time on accountancy activities, early detection of risks, and additional opportunities to conduct financial analysis. Eventually, companies tend to move from reporting to providing proactive financial advice.
Definitely not, but such reasoning is completely flawed. AI assists in conducting repetitive processes and improving analytical capabilitie, bute it does not duplicate the human ability to make decisions, think strategically, or communicate effectively. The best future-ready finance professionals are those who collaborate with AI.
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