I recently moderated a powerhouse panel featuring finance executives Chris Suh (CFO, Visa), Jannie Affeld (VP, Finance Systems Engineering, Google), and Dan Durn (CFO, Adobe) to discuss the impact of AI on finance functions, strategies for adoption, and how to prioritize technology investments. With over 50 finance leaders from companies like Intuit, Uber, Salesforce, Twilio, and Wells Fargo in attendance, the discussion was a deep dive into the future of finance in the AI era. Both the panelists and the audience shared their ideas, reservations, and optimism for AI-enabled finance functions.

AI’s Expanding Role in Finance

While the overriding sentiment was that AI is still in its early days and we are just beginning to understand its full potential, the panel noted a few core areas already undergoing significant transformation. These include FP&A functions, finance operations requiring repetitive tasks, and previously neglected tasks overlooked by traditional technology solutions.

AI’s impact is being felt across three major areas:

1. Enhanced Forecasting & Analytics – AI-powered forecasting models are improving accuracy in financial planning and cash flow predictions.
2. Automation of Finance Operations – AI is streamlining invoice processing, fraud detection, and supplier analysis, reducing manual effort.
3. Decision Support & Data Analysis – AI is transforming vast amounts of unstructured data into actionable insights, enabling finance teams to make faster, more informed decisions.
   

Dan Durn (Adobe) emphasized the importance of AI in handling data:

“There is a mountain of unstructured data in this world. Data is good, but data without insights is of little value. AI helps translate unstructured to structured data, which gives you a starting point to derive insights and make an impact.”

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Challenges & Best Practices for AI Adoption

Despite AI’s potential, adoption comes with challenges. The panelists acknowledged that AI transformation in finance may resemble prior technological shifts, but with a crucial difference: AI adoption is as much about people as it is about the technology itself.

Dan Durn (Adobe) highlighted the importance of change management:

“Change management is as important as the technology itself. The biggest demotivator for people is to burden them with something that goes nowhere. You need early proof points of success to unlock enthusiasm and adoption.”

To navigate these challenges, the panelists agreed on the importance of quick experimentation, early wins, and thoughtful implementation.

 Jannie Affeld (Google) pointed out the need for disruptive thinking:

“We need disruptive thinking, especially when it comes to process. The problem I run into is never technical—it’s process complexity. AI can help us rethink workflows completely, reducing unnecessary steps.”

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Measuring AI ROI

For finance leaders considering AI, proving return on investment is a key concern. Since AI in finance is still evolving, standardized ROI metrics remain limited. However, the panelists shared frameworks that organizations can use to capture early wins, fail fast, and ensure human oversight.

Chris Suh (Visa) addressed a common misconception about AI and workforce impact:

“There’s a misconception that AI translates into fewer heads. Our teams are more productive than ever before, but I have more people than I ever had before. AI allows us to do more, not necessarily reduce headcount.”

Key strategies for measuring AI’s impact include:

• Amplifying Impact – AI enables finance teams to scale efficiently and focus on strategic work rather than simply reducing headcount.
• Quick Wins & Pilot Programs – Starting with small-scale implementations helps prove AI’s value before scaling across the organization.
• Complexity Reduction – AI simplifies finance processes, enabling organizations to scale without increasing inefficiencies.

Jannie Affeld (Google) reinforced this by stating:

“It's the combination of AI and data that allows us to make better decisions - and that just speeds things up, so we get more efficient working capital.”

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The Future of AI in Finance

While the pace of AI adoption will vary across organizations, the panelists agreed that AI is set to fundamentally reshape finance teams and enterprise systems.

How AI Will Transform Finance:

• Evolving Finance Roles – AI will shift finance professionals from manual data handling to strategic decision-making, requiring stronger data literacy and analytical skills.
• Next-Gen Finance Systems – Traditional ERP systems are becoming outdated. Jannie Affeld (Google) predicted that the future lies in modular, AI-driven applications that can be deployed quickly and replaced as needed.
• AI as a Finance Assistant – AI will act as a co-pilot, helping finance teams synthesize data, manage workflows, and optimize processes in real time.

Jannie expanded on the future of finance technology:

“We’re at a very interesting time in 2025. Startups are leading the way right now, but we’re going to see the big players stepping in. Instead of just automating two processes, companies will look at 20, and eventually, we’ll see mainstream ERPs being rebuilt with AI.”

Dan Durn (Adobe) emphasized AI’s role in accelerating insights:

“The value of AI isn’t just in automating tasks—it’s about speeding up time to insight. AI will help finance teams identify signals in data faster, so they can focus on making strategic decisions.”

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Key Audience Questions & Takeaways

Finance leaders in attendance raised critical questions about AI’s role in decision-making, risk management, and governance:

Q: Can AI approve major financial decisions?
A: Not yet. AI can assist in decision-making but requires human oversight, especially for high-stakes approvals.

Q: How do we trust AI-generated forecasts?
A: Many companies are running AI models in parallel with human-driven forecasts to validate accuracy.

Q: What skills will future finance teams need?
A: Finance professionals will need a mix of domain expertise, AI literacy, and data analytics capabilities to leverage AI tools effectively.

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Final Thoughts

The panel concluded that while AI adoption in finance is still in its early days, its transformative potential is undeniable. Organizations that embrace AI strategically—focusing on quick wins, data governance, and talent development—will be best positioned to drive efficiency and innovation in the years ahead.

Dan Durn (Adobe) offered a final word of advice:

“The mistake a lot of organizations make is they overanalyze things on the front end and are slow to act. The most important thing we can do is activate the organization, capture imagination, get quick wins, and build momentum.”

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