AI in Accounting & Finance: The CFO’s Role in Driving Adoption
Alexandre (Finance Director @ Upflow)
Jul 2, 2025
From Financial Stewardship to Strategic Leadership
Artificial Intelligence (AI) is fundamentally reshaping finance functions worldwide. In 2025, CFOs stand at a pivotal crossroads transitioning from traditional financial stewards to strategic leaders who harness AI’s transformative power to drive efficiency, agility, and innovation.
Yet, despite AI’s promise, a 2024 CFO Connect survey revealed that while 85% of CFOs recognize AI’s potential, only 39% have implemented AI solutions effectively. The gap is not just technological; it’s about leadership, culture, data, and skills.
This blog offers a deep exploration of the CFO’s critical role in driving AI adoption. We unpack challenges, leadership mindsets, and practical strategies to help finance leaders lead AI-powered transformations with confidence and impact.
Keep reading to discover:
How the CFO role is evolving from traditional stewardship to AI-powered strategic leadership
The Four Foundational Pillars every CFO must master to drive effective AI adoption
A Practical Roadmap to kickstart AI transformation within your finance function
The Expanding CFO Mandate in the Age of AI
AI Is More Than Automation, It’s Augmentation
Nicolas Boucher, AI finance expert, shared in The Growth-Minded CFO podcast (S2,E5) that CFOs must shift their mindset: “AI isn’t here to replace finance professionals; it’s here to augment their capabilities. The CFO’s job is to manage AI like a team member, understanding its strengths and limitations, and integrating it thoughtfully into workflows.”
This means CFOs must move beyond cost-cutting automation to leverage AI for real-time insights, predictive analytics, and strategic decision support. The CFO role now includes:
Championing AI strategy aligned with business goals
Ensuring data integrity and governance for reliable AI outputs
Building AI literacy and culture within finance teams
Driving cross-functional collaboration to embed AI across the enterprise
Real CFO Voices: Lessons from the Front lines
💡 Steve Priest, CFO of eBay has continuously emphasized on how eBay’s long-standing investments in AI are enhancing both customer experience and financial operations. In an interview with The Wall Street Journal and other forums, Priest highlighted that eBay has been leveraging AI for over a decade to streamline seller workflows, such as automating product listings and improving transaction processes. He also noted that AI is increasingly supporting finance by automating reconciliations, extracting contract terms, and aiding sustainability reporting, which boosts productivity and sharpens decision-making. Priest described AI not just as a tool but as a strategic partner that requires careful governance to ensure compliance and accuracy.
The Four Pillars of AI Adoption for CFOs
1. Building AI Literacy and Closing Skill Gaps
A recurring theme is the critical need to upskill finance teams. Nicolas Boucher stresses that “AI literacy is no longer optional.
CFOs must invest in training programs that create AI champions who understand both finance and technology.”
While upskilling is a key enabler of AI adoption, few CFOs have formalized programs. In earlier episodes of The Growth-Minded CFO, leaders like Charly Kevers at Carta emphasized the importance of building adaptable finance teams ready to support rapid scaling — a mindset that pairs naturally with future AI integration.
💡Actionable Tip: Develop ongoing AI training modules tailored for finance professionals, blending technical skills with ethical and strategic considerations.
2. Ensuring Data Quality and Governance
AI’s power is only as strong as the data it consumes.
Gabi Steele, Co-founder and CEO of Preql noted, “AI can only work its magic if the underlying data is clean, structured and accessible.”
Supporting this, Gartner’s 2025 report reveals that 70% of AI failures in finance are due to poor data quality or governance. This stark statistic underscores why CFOs must take a leadership role in building strong data governance frameworks. Such frameworks should include clear policies, regular audits, and accountability mechanisms that span finance, IT, and operations, ensuring AI initiatives are built on a foundation of trustworthy data.
💡Actionable Tip: CFOs should lead efforts to implement data governance frameworks, including policies, audits, and accountability structures that span finance, IT, and operations.
3. Leading Change and Overcoming Cultural Resistance
Fear of job displacement and mistrust in opaque AI systems are common barriers. Nicolas Boucher warns against “black-box” AI models that finance teams don’t understand, which can breed skepticism.
Steve Priest’s experience at eBay also underscores the importance of transparent communication and involving finance professionals early in AI design and deployment.
💡 Actionable Tip: Launch change management initiatives focused on transparency, ethical AI use, and highlighting AI’s role as an enabler, not a replacer.
4. Driving Cross-Functional Collaboration
AI adoption is not a finance-only initiative. It requires breaking down silos and fostering partnerships across IT, legal, operations, and business units.
At Microsoft, successful AI integration has been driven by establishing cross-functional teams that include finance, IT, legal, and business leaders. This collaborative approach ensures AI solutions are practical, compliant, and aligned with strategic objectives. For example, Microsoft’s deployment of AI-powered tools like Copilot involved close coordination between product teams, finance, and compliance to accelerate forecasting accuracy and risk management.
💡 Actionable Tip: Establish cross-departmental AI task force to oversee strategy, implementation, and continuous improvement.
Practical Steps for CFOs: A Roadmap to AI Adoption
Step 1: Identify High-Impact Pilot Projects
Begin with small, focused pilot projects that tackle clear pain points like cash flow forecasting or expense management. These quick wins help build momentum and earn the trust of stakeholders, making it easier to expand AI initiatives later.
Step 2: Invest in Talent Development
Upskill your finance team continuously. Encourage curiosity and experimentation with AI tools. Promote collaboration between finance and IT.
Step 3: Strengthen Data Governance
Strong AI depends on solid data. Lead the charge in setting up data stewardship programs that ensure your data is accurate, compliant, and secure. Regular audits and clear policies will keep your AI insights reliable and trustworthy.
Step 4: Build a Culture of Collaboration and Transparency
Create open channels where finance teams can discuss AI’s benefits and challenges. Involve them early in AI design and decision-making to reduce resistance and promote shared ownership of AI projects.
Step 5: Treat AI as a Collaborative Partner
Integrate AI agents as “team members” that augment human judgment by surfacing insights, flagging anomalies, and automating routine tasks while humans retain final decision authority.
Real-World Impact: AI Transformations in Finance
JPMorgan Chase: AI-driven cash flow management reduced manual processing by 90%, enabling teams to focus on strategic initiatives. (CFO Connect, 2024).
Microsoft: Adoption of AI tools like Copilot has enhanced financial forecasting by automating data analysis and providing actionable insights, helping finance teams accelerate risk management and decision-making processes.
Conclusion
AI adoption is a complex, multi-dimensional journey demanding visionary leadership, cultural agility, and relentless focus on data integrity. As the Growth-Minded CFO podcast reveals through candid conversations with leading CFOs and AI experts, success comes from embracing AI as a strategic partner, investing in talent, fostering collaboration, and managing change thoughtfully.
CFOs who lead with curiosity, courage, and a growth mindset will transform their finance functions into agile, insight-driven engines of growth, turning AI from a challenge into a powerful competitive advantage.
FAQs
Q: Will AI replace finance jobs?
A: No, but it will reshape them. The best finance teams won’t be replaced by AI but they’ll be outperformed by teams that know how to use it. Upskilling and smart adoption are the winning play.
Q: What are the most valuable AI use cases in finance today?
A: Cash flow forecasting, real-time reporting, anomaly detection, and automated close processes are leading the pack. AI is already creating value in areas with high-volume data and repetitive workflows. If it’s rules-based, AI can probably help.
Q: How do I measure ROI on AI investments in finance?
A: Start with time saved, errors reduced, or forecast accuracy improved. Then map those to financial outcomes. Treat AI like any other strategic investment: pilot it, track impact, and scale what moves the needle.
Q: How do I upskill a finance team that’s never touched AI or data science?
A: You don’t turn your controller into a data scientist overnight and you don’t need to. Start with AI literacy: What it is, what it isn’t, and how it supports their work. Then offer tool-specific training and bring in partners to fill deeper tech gaps.
Q: How do I get buy-in for AI from a skeptical finance team?
A: Don’t sell the tech, show the outcomes. Run one high-impact pilot. Show them hours saved, bottlenecks removed, or forecasts improved. Involve them early in the process, and shift the narrative from “AI is replacing you” to “AI is helping you lead.”