What Is Finance AI? A CFO’s Plain-Language Guide

Cherry Bekaert’s 2025 Middle Market CFO Survey found that 77% of CFOs are integrating or optimizing existing finance technology before considering new investments. For many finance teams, that makes modernization, not another AI platform, the logical starting point.

That reframes the real challenge: it isn’t whether AI exists, it’s where it can create measurable value inside already fragmented finance operations without adding another layer of complexity.

This is particularly relevant for middle-market finance teams. Cherry Bekaert’s survey found that 76% of CFOs are focused on streamlining accounting and finance processes, while fragmented systems and data quality gaps continue to slow financial decision-making. (CBH)

Used properly, AI can help finance teams classify transactions, identify anomalies, reconcile accounts, prioritize collections, summarize variances and support forecasting. But AI is not a shortcut around weak data or broken processes. The value comes when AI is embedded into a well-defined workflow with reliable data, clear controls and human accountability.

This guide explains AI in finance in those practical terms, from traditional automation and generative AI to AI agents, finance use cases, controls, ROI and the path from a first pilot to production.

For finance teams assessing where AI could fit into their existing operations, the first step is understanding data readiness, workflow complexity and control requirements. tkxel’s accounts and finance services overview breaks down the data-readiness gaps mid-market teams encounter first.

• Start with the workflow, not the technology. Identify the finance processes consuming the most manual effort before evaluating AI tools.
• Optimize existing finance technology first. Cherry Bekaert found that 77% of CFOs are integrating or optimizing existing finance technology before considering new investments.
• Prioritize measurable workflows. AP, close, O2C and FP&A offer different combinations of transaction volume, data readiness and measurable outcomes.
• Treat controls as part of the architecture. Define human approval, auditability, exception handling and ownership before scaling.
• Measure more than cost savings. Track efficiency, accuracy, decision speed, forecast quality and financial impact.
• Read the AI automation readiness assessment before selecting which workflow to pilot first.

Middle-market finance teams are under pressure to improve decision speed without proportionally increasing headcount or technology complexity. The urgency is clear: 99% of surveyed middle-market CFOs plan to modernize their finance operations within the next 12 months. Nearly two-thirds of CFOs report that their companies are either actively automating processes to improve efficiency or transforming operations, while 26% still report significant reliance on manual processes across core finance activities. Month-end close, audit preparation and budgeting remain particularly manual.

The problem isn’t just manual work. It’s the chain reaction behind it: fragmented systems create poor data; poor data creates manual reconciliation; manual reconciliation slows reporting; and slower reporting slows decisions.

Connected data and optimized workflows can reduce the friction between financial data, analysis and decision-making. AI can add another layer by accelerating analysis, surfacing patterns and supporting decisions. That’s the conceptual shift this guide walks through.

AI in finance is the application of machine learning, generative AI and increasingly AI agents to processes that involve classification, prediction, interpretation, decision support or multi-step workflow orchestration. The table below breaks down what each layer of that technology actually does inside a finance workflow.

These four workflows illustrate where AI can create value across transaction processing, financial close, working capital and planning. Each has a different data profile, risk level and measurable outcome, making workflow selection more important than simply choosing an AI tool.

1. Accounts Payable / Procure-to-Pay (P2P)
Procure-to-pay is one of the strongest starting points for finance AI. Invoice capture, matching and exception routing are high-volume, repetitive activities with relatively clear outcomes and measurable exception rates. AI supports invoice extraction, classification, PO matching, duplicate detection, exception identification and routing. This combination of high transaction volume, repetitive work and measurable exceptions makes P2P a practical entry point for many finance teams without an in-house data science function.

2. Record-to-Report (R2R) / Close
Record-to-report is another strong area for AI because close activities combine repetitive reconciliation with judgment-heavy exception handling. Cherry Bekaert specifically identifies month-end close and audit preparation among the areas where manual processes remain significant. AI opportunities here include reconciliation, anomaly detection, journal-entry review, supporting documentation, variance commentary and close-status monitoring, which can help shorten close cycles while keeping human reviewers accountable for final sign-off.

3. Order-to-Cash (O2C)
Order-to-cash applies AI to collections prioritization, payment prediction, dispute classification and customer risk signals, ranking which receivables are likeliest to pay late so collections teams focus effort where it matters.

4. FP&A
Poor data quality and fragmented systems constrain forecasting accuracy for many finance teams. That’s a direct bridge into driver-based forecasting, scenario modelling, variance analysis, management commentary and predictive insights, letting FP&A teams build baseline forecasts and run scenario comparisons faster than a manual spreadsheet cycle allows. Corporate finance teams evaluating an AI use case in finance for the first time should treat these four as a shortlist, not a menu to tackle at once.

Gartner’s research shows that adoption accelerated sharply before beginning to level off. In 2024, 58% of finance functions reported using AI, up from 37% in 2023, according to Gartner.

By 2025, Gartner’s finance-function research found adoption had reached 59%, suggesting that the next challenge is less about getting finance teams to experiment and more about turning adoption into measurable, scalable outcomes. But adoption does not automatically translate into impact. Gartner’s 2025 research found that 91% of finance leaders reported low or moderate initial impact, reinforcing the distinction between experimenting and scaling successfully.

KPMG’s 2026 global survey shows how quickly adoption has accelerated: active AI use across finance increased from 30% in 2024 to 75% in 2026.

AI use cases in finance can be viewed on a spectrum of autonomy rather than as a single fixed level of automation. Naming that spectrum helps distinguish an AI system that supports a decision from one that takes action.

Assist
AI helps a human do the work. AI surfaces information, drafts a summary or flags an anomaly. A human reads the output and decides what to do with it. Variance commentary and anomaly detection in the close process sit here.

Recommend
AI helps a human decide. AI ranks or prioritizes options for a human to choose from. Collections prioritization in order-to-cash is a recommend-level use case: AI ranks which receivables are likeliest to pay late, and a collections team decides where to act.

Orchestrate
AI coordinates the work. AI agents coordinate multi-step workflows across systems, such as matching an invoice, flagging a discrepancy and routing it for review, without a human triggering each step individually.

Execute
AI performs a controlled action. AI completes a predefined action without human review at the point of execution. This is generally appropriate for low-risk, high-volume, well-controlled workflows where actions fall within predefined thresholds and errors can be detected, contained or reversed. Execute does not mean unrestricted autonomy. It means autonomy within predefined boundaries.

For most organizations, Assist and Recommend are the practical starting points. Orchestrate becomes relevant as workflows mature and AI agents can reliably coordinate multiple steps. Execute should be reserved for tightly controlled actions that fall within predefined thresholds. The controls condition in the Finance AI Readiness Framework exists precisely to decide, workflow by workflow, how far up this spectrum AI should be allowed to move.

The case for controlled autonomy is supported by KPMG’s global survey. Organizations with stronger assurance readiness reported significant error reduction at more than three times the rate of less-ready organizations (33% versus 6%) and were substantially more confident in their ability to scale AI (42% versus 14%). This changes how CFOs should think about governance. Controls are not simply a safeguard around AI; they can be part of what makes AI scalable.

Controls aren’t simply a safeguard around AI. They can be part of what makes AI scalable.

Before deploying AI into a finance workflow, a team should assess six conditions. These six conditions help determine whether an AI use case is ready to move beyond experimentation into production.

1. Workflow: is the process worth improving?
Automating a broken or redundant workflow just makes the underlying inefficiency run faster, not disappear. Fix the process design before applying AI to it.

2. Data: is the required information reliable?
AI systems depend on the quality and context of the data they receive. Inconsistent or incomplete records can produce unreliable outputs and scale existing data problems rather than solve them.

3. Systems: can the AI access the systems it needs?
ERP, CRM, billing, AP/AR and operational systems need to provide the AI workflow with access to the relevant data and actions. AI applied to a single disconnected system may produce useful output in isolation but remain blind to information elsewhere in the workflow.

4. Controls: what can AI do, and what requires human approval?
Define who can approve the AI output, who can override it and what requires human review before deployment. Without defined answers, an AI system operates without accountability.

5. Measurement: what baseline will prove value?
The team needs its current processing time, error rate, manual effort, exception rate and close duration documented before AI touches the workflow. Without a baseline, teams cannot credibly quantify how much the workflow improved after AI was introduced.

6. Ownership: who is accountable when AI is deployed?
Someone whether in finance, IT or operations has to own the workflow after AI is deployed and be accountable when the system gets something wrong after AI is deployed and answer for it when the AI gets something wrong.

AI can automate a bad process faster, but it cannot determine whether the underlying process is designed correctly.

If data is inconsistent, systems are disconnected or ownership is unclear, AI inherits those weaknesses. That’s why finance AI should be treated as a layer within finance modernization, not as a replacement for it.

The rule is simple: modernize the workflow first, then apply AI where it adds intelligence, prediction or orchestration.

Finance AI is increasingly being measured not only by cost savings, but by the quality and speed of financial decision-making. KPMG’s global survey, which surveyed 1,013 senior finance leaders across 20 countries and 13 sectors, found that organizations reported improvements in decision-making quality (70%), decision-making speed (71%) and forecast accuracy (64%). Seventy-one percent of finance leaders said their AI initiatives were meeting or exceeding ROI expectations.

A CFO evaluating finance AI ROI should measure four things: efficiency, accuracy, decision velocity and financial impact. This prevents a common mistake in AI business cases: treating labor hours saved as the only measure of value. Faster decisions, better forecasts and fewer errors can create value even when headcount doesn’t decline.

A CFO builds a defensible AI business case by answering four questions before requesting budget.

1. What’s real? Separate vendor claims from evidence. Look for documented adoption, measurable outcomes and a clear connection between the technology and the workflow it is intended to improve.

2. Where should we start? Pick the workflow with the cleanest data, sufficient transaction volume and the clearest measurable outcome. For many teams, accounts payable can be a practical starting point because invoice processing is high-volume, repetitive and measurable.

3. How much autonomy is appropriate? Decide whether the workflow calls for Assist, Recommend, Orchestrate or Execute, based on the controls and risk tolerance defined in the readiness framework above.

4. How do we move from pilot to production? Establish a baseline before implementation and measure the outcome over a defined period, typically 60 to 90 days for a workflow where sufficient transaction volume exists, before expanding scope.

For teams ready to assess their finance AI readiness, tkxel’s advisory and strategy services cover how to prioritize AI initiatives against existing systems and controls.

A board-ready case does not require a company-wide AI strategy on day one. It requires one workflow, one metric and one documented result.

tkxel is a B2B software engineering and AI services company that helps organizations move AI use cases from workflow assessment and prototyping into production systems. Its approach centers on mapping a high-friction finance workflow, identifying where AI can add value, and designing controls, integrations and auditability into the solution architecture.

AI in finance is not a single technology or a single software purchase. It is a new layer of intelligence that can be embedded into the finance workflows CFOs already manage, from accounts payable and reconciliation to forecasting, collections and variance analysis.

The starting point isn’t AI. It’s the workflow.

Modernize the workflow. Establish the controls. Apply AI where it adds value. Increase autonomy only as the workflow proves ready.

Cherry Bekaert’s middle-market survey shows why. Finance leaders are prioritizing process improvement and technology optimization, while fragmented systems and poor data quality continue to slow decision-making. The question is no longer whether finance modernization matters, but how CFOs can introduce AI without adding complexity or weakening control.

For a structured way to check whether your finance function has the data and controls maturity to start, review your current baseline against the six readiness conditions above before scoping a pilot.

For a CFO evaluating AI, the practical path is straightforward: identify one high-friction workflow, establish the baseline, assess data readiness, define controls, deploy AI, measure the outcome and scale what works.

The goal isn’t to automate finance for the sake of automation. It is to help finance teams move from data to insight, and from insight to decision, faster, without giving up control. That is where AI in finance becomes more than a technology experiment. It becomes a finance modernization strategy.