A new kind of finance leader: The changing role of the CFO in an AI-empowered world

Not so long ago, most CFOs were known for being the steady hands on the wheel, keeping budgets in check, closing the books, and keeping surprises to a minimum. With the evolving role of the CFO, the job looks very different today.

AI is reshaping what it means to lead a finance team. It’s changing how companies forecast growth, structure pricing, and even build their finance orgs from the ground up. And CFOs are right in the middle of it all.

Today's finance leaders aren’t just reporting on what happened. They’re also helping teams plan for what’s next, often with AI tools that bring faster insights, smarter decisions, and new ways of thinking about value. AI is drastically changing how CFOs lead.

This article discusses what the changing role of the CFO looks like and how the next generation of CFOs is being shaped by the tools and decisions AI makes possible.

From data stewards to AI leaders: Changing role of the CFO

The finance function has always been grounded in data. CFOs have been tracking revenue, managing risk, and crunching numbers for decades. But, as AI becomes more ingrained across the business, CFOs are stepping into a very different role. 

This shift has given rise to what many are calling the “intelligence model”. This model is a new framework for how CFOs lead in the age of AI. It positions finance leaders as the ones who connect insights across the company, helping teams move from looking backward to looking forward.

This model means CFOs start to lead AI adoption from the inside out by asking better questions, creating better workflows, and building teams that can turn raw data into clear, confident decisions.

In practice, this shows up in a few ways:

• Insight generation: AI-powered FP&A tools help CFOs see trends earlier, flag anomalies faster, and explore “what if” scenarios on the fly. 
• Risk stewardship: Predictive models alert finance teams about their cash runway, potential compliance concerns, or pricing gaps before they show up in the P&L. 
• Data governance: CFOs are taking the lead on keeping enterprise data clean and consistent. Today, CFOs are setting the rules, choosing the tools, and making sure AI runs on complete and accurate information.
• Board fluency: Strong CFOs turn AI discoveries into clear strategies. They help boards and CEOs see where the business is going and what needs to happen to get there.

Key industry findings: What research and surveys reveal

If you’ve been following the finance headlines, you’ve probably noticed AI in FP&A is no longer an “emerging” technology for finance leaders. Let’s look at some research findings and see what they mean for you.

AI adoption is rising, but maturity is low

McKinsey found something interesting in their latest State of AI report: 78% of companies now use AI somewhere in their business, and 71% regularly use generative AI.

This sounds impressive until you look a little closer. Only about one percent of companies actually describe their gen-AI programs as "mature." More than 80% say they're not seeing any real impact on their bottom line yet.

‍IBM's finance study backs this up. Nearly 70% of CFOs say AI is central to their transformation strategy. But fewer than 30% are actually running or optimizing AI in their key processes.

The reality is that AI in finance is still pretty young. Most CFOs are running pilots for forecasting, anomaly detection, and maybe some reconciliation automation. Very few have made AI part of their everyday operating rhythm.

Skills gaps and readiness challenges

When Deloitte asked CFOs about their concerns for getting teams ready for generative AI, three things emerged as top concerns based on percent of CFOs that responded: 

1. Technical skills topped the list, with 65% of CFOs identifying it as an issue. This makes sense because most finance people didn't go to school expecting to work alongside AI.
2. AI fluency came in second, with 53% of CFOs surveyed citing it as a top concern.  This is more about understanding what AI can and can't do, when to trust it, when to question it.
3. Adoption risk was cited by 30% of respondents, making it the third top concern.

What's really interesting is that the challenge isn't only about hiring AI finance professionals. It's about making sure your existing finance team can work efficiently with AI tools and actually translate what the AI spits out into business decisions.

Data quality and integration issues

CFOs know that AI is only as good as the data it ingests. And for many, data quality is a big issue hindering their use of AI. In its early 2025 survey on the strategic use of AI in finance, analyst firm BARC found that 34% of respondents felt their data architecture was generally unsuitable for AI applications and their data foundation did not support the integration necessary to support it. More specifically, about the same number (33%) cited data availability, reliability, or quality concerns as limiting factors.  

These results ring pretty true. Walk into most finance departments and you'll find the usual suspects:

• Legacy systems that don’t connect
• Siloed databases
• Inconsistent data formats and definitions
• Important info trapped in countless spreadsheets
• Poor data governance and a lack of ownership for data quality
• Manual data entry errors
• Lack of real-time visibility

This fragmentation makes it nearly impossible for AI to produce reliable results. Garbage in, garbage out—it's an old saying, but it's never been more relevant. 

Strategic priorities: Governance, ROI, and skills

As you bring AI into your organization, you need to make sure your investments deliver measurable ROI. Here are four strategic priorities for every finance leader to effectively  manage risk and build a team that can successfully leverage AI systems.

1. Build AI governance and risk management frameworks

Nobody wants to be the CFO who turned AI loose and created a compliance disaster. But you also don't want to kill innovation with a million approval layers. Most CFOs already know how to manage risk. You do it with financial controls every day. Apply the same thinking to AI. Build safeguards into your AI workflows from the start. This includes:

• Implementing strong cybersecurity measures
• Monitoring AI outputs to prevent risks
• Ensuring teams can interpret results and question them when something looks off

The idea is simple. You need to design governance models that can scale. Yet you don’t need to create so many processes that nobody wants to use the tools. The goal is to balance innovation with security while ensuring AI delivers measurable business value.

2. Start building the data foundation you need

The smartest CFOs we've seen are treating their data quality and integration issues as a strategic problem, not just a technical one. 

Some are setting up what they call "data councils"—cross-functional teams with people from finance, IT, operations, legal, and compliance. These groups work together to define shared data standards and actually assign ownership for keeping data clean and accurate.

Others are leaning on modern FP&A platforms that can handle the integration headache for you. Drivetrain, for example, comes with 800+ pre-built connectors and custom integrations that act as connective tissue between your operational data, ERP, and planning workflows. It's one way to solve the integration problem without rebuilding your entire tech stack.

3. Demonstrate the ROI and value of AI to stakeholders

If you can't prove AI is delivering results, it’s possible that you’ll lose support for it. Boards and CEOs expect to see proof that it is shortening processes, lowering costs, or discovering new revenue opportunities, and ideally, doing all three. 

The right finance software can make ROI tracking automatic. Real-time dashboards let you show AI’s impact as it happens, whether that’s faster scenario modeling, higher forecast accuracy, or fewer write-offs. 

When you can connect AI initiatives directly to measurable business results, the discussion with stakeholders moves from “why are we investing in this?” to “how can we scale it further?”

4. Upskill and reskill to build AI-enabled finance teams

You don’t need a team of data scientists to make AI work in finance. What you do need is for your existing team to feel confident using AI tools in their day-to-day work.

We're seeing CFOs handle this in a few different ways. Some run short, focused training sessions to cover the basics. Others pair finance analysts with data teams on live projects.

Another strategy that is gaining traction is creating AI champions inside finance. These are your early adopters who test new tools first, figure out what works, and then help the rest of the team get comfortable. It’s far more effective than pushing training from the top down.

Lastly, another smart move is hiring hybrid talent, i.e., finance pros who also understand data engineering or machine learning. They can translate business needs into tech requirements and bridge the gap between finance and IT.

Emerging opportunities for CFOs ready to embrace AI 

Once you’ve laid the foundation, you can start building on it with AI use cases that can unlock value in a variety of ways.

Usage-based pricing and revenue model innovation

For SaaS companies, specifically, AI makes it much easier to track, forecast, and invoice based on actual product usage, not just fixed contracts. This allows CFOs to adopt different pricing models like consumption-based billing.

With real-time AI analytics, you can see usage patterns in real-time. That means spotting upsell opportunities, identifying churn risks earlier, and fine-tuning pricing to customer behavior without waiting for a quarterly review. 

These models also demand new metrics. It’s no longer enough to watch ARR alone. CFOs should be tracking unit economics tied to usage, LTV under variable pricing, and profitability by customer cohort and using these insights to make faster, sharper pricing calls.

Generative and agentic AI applications in finance

Generative AI is already reshaping forecasting and scenario planning from a slow, manual process into something that happens in minutes. With AI-powered forecasting software, you can now feed historical data, external signals, and operational inputs into AI tools that generate multiple forecast scenarios in minutes.

Beyond this, agentic AI can run continuous scenario simulations. For example, you can adjust assumptions for market shifts or change interest rates and surface the most likely outcomes in real time. 

AI financial modeling software like Drivetrain is making this capability accessible by integrating with ERP, CRM, and accounting systems. That means your “what if” scenarios are powered by a single source of truth, and the results are ready to share with stakeholders in a format they can act on right away.

A strategic action plan for forward-thinking CFOs

For AI to be a core part of the finance toolkit, CFOs need to act now. They need to build a roadmap that links AI initiatives directly to the organization’s long-term goals. 

1. Setting a vision: You need to decide where AI can make the biggest difference in your organization. It’s ideal to start with areas that give you early wins, like automating close processes or improving cash flow visibility. 
2. Establishing a scalable operating model: Design processes, workflows, and ownership structures that can expand as your AI adoption grows.
3. Build AI fluency across your team: Your finance function needs people who can work comfortably with AI tools, challenge outputs, and apply insights in context. Use targeted training and cross-team projects to embed skills without inflating headcount.
4. Strengthen your data foundation: Eliminate silos, clean up historical records, and standardize definitions across the business. If possible, use an AI FP&A software to create a single source of truth that connects operational, financial, and market data.
5. Strengthen external visibility: Use AI-driven insights to engage investors and boards with forward-looking perspectives on performance, risk, and growth opportunities. Position finance as the source of these insights.
6. Monitor the market and adapt: Keep pace with developments in AI capabilities, regulations, and peer adoption. Update your roadmap regularly to capture emerging opportunities.

Lead the next era of finance with AI

AI is no longer a side project for finance teams. The future of the CFO role is one where leaders The CFOs who act now, link AI to strategy, build the right capabilities, and measure real results will set the standard for the next decade of financial leadership.

Drivetrain is an AI-native FP&A platform built for CFOs ready to become true navigators for the business, providing deeper insights faster than ever before.

Explore Drivetrain today to learn more about how you can start using AI to forecast faster, plan smarter, and turn data into decisions.