What CFOs Should Know About AI Adoption in Finance

Artificial intelligence is no longer a future consideration for finance leaders. It is rapidly becoming a practical tool for improving efficiency, strengthening decision-making, and reducing the burden of repetitive work. However, successful adoption requires more than enthusiasm for new technology. It demands a deliberate approach that balances innovation with the financial controls, accuracy, and accountability that organizations depend on.

AI adoption in finance has moved beyond experimentation and into active investment. CFOs now face a practical question: where can AI deliver measurable business value without compromising governance, accuracy, or oversight?

The answer is not a company-wide rollout from day one. Finance leaders need a focused, phased strategy. The most effective starting point is a process that involves repeatable tasks, reliable data, clearly defined ownership, and measurable outcomes. By beginning with a targeted use case, finance teams can evaluate performance, identify risks, validate results, and build confidence before expanding AI adoption across other functions.

AI Is Moving into Core Finance Operations

CFO interest in AI is high. Deloitte reported that 87% of surveyed North American CFOs expect AI to be extremely or very important to finance department operations in 2026. The same survey found that 54% see AI agent integration as a finance transformation priority.

Adoption is already visible. Gartner reported that 59% of finance leaders were using AI in their finance functions in 2025. Among teams that had implemented AI, common use cases included knowledge management, accounts payable automation, and error or anomaly detection.

For CFOs, the question is no longer if AI belongs in finance. The real question is where it should be applied first.

Start With the Finance Process, Not the Software

AI performs best when it solves a defined operational problem. A CFO should begin by reviewing processes with high transaction volume, repetitive manual work, delays, or recurring exceptions.

Arthur Lawrence supports finance and accounting operations across source-to-pay, order-to-cash, and record-to-report workflows. These areas offer practical starting points for AI adoption because each process contains work that can be tracked against clear performance measures.

Finance Area	Practical AI Application	Metric to Track
Source-to-pay	Invoice classification, duplicate detection, payment exception review	Processing time, exception rate
Order-to-cash	Cash application, collections prioritization, dispute routing	Days sales outstanding, recovery rate
Record-to-report	Reconciliation support, journal review, reporting assistance	Close time, correction rate
FP&A	Forecast support, scenario analysis, variance summaries	Forecast accuracy, planning cycle time

A narrow pilot gives the CFO a better basis for investment decisions than a broad launch with unclear targets.

Treat Data Readiness as the First Investment

AI systems depend on the information they receive. Poor data quality can lead to weak recommendations, missed exceptions, and inaccurate outputs. Experts identified data literacy, technical skills, and inadequate data quality or availability as major barriers to finance AI adoption. Deloitte also found that 41% of early-stage finance teams cited legacy technology as an adoption barrier, compared with 31% of teams further along in implementation.

Before scaling AI, CFOs should ask:

• Are account structures consistent across business units?
• Is historical data complete and usable?
• Are access permissions limited by job function?
• Can the system trace each output back to an approved source?
• Is there a clear process for correcting errors?

This work may feel less exciting than a new AI launch, but it directly affects accuracy and trust. Small improvements in data quality, governance, and system reliability often have a greater impact on long-term AI performance than flashy new features. Without a strong foundation, even the most advanced AI models can struggle to deliver consistent results.

Measure Value from the Start

AI adoption should be tied to financial and operational results. Cost savings matter, but they are one part of the case. A reported 63% of surveyed finance departments had fully deployed and were actively using AI solutions. Yet only 21% believed those investments had delivered clear, measurable value. Only 14% had fully integrated AI agents into the finance function.

This gap matters. A CFO needs a baseline before the pilot begins. For an accounts payable project, that may include invoice processing time, cost per invoice, exception volume, late-payment penalties, and duplicate-payment rates. For forecasting, it may include forecast error, preparation time, and the time spent reviewing variances.

The business case should also include the cost of integration, employee training, data preparation, system monitoring, and human review. AI should be assessed as an operating investment, not a software purchase with an assumed return.

Put Controls in Place Before Scaling

Finance teams work with sensitive information. AI use can introduce data exposure, access-control failures, unsupported conclusions, and audit issues. Additionally, data privacy remained a leading concern, cited by 57% of finance teams further along in AI use and 44% of early-stage teams.

The National Institute of Standards and Technology provides a voluntary AI Risk Management Framework for managing AI-related risks. NIST also released a generative AI profile in July 2024 to help organizations identify risks linked to generative AI systems and select suitable actions.

CFOs should require clear rules for:

• Data access and user permissions
• Human approval for material decisions
• Audit trails and source records
• Accuracy testing before release
• Escalation procedures for unusual outputs
• Vendor review and contract terms
• Periodic performance checks after launch

AI can support finance decisions. It should not create a black box around them. Transparency, explainability, and human oversight remain essential, particularly when financial outcomes affect strategy, risk, and stakeholder trust.

Keep Human Review Where Judgment Matters

Finance automation should reduce manual work while keeping people involved in decisions that require context, accountability, or professional judgment.

AI can classify invoices, highlight unusual transactions, summarize reports, and support forecasts. A finance professional should still review material journal entries, accounting estimates, policy exceptions, regulatory filings, and high-impact recommendations.

This approach gives finance teams time for analysis and business planning while maintaining oversight. It also helps employees build confidence in the system through practical use.

Build a Phased Adoption Plan

A practical finance AI plan can follow four stages:

1. Select one workflow with a clear problem and measurable baseline.
2. Run a limited pilot using approved data and defined review rules.
3. Compare results against cost, accuracy, time, and risk measures.
4. Expand the system only after the pilot meets the agreed standard.

CFOs do not need to chase every AI development. They need a disciplined adoption plan linked to finance priorities. The goal is not to implement AI for its own sake, but to deploy it where it can deliver measurable improvements in forecasting, efficiency, risk management, and decision-making.

Conclusion

AI can help finance teams automate repetitive tasks, enhance reporting accuracy, and respond more quickly to operational challenges. However, achieving meaningful results requires a focused and strategic approach. Rather than attempting large-scale implementation from the outset, CFOs should begin with a specific use case, ensure the underlying data is reliable, establish clear governance and review processes, and measure outcomes before expanding adoption.

A phased implementation strategy provides greater control over cost, accuracy, compliance, and risk. By combining clear objectives with strong human oversight, finance leaders can use AI to strengthen decision-making, improve operational performance, and deliver measurable business value while maintaining accountability at every stage.

At Arthur Lawrence, we help organizations streamline finance operations, strengthen financial controls, and identify practical, high-impact opportunities for AI adoption. By aligning technology with business objectives, we help finance teams improve efficiency, enhance decision-making, and create a foundation for sustainable growth.

Are you ready to modernize your finance function? Contact us today to explore how the right finance and accounting strategy, combined with responsible AI implementation, can improve performance, reduce risk, and support your long-term business goals.