Preparing your finance organization for the new era of AI

Finance and accounting teams have long used automation, including robotic process automation (RPA) for data entry and rules-based software for routine reporting. But AI for finance and accounting has moved beyond these applications. Today, AI-powered features like transaction matching and anomaly detection are embedded in the enterprise platforms some finance teams already use, handling tasks that once took hours of manual work.

The adoption numbers back this up. Robert Half research shows 92% of finance and accounting leaders are already using AI capabilities built into their existing systems. Across all functions, 88% of managers say their team is currently using AI, and 85% of current users are increasing their investment this year. With adoption this widespread, the gap that matters isn't between users and non-users, but between teams that get real value from AI and those that have barely scratched the surface.

This article can help you land on the right side of that ledger. Here's what AI for finance looks like right now and the steps you can take to make sure your organization is ready for it.

AI for finance and accounting: What you need to know in 2026 What AI for finance and accounting looks like in practice 6 tips to prepare your team for AI in finance and accounting 1. Define where AI adds the most value 2. Prepare a strong data foundation 3. Assess the supply of available AI skills 4. Decide whether to build, buy or partner 5. Manage the change carefully 6. Stay curious—and risk aware

What AI for finance and accounting looks like in practice

The benefits of AI in finance are most evident in the repetitive tasks that traditionally consume weeks of staff time each quarter. AI-powered reconciliation tools in platforms can match thousands of transactions in minutes, flagging only the exceptions that need human review. Expense management platforms use machine learning to automatically detect duplicate submissions and policy violations.

In financial planning and analysis (FP&A), AI for finance and accounting speeds up variance analysis and forecasting. Tools can pull from historical data to surface patterns a human analyst might miss—a seasonal dip in receivables that's been masked by overall revenue growth, for example. Instead of spending a full day preparing a variance report, an FP&A analyst might spend an hour reviewing and refining one that AI has already assembled.

Generative AI, now built into many platforms, takes this further.

Finance professionals can use it to summarize lengthy audit documents or draft narrative explanations of budget variances from raw data. Recent advances in AI-powered design mean you can also turn a spreadsheet or quarterly report into a polished slide deck in minutes. The technology handles the initial assembly; the professional provides the judgment and context.

The next, but surely not final, frontier is so-called agentic AI—autonomous systems that can handle multi-step finance workflows with minimal human oversight. It's still early, but some finance teams are already piloting agents for specific processes. Treasury teams, for instance, are using AI agents to monitor cash positions across accounts and flag short-term investment windows as they open. Others are testing planning agents that continuously adjust forecasts as new revenue or cost data comes in, rather than waiting for a quarterly refresh. Agentic AI points to where automation in finance is heading: away from single-task tools and toward systems that can coordinate entire workflows on their own, with humans stepping in for oversight and final approval.

6 tips to prepare your team for AI in finance and accounting

You won't see the full benefits of AI in finance without a thoughtful rollout that accounts for the people it touches: your workforce and your clients alike. Get it wrong, and the best-case outcome is a rocky implementation that drags on for months. The worst case is far more serious: AI that puts your firm's data security and your clients' at risk.

Taking these steps can help your organization achieve AI readiness:

1. Define where AI adds the most value

Start by identifying the specific workflows where AI could save the most time or reduce the most errors. If your month-end close takes 10 business days and half of that goes to manual reconciliations, that's a strong candidate for finance automation. If your accounts payable team spends hours matching invoices to purchase orders, AI-powered matching can handle routine items, freeing your people for exception handling.

Focus on problems first, then find tools that solve them. Buying a platform without a clear use case often creates more technical debt than productivity gains.

2. Prepare a strong data foundation

Everyone has heard of the “garbage in, garbage out” risk with AI, and there’s no question that data quality and integrity are must-haves when working with the technology. However, there are several questions you’ll want to address to confirm you have the right data to build and evolve your models:

What type of data do we need?

Where is that data located?

Is it ready to use now, and is it scalable?

You'll also need clear data governance policies that define who owns the data and who can access it. Without these guardrails, even a well-chosen AI for accounting tool can produce misleading outputs or expose your organization to compliance risk.

3. Assess the supply of available AI skills

Only 35% of workers say they feel very confident using AI tools effectively. That confidence gap is an opportunity for finance leaders willing to invest in structured training, such as hands-on workshops with the specific tools their team will use.

Pair formal training with low-risk experimentation. Let your FP&A team test an AI-powered forecasting feature on a non-critical budget cycle before relying on it for the annual plan. If you need specialized expertise quickly, skilled consultants can bridge the gap while your permanent team builds its capabilities. Employees who learn to work confidently with AI often become strong advocates for further adoption across the department.

4. Decide whether to build, buy or partner

How you roll out AI for finance and accounting depends on what you want to do and what you can do. Building custom AI infrastructure gives you more control but requires data science and engineering talent you may not have on staff.

Buying AI technology off the shelf can be an appropriate strategy if you aim to experiment with the technology on a limited basis, or if you have a specific use case that a proven product available in the marketplace can readily address—or when you want to experiment before making a larger commitment. Subscribing to an AI writing and analysis tool, adding AI features to your existing accounting or ERP platform, or deploying a purpose-built solution for invoice matching or anomaly detection are all examples of this approach. You're licensing a ready-made product, deploying it with minimal customization, and absorbing updates as the vendor releases them.

Partnering with an AI provider is a different kind of engagement. Rather than simply purchasing access to a product, you're entering a working relationship in which the provider helps you integrate AI into your systems and workflows at scale. This route makes the most sense when your use cases are complex or organization-specific, when you want implementation support and ongoing expertise, or when you're ready to move beyond experimentation toward enterprise-wide adoption.

5. Manage the change carefully

Introducing new, transformative technologies like AI into your workplace can be very disruptive. Managing that disruption thoughtfully—communicating clearly, involving staff early, and supporting people through the transition—is where change management is strongly called for. If change management is lacking, you risk creating confusion, alienating (and potentially losing) valued staff, undermining employee morale, and ultimately underutilizing your technology investment.

That includes investing in people and business processes to help your AI initiative succeed. Create and communicate a well-defined plan for how you intend to bring advanced AI tools into your organization and use them for transformation. Share details about when and how new tools will be implemented, what those tools are designed to do, and how those new capabilities will benefit employees and provide the business with a competitive advantage.

Most employees want to see that their employers are committed to continuous improvement and prioritizing investments that can help make the best use of everyone’s talents and reduce wasted time and work. If they understand what they need to learn, how they will learn it, and how the organization intends to measure success and collect and act on feedback, they are more likely to have a positive experience with the AI-driven change you want to implement.

6. Stay curious—and risk aware

AI for finance and accounting is reshaping how teams close books and build forecasts. But AI outputs still require human judgment, particularly in a field where accuracy and compliance are non-negotiable. Generative AI tools can produce confident-sounding narratives that contain factual errors, so every AI-generated analysis needs to be reviewed by someone who understands the underlying numbers.

Finance leaders should also seek input from compliance and IT security before adopting new AI tools. These perspectives can surface data privacy and regulatory risks that a finance-only evaluation might miss.

A strong first step: pick one or two high-impact workflows, pilot an AI tool with clear success metrics and build from there. Organizations that treat AI as a gradual, well-managed evolution are the ones most likely to see lasting benefits of AI in accounting.

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