Artificial intelligence has already become routine in the products and services that tech companies deliver to their customers.
But there is a curious detail in this whole story: while these companies teach the market how to use AI to optimize processes, many of them still ignore their own internal finance departments.
It is almost a paradox, right?
The ones selling the future are still living in the past when it comes to internal financial management.
And it is not just a feeling.
EY brought data that shows exactly this gap: automation in finance at tech companies is still fragmented, full of data stuck in silos, and lacking a clear ownership strategy for AI.
The result?
A model that EY itself calls RPA 2.0 — in other words, automation of parts of the process, but never the process as a whole.
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The good news is that this is changing, and the transformation coming to corporate finance promises to be much deeper than any system upgrade you have ever seen out there. 🚀
The Problem Nobody Wants to Admit in Corporate Finance
There is a huge irony in the tech market today. The same companies that develop artificial intelligence platforms for their clients to automate workflows, reduce operational costs, and make faster decisions continue to operate internally with fragmented, manual, and rework-heavy financial processes. It is no exaggeration to say there is a chasm between what these organizations sell and what they actually practice when it comes to managing their own money. And this chasm has a real, measurable cost that shows up in the speed of accounting closes, the quality of reports, and the ability to react quickly to market changes.
The data gathered by EY sheds light on something many CFOs and finance directors already notice in their day-to-day work but struggle to address clearly. The automation that exists today in most corporate finance departments is piecemeal, disconnected, and built on top of traditional RPA logic that automates repetitive tasks but does not transform the end-to-end process. It is like strapping an electric motor onto a bicycle and thinking you now have a car. The structure stays the same, just with a little extra push. The real problem lies in the architecture of the process, not just in the tool that handles one specific step of it.
As Adam Blaylock, EY Americas Technology Sector Leader for Financial Accounting Advisory Services, put it well, most technology leaders can describe exactly what the future of AI in finance will look like. The real challenge is getting from where they are today to that future in a careful, value-driven way, because building a reimagined finance function is not a simple task. Companies of all sizes can benefit from partnering with someone who understands the nuances of finance and regulatory reporting.
Another critical point identified is the issue of siloed data. Inside a mid-size or large tech company, it is very common to find ERP systems that do not talk to business intelligence platforms, which in turn do not integrate with financial planning tools. Each department handles its own piece, generates its own reports, and delivers a partial view of reality. When someone in the C-suite needs a consolidated view to make a strategic decision, the process of pulling it all together can take days or even weeks, depending on how complex the operation is. In a market where speed of decision-making is a competitive advantage, this is a problem too serious to ignore.
Reimagining Finance with Agentic AI
Some tech companies are taking a renovation approach to AI in finance, developing point solutions focused on specific use cases like extracting data from invoices or detecting anomalies. That does help make processes more agile and reduce costs, but it does not truly change the game in terms of structure, and it certainly does not tap into the full power that artificial intelligence has to genuinely move the finance function forward.
The more complete, long-term strategy is to rethink the finance function in an AI-centric world, using an approach that starts from scratch. In practice, this means redesigning the process by focusing on the outcomes you want to achieve and how those outcomes could be reached without human intervention. Only after that do you bring the person back into the workflow, but only where they are truly indispensable — not simply because finance has always done it that way.
EY professionals believe that for tech companies, it is worth prioritizing internal development around order-to-cash (OTC) processes and financial planning and analysis first. The reason is that OTC is precisely what makes these companies unique in the market, and they would be hard-pressed to find off-the-shelf AI solutions already built for it. On the other hand, processes like procure-to-pay (PTP) and record-to-report (RTR) tend to be more straightforward at a tech company, and it may even make sense to wait for software vendors to embed AI into those products.
According to Amanda Donohue, a principal in financial consulting at Ernst & Young LLP, the important thing to remember is that there is no one-size-fits-all solution. Companies need to take a deep look at their internal processes and the technology that supports them before moving forward. The key is to invest in reinventing the most critical and complex processes rather than waiting for someone else to reinvent them for you.
How Artificial Intelligence Is Redefining Efficiency in Finance
The arrival of a new generation of tools based on generative artificial intelligence and large language models is completely changing the logic of how financial automation can work. Unlike traditional RPA, which follows rigid scripts and breaks at the first variation in the process, modern AI systems can interpret context, handle exceptions, learn from historical patterns, and even anticipate future scenarios based on real-time data. This represents a real paradigm shift, not just an incremental evolution of the tools that already existed.
In a fully agentic model, financial work splits into two categories. On one side are routine operational tasks, managed by AI with human oversight. On the other are higher-level tasks, driven almost entirely by people. As time-consuming operational activities get handled seamlessly in the background by AI, finance teams can operate with fewer people dedicated to repetitive tasks and more focus on deliverables that truly add value.
A practical example helps illustrate this. Today, a financial planning and analysis professional manually gathers data before doing any analysis, often pulling information from several different systems. With agentic AI, that data arrives assembled and ready automatically, right when it is needed. The AI even delivers a first draft of the analysis, freeing the analyst to focus on deeper insights and act as a true business partner in important decisions about the path forward.
In many industries, artificial intelligence is already transforming the entire lead-to-cash process — from creating personalized marketing campaigns based on customer data to contract management, credit management, billing, collections, order fulfillment, and customer support. Across all of these stages, agentic AI uses predictive analytics to better understand customer preferences, payment risks, and the likelihood of renewal or repurchase. The result is a highly automated process that boosts sales, handles complex contracts, reduces the risk of default, and improves cash flow.
EY points out that organizations moving in this direction are adopting an approach that goes beyond simply implementing new tools. They are rethinking the finance operating model as a whole, clearly defining who is responsible for data governance, how artificial intelligence will be supervised within processes, and how teams will be trained to work side by side with these systems. When properly designed and implemented, agentic AI improves the accuracy of financial data because it eliminates human error and opens up more time for review and analysis.
It is worth highlighting an important caveat here. Although AI makes it much easier for employees to write code and create automated processes, there is no guarantee that these tools are delivering accurate data or correct answers. That is why organizations increasingly need to implement AI validation tools to monitor the performance of their own proprietary models and maintain confidence in the numbers.
The Transformation That Is Coming and What It Demands from Companies
The word transformation is used so frequently in the corporate world that it sometimes loses its impact. But when we talk about transforming corporate finance with artificial intelligence, the term does justice to what is actually happening. This is not about swapping one system for another or adding a chatbot to the internal support process. It is about redesigning how the finance function operates, how data flows within the organization, and how decisions are made at every level of the company. It is a structural change that affects culture, technology, processes, and people all at the same time.
For tech companies, the question is no longer whether it is worth using AI in finance. The question now is how quickly they can move from fragmented automation to full transformation. And the numbers are encouraging. According to Blaylock, an end-to-end agentic AI implementation can generate cost savings of up to 40% while delivering better financial insights. And just as importantly, by focusing on an AI-first delivery model and an outcomes-based strategy, it is possible to transform the finance function in a matter of months, not years.
The Challenges Still Holding Back Adoption
Even with all this potential, finance leaders at tech companies still face three very common concerns that have been slowing down AI adoption. The first is deciding which AI tools to invest in, given the massive volume of options available on the market. The second is assessing whether the team is truly prepared to work with AI and figuring out how to upskill or motivate people to embrace this change. And the third is understanding the best way to integrate AI into major enterprise system upgrade projects.
These are legitimate obstacles, but each one points to a bigger need — rethinking the future of the finance function. More specifically, how the work will be divided between AI agents and employees and how the finance department should be structured once that integration is fully up and running.
The first challenge in practice is data quality. No artificial intelligence model is going to perform well if it is fed inconsistent, outdated, or poorly structured information. Before even thinking about implementing any AI solution in finance, it is essential to invest in cleansing, standardizing, and integrating existing data sources. This foundational work is what will determine whether the project actually takes off or stalls in the first few months of operation.
The second major challenge is human. Finance teams that grew up in an environment of spreadsheets, manual processes, and legacy tools need time, support, and training to adapt to a new way of working. The efficiency that AI brings is not instant — it builds over time as teams learn to interpret model outputs, identify when the system is right and when it needs correction, and use the generated insights to make better decisions. Companies that understand this and invest in upskilling their people see far more consistent results than those that assume technology solves everything on its own. 💡
The scenario taking shape for the coming years is a clear divide between companies that managed to integrate artificial intelligence strategically into their financial operations and those that kept patching old processes with point solutions. Operational efficiency, close speed, forecast quality, and the ability to respond quickly to market changes will be increasingly important competitive differentiators — and those differentiators will be built, in large part, by how each organization decides to approach this transformation right now.
What is clear is that the time to act is now. The tools are mature, the use cases are proven, and the benefits are tangible for anyone willing to go beyond the pilot and build a real journey of finance transformation with AI. 🎯
