From budgeting and forecasting to tax compliance and reporting, finance teams are under mounting pressure to modernize. In response, CFOs are increasingly turning to the rapidly growing ecosystem of AI tools designed to automate tasks, extract insights, and accelerate decision-making.

This is reflected in spending: investment in AI across the financial sector is expected to hit 45 billion U.S. dollars in 2024, up from 35 billion in 2023. Banking institutions are leading the charge, increasing their AI investment from 20.65 billion to 31.3 billion dollars in just one year.

But as early adopters are discovering, not all AI tools are created equal, and simply buying into the hype isn't enough. To achieve meaningful outcomes and ROI, financial leaders must go beyond surface-level promises and focus on selecting the right tools for the proper use cases. That means making decisions based on a clear understanding of team capabilities, data quality, operational readiness, and strategic priorities.

When navigating this complex terrain, the best insights often come from those who have already taken the first steps.

AI Tools in Finance: An Ever-Expanding Landscape

Today's finance AI ecosystem includes a wide range of tools, each built with a different engine under the hood.

Traditional AI tools are typically designed for structured data, like ERP exports, spreadsheets, or general ledger transactions. This category of tools is used for predictive analytics, anomaly detection, and optimization.

Generative AI tools, on the other hand, work with unstructured data such as policies, emails, and meeting notes. These models help generate narratives, extract key points from documents, and support contextual decision-making.

The most powerful approaches increasingly combine the two, layering predictive models with natural language generation to support use cases like forecasting commentary or automated management reporting.

These advancements are shaping the market for AI tools across domains like accounting, FP&A, compliance, and audit. Vendors now offer a broad spectrum of AI tools for finance, many tailored to common workflows, from transaction classification to narrative report generation.

What Finance Leaders Are Actually Buying

According to Gartner's 2025 Finance Software Investment report, CFOs are not just exploring AI; they're actively budgeting for it. Over half of planned AI and machine learning tool purchases in 2025 are categorized as entirely new investments. Generative AI tools are close behind, with 46% of planned purchases labeled "new." This reflects a bold shift from legacy systems to more flexible, AI-augmented platforms.

Finance leaders are looking for scalable tools that integrate smoothly into broader ecosystems. Automation, data analytics, and reporting are central priorities, with a clear focus on platforms that offer embedded AI capabilities rather than disconnected solutions.

These trends signal a broader shift, where AI tools in accounting and finance are moving beyond experimentation toward enterprise-level adoption. The focus is on delivering real, measurable impact rather than just ticking a box to showcase technological innovation.

AI Use Cases That Are Gaining Ground

AI use cases in finance are numerous, and they're expanding rapidly. The 2024 KPMG Global AI in Finance Report highlights how forward-thinking companies are pushing the boundaries of what's possible. For example, a Canadian bank is combining AI with blockchain to enable secure, transparent financial transactions, while a French logistics firm is using AI-powered adaptive pricing algorithms to optimize pricing in real-time based on shifting market trends.

However, the most widely implemented AI use cases aren't necessarily the most strategic ones. Instead, many organizations start with applications that are easy to pilot. For example, coding assistance is currently the most common generative AI deployment in finance.

More strategically, many organizations are also turning to AI to support tasks like forecast and budget variance explanation, spend classification, and commentary generation for reporting packages. These use cases blend structured and unstructured data processing, highlighting the increasing value of composite AI solutions.

According to Gartner, finance teams are also applying generative AI to compare contracts and invoices, summarize policy documents, and prepare for stakeholder meetings. These use cases are saving time, enhancing accuracy, and freeing up staff to focus on more strategic work.

Adopting AI Tools Doesn't Guarantee ROI

Despite the rapid increase in adoption, many finance leaders are finding that the return on investment is harder to quantify than expected. In fact, only 14% of finance teams that have implemented AI tools report being able to measure ROI concretely.

While many CFOs hoped to see clearer cost reductions or headcount savings, the benefits experienced are often more nuanced, with faster access to insights, more consistent compliance, and better employee experience.

The key takeaway is that tools by themselves aren't transformational. Success comes from embedding those tools into well-defined processes supported by skilled people who understand how to use them. The most effective AI tools for finance professionals are those that enhance, rather than replace, human judgment.

What's Getting in the Way?

Implementation remains a major hurdle for many finance teams. Gartner's research points to a handful of recurring obstacles. Talent shortages are one of the most cited concerns, particularly around generative AI literacy and data fluency. Poor data quality and fragmented systems also limit the impact of even the most advanced AI models. Without strong change management and governance, adoption often stalls before delivering measurable results.

In many organizations, there’s a disconnect between those who purchase AI tools and those expected to use them. When finance teams lack ownership or confidence in the technology, adoption remains superficial, no matter how capable the software may be.

A Smarter Way to Evaluate AI Tools

To avoid misaligned investments, finance leaders are beginning to adopt more structured evaluation frameworks. One such model, outlined in Gartner's Finance AI Use Case Prioritization research, emphasizes three core criteria: net business value, feasibility, and scalability.

This approach encourages CFOs and their teams to score and compare AI tools not just by their capabilities but by how well they align with business goals, how easy they are to implement, and how far they can scale across the organization.

For example, a spend classification tool might offer clear short-term value and be relatively easy to deploy but have limited expansion potential. A forecasting assistant powered by generative AI might offer broader benefits but requires higher data maturity and workflow integration. Using this kind of rubric helps prioritize the right AI tools for accounting and finance: those that deliver the most value within the organization's current capabilities.

Final Thoughts: Better Questions, Smarter Tools

CFOs don't need more AI tools. What they need is better questions to ask before choosing them. What problem are we solving? Is our data ready? Who will own this? Can we measure success?

The real differentiator isn't the tech itself; it's the clarity of purpose behind it. Early adopters are proving that impact comes not from chasing features but from aligning tools with strategy, integrating them with intention, and empowering the teams who use them.

As AI adoption accelerates, the difference between ROI and regret won't hinge on what you buy—but on how and why you buy it.