How agentic AI can transform finance beyond automation in tech firms

Deploying agentic, workflow-driven AI can unlock major efficiencies, improve reporting confidence and reshape the operating model.

In brief

• Many tech companies under-utilize AI in finance due to siloed data and unclear ownership.
• Agentic AI enables end-to-end automation, reducing costs and improving scalability.
• A holistic, outcome-based approach to AI can deliver faster transformation and greater value.

Tech companies are driving the future with AI innovation for clients — but are they maximizing its use in their own finance functions?

The answer — at least at many companies — is “no.” AI in finance remains under-optimized across tech firms, driven by factors such as siloed data, unclear ownership of AI and challenges in change management.

The situation at many companies could easily be called “Robotic Process Automation 2.0” — in other words, a good deal of AI in finance today is simple automation of parts of the process, not the whole. There are plenty of solutions on the market looking for problems, but putting them together in a holistic manner takes a different mindset.

“Most tech leaders today can tell you what the future of AI in finance looks like,” said Adam Blaylock, EY Americas Financial Accounting Advisory Services TMT Industry and Technology Sector Leader. “The challenge is getting from today to tomorrow in a thoughtful, value-driven way, because building a reimagined finance function isn’t a simple task. Tech companies of all sizes can benefit from a partnership with a firm that has deep experience in finance and AI technology, one that understands the nuances of the finance function and regulatory reporting.”

Reimagining finance with agentic AI

Some tech companies are taking a “renovate” approach to finance AI, developing point solutions that focus on specific use cases, such as invoice extraction or anomaly detection. While this can streamline processes and reduce costs, it is not a game changer in terms of headcount, nor does it fully embrace the power of AI to move the finance function forward.

A more comprehensive, long-term strategy is to reimagine the finance function in an AI-centric world, using a “design for zero” approach. This means that the process needs to be reimagined to focus on the outcomes and how those outcomes could be achieved without human intervention, and then only putting humans into the process where absolutely required, not because finance has “always done it that way.”

EY professionals believe that for technology companies, focusing first on internal development around order-to-cash (OTC) and FP&A should be a priority, primarily because OTC is what makes them unique in the marketplace, and it’s unlikely they could buy off-the-shelf solutions that are AI-ready. However, processes such as procure-to-pay (PTP) and record-to-report (RTR) are typically more straightforward at a tech company, and it might make sense to wait until software providers for these processes embed AI into their products.

“The important thing to remember is that there is no ‘one size fits all’ solution and companies need to take a deep look at their internal processes and supporting tech before moving forward,” said Amanda Donohue, Principal, Finance Consulting, Ernst & Young LLP. “The key is to invest in reimagining your critical and complex processes rather than waiting for someone else to reimagine them for you.”

Simplifying finance: how it can work

In a full agentic AI model, finance work falls into two categories: routine operational tasks managed by AI with human oversight and higher-level managed tasks handled almost entirely by people. Because time-consuming operational tasks are performed seamlessly behind the scenes by AI, finance teams can manage their work with reduced headcount and a greater focus on value-added tasks.

For example, an analyst in FP&A today gathers data manually before doing any analysis, often pulling from multiple systems. With agentic AI, that data is already gathered and ready automatically as needed. AI delivers a first draft of analysis, freeing analysts to focus on deeper insights, allowing them to truly partner with the business to make critical decisions on the path forward.

In many industries, AI is transforming the entire lead-to-cash process, from generating personalized marketing campaigns based on customer data to contract management, credit management, invoicing and collections, order fulfillment and customer service. 

Throughout those steps, agentic AI uses predictive analytics to better understand customer preferences, payment risks and renewals or reorders; automates multiple processes that previously required staff time and input; and personalizes communication with prospects and customers to streamline lead generation, orders, invoices and payments. The result is a highly automated process that increases sales, manages complex contracts, reduces risks of nonpayment and increases cash flow. 

“We have a thorough understanding of the intricacies of reporting and regulatory compliance and respect for the values of accuracy and trust,” Blaylock said. “When properly designed and implemented, agentic AI improves financial data accuracy because it removes human error and allows more time for review and analysis. As a firm, we are always working with regulators to improve financial reporting processes and standards, and we believe AI has a major role to play today and in the future.”

Another value-add: While AI makes it easy for employees to write code and create automated processes, there is no guarantee that these tools are delivering accurate data or responses. Increasingly, organizations must implement AI validation tools to monitor proprietary model performance.

For tech companies, the choice is no longer whether to use AI in finance but how quickly it is to move from piecemeal automation to full transformation. 

“End-to-end agentic AI implementation can deliver cost savings of up to 40% while delivering better financial insights,” Blaylock said. “And just as important, when you focus on an AI-first delivery model and an outcomes-based strategy, you can achieve finance function transformation in months rather than years.”

Challenges to consider

Tech finance officers also are struggling with three common concerns that have delayed AI implementation: choosing which AI tools to invest in; assessing whether their workforce is ready to work with AI and determining how they can upskill or incentivize employees to embrace the change; and deciding how they can best integrate AI with major enterprise software upgrades.

These are legitimate hurdles, but each highlights the broader need to rethink the future of the finance function — specifically, how work will be divided between AI agents and employees and how the finance function should be structured once this integration occurs.

Summary

Tech companies often limit AI in finance to basic automation. A shift to agentic AI enables end-to-end workflows, scalability and compliance. When properly implemented, AI improves accuracy, reduces costs and frees teams to focus on higher-value tasks, accelerating transformation across the finance function.