Can Agentic AI Improve the Finance Function?

Today’s finance teams are under pressure to deliver fast, accurate reports and forecasts to help their businesses stay competitive. Yet the use of legacy spreadsheets and processes and fragmented data and communication trails often hold finance teams back.

Finance trends suggest that many business leaders are keen to adopt advanced scenario planning technologies and more agile governance frameworks when they help speed up decision making, especially amid ongoing economic uncertainty. To me, this translates to an expectation of proactivity and access to rolling insights, not backward-looking, manual processes.

So, is the solution turning to agentic artificial intelligence (AI) to help modernize and automate the finance function and free more time for actively working on valued strategy and analysis? I say yes.

How Agentic AI Improves Planning, Forecasting, and Decision Making

Finance teams using AI-powered tools are reporting faster cycle times and more responsive forecasting, along with boosted business efficiency, competitiveness, profitability, and productivity. Here are some of the ways finance teams can use agentic AI to help them move toward providing greater decision-ready insight to business leaders:

• Rolling forecasting: Finance teams can use agentic AI to pull and stream data from invoices, expenses, and other banking feeds in real time, consolidating it into forecasts that refresh continuously. By doing so, teams can reduce extensive, manual batch processing of banking data, freeing personnel to focus on high-value tasks. Delegating data pulling and forecast building to AI could also mean having access to reliable, relevant data far ahead of deadlines and board discussions.
• Faster, more in-depth scenario building: AI-augmented finance teams can build scenarios in a matter of minutes or hours, with immediate access to thousands of data points and contexts. Not only is AI-driven scenario building more efficient, it also allows finance teams to produce more hyper-specific scenarios, allowing for more nuanced decision making and greater confidence in the face of risk.
• On-demand gap and variance analysis: AI-supported finance personnel can explore the deeper, root causes of data divergence without the need for manual review. Teams can trace specific drivers for variance and build detailed but accessible executive summaries and explanations. For example, agentic AI can find fluctuations in foreign exchange rates, volatility in the costs of raw materials, or even duplications in licenses or inventory items that are driving up costs unnecessarily.

Guardrails, Groundwork, and Auditing Considerations

Importantly, finance teams using agentic AI should set guardrails and review points to ensure outputs are appropriately governed, keeping processes audit-ready and compliant. For instance, many finance teams set “human-in-the-loop” review controls to ensure that AI-handled journal entries are recorded in line with data retention requirements.

Keeping humans in the loop ensures that AI is both held accountable and continuously adjusted to meet the team’s expectations and auditing needs. One solution that could help with this is Glassbox, an AI-powered analytics platform, which provides data that can be automatically logged alongside actions taken.

Notably, auditors look for input-to-output traceability, with clear records of prompts, parameters, data sets, and models used to come to specific decisions and figures. They also expect clear evidence of AI control and guardrails, such as version updates, review checkpoints sewn into processes, and human-in-the-loop delegation (e.g., evidence of the latter includes proof of digital sign-off on AI decision making).

All in all, the most effective finance AI rollouts carefully test AI’s impacts on one key process at a time, so it can be measured and adjusted gradually. This process reduces operational overwhelm and downtime and gives personnel time to build new AI management capabilities. However, it’s important for teams to be aware that these processes are gradual; an effective rollout can take months depending on company size and workflow scope.

However, with the right guardrails, review processes, and human oversight, agentic AI does more than reduce high-volume, manual work. It gives finance teams more room to do the work that matters: analyzing variances, modeling scenarios, and advising business partners. The benefits aren’t theoretical or on the horizon—they’re being delivered today, and they’re changing how finance teams plan, forecast, and lead.