Tech CFO Says Finance Leaders Are Misunderstanding the Financial Impact of AI
See how finance leaders are using AI to automate workflows, improve accuracy, and reduce manual work, while keeping humans responsible for judgment and oversight.
Ashok Manthena
CFO and Chief Finance AI at ChatFin.ai
AI Execution: Finance teams should focus on integrating AI into operations beyond mere analysis to enhance execution.
Gradual Adoption: CFOs must implement AI gradually to avoid disrupting critical finance operations in large organizations.
Cost Impact: AI can reduce costs and improve efficiency, but leaders must prioritize business continuity over quick savings.
AR Workflow: AI agents streamline accounts receivable processes, making them more efficient and largely touchless.
Judgment Role: Budgeting and forecasting remain reliant on human judgment, as AI cannot fully automate these functions.
Ashok Manthena is the CFO and Chief Finance AI at ChatFin.ai. He's also an author and researcher focused on applying AI to finance operations.
In this interview, Ashok shared how finance leaders must approach AI transformation in their organizations.
Moving Beyond AI Analysis to AI Execution
I’m Ashok Manthena, a researcher and practitioner focused on applying AI to finance operations. My work spans core finance functions, including AP/AR, controller processes like month-end close, FP&A, and tax.
Want more from The CFO Club?
Create a free account to finish this piece and join a community of modern CFOs and finance executives accessing proven frameworks, tools, and insights to navigate AI-driven finance.
Have an account? Log In
I have worked with large enterprises throughout my career, and today I work with a wide range of companies — from mid-sized organizations to businesses with several hundred million to over a billion dollars in revenue. These organizations vary across industries, technology stacks, and ERP systems, which adds more layers of complexity — especially regarding cybersecurity.
My focus is on how AI can move beyond analysis into execution, automating workflows, improving accuracy, and enabling finance teams to operate with greater speed and control.
Why CFOs Must Push for Gradual AI Adoption
Many CFOs — especially in mid-to-large companies, though this applies more broadly as well — assume their AI transformation will be a big-bang shift, where AI quickly takes over processes from day one. That’s not practical.
In larger organizations, finance operations are critical to running the business and cannot be disrupted. As a result, AI adoption must be much more deliberate and gradual than people expect. Every CFO should recognize this.
In any transformation, the human element is critical. Resistance to change and proper change management require serious attention — and this is especially true with AI transformation. Ultimately, a human remains responsible for the output; someone is always accountable for it. Therefore, we must not overlook or treat casually the human aspect of AI adoption.
A top-down approach works best. When finance leaders actively drive AI adoption, identify where teams are spending effort, and push for targeted improvements, adoption accelerates. That leadership-driven focus on operational bottlenecks makes AI initiatives give results.
How AI Affects a Company's Bottom Line
When implemented thoughtfully, I've seen the benefits of AI on multiple fronts.
There has been significant cost reduction, as well as reduced dependency on manual resources, because many processes now run automatically.
And beyond that, we've seen a more meaningful shift — people and teams can now do things they previously couldn't due to lack of time or bandwidth. Instead of focusing only on routine tasks, they are spending more time on work that directly impacts the company’s bottom line.
With that said, I think many people misunderstand the financial impact. The initial cost savings from automation show up quickly, but they plateau just as fast. What matters more is protecting business continuity, minimizing the risk of disruption, and improving overall business performance. In large finance organizations, even small disruptions can have outsized financial impact, so slower, controlled adoption preserves value better than aggressive cost-cutting.
As teams experiment, they identify which use cases truly drive ROI — not just by reducing effort, but by improving accuracy, control, and decision-making. That’s where the real financial upside comes from.
What matters more is protecting business continuity, minimizing the risk of disruption, and improving overall business performance.
How AI Fundamentally Changes AR Workflows
I’ve been a part of transformations across nearly all CFO functions.
Take AR inbox management as an example. Traditionally, teams spend significant time communicating with customers, answering queries, and following up on outstanding invoices. While earlier methods automated parts of this, AI agents have made it much simpler and far more effective.
The workflow begins when the agent reads invoices, identifies due and upcoming items, and applies your policies and payment terms. It automatically sends reminders for overdue invoices and periodic statements covering all open and upcoming balances. When a customer responds, the agent retrieves the relevant data, verifies the requester, and replies with the appropriate information.
We tested it over a few weeks to ensure accuracy before running it in production. Now, it's a largely touchless process, fundamentally changing how the AR workflow operates.
Why Budgeting and Forecasting Cannot Be Fully Automated
AI impacts budgeting less than other functions. It relies heavily on judgment, context, and business intuition. AI can assist with data gathering and parts of the budget workflow, but we haven’t fully solved how to effectively combine human inputs with AI-driven insights here.
The same goes for forecasting. It remains a calculated judgment rather than a fully automated outcome, and I believe it will continue to be that way for some time.
At a consolidated finance level, it’s difficult to rely purely on machine learning because the signal is often limited, and predictions may not be reliable enough. This is where human judgment still plays a critical role — understanding the business context, nuances, and making informed assumptions.
So, for budgeting and forecasting, we rely on structured, parameter-driven models using known variables and business logic, so that outputs are deterministic and easy for finance teams to validate, rather than purely ML-driven forecasts. And we rely on humans-in-the-loop.
For the rest of the finance operations, like reconciliations, AP, AR, reporting, and close activities, we use AI agents. These handle data ingestion, matching, communication, and workflow execution.
Why AI Is Not Changing the Analytics Process
AI has not delivered the impact I was expecting in analytics, either.
AI has made these processes faster, but it hasn’t consistently produced fundamentally new insights beyond what finance teams were already deriving.
The outputs are often better packaged and quicker to generate, but the core understanding of the business hasn’t materially changed — at least for us.
How finance will shift in the coming years
Moving forward, AI will change finance tooling from a collection of separate tools to more unified, integrated systems. New software categories will emerge, and this shift will change how organizations design finance systems and use technology. Organizations need to start thinking about it now.
AI is changing finance, not through a single shift, but across workflows, teams, systems, and decision-making.
We are still very early in the AI transformation journey, and many unknowns remain, especially in areas like capital allocation. So, while AI can now perform many tasks autonomously, my advice is for finance leaders to hold onto final judgment — grounded in business knowledge, industry context, and experience.
Follow Along
You can follow Ashok Varma's journey on LinkedIn.
More expert interviews to come on The CFO Club!
