What CFOs at Adobe, Dataminr, and Huntington say about scaling AI

Good morning. AI is rapidly becoming a board-level priority, pulling CFOs to the center of enterprise AI strategy.

That was a takeaway from the first Fortune Emerging CFO webinar of the year, held on Jan. 27 in partnership with Workday, during our discussion with three finance leaders: Tiffany Buchanan, CFO of Dataminr; Dan Durn, CFO and EVP of finance, technology, security and operations at Adobe; and Zachary Wasserman, CFO at Huntington Bancshares.

At Dataminr, AI is a standing item in the company’s annual operating plan and a factor in every budget decision, Buchanan said. She views AI adoption as non-negotiable across all functions, enabled by native capabilities in modern SaaS platforms and AI-powered tools that are now accessible to companies of all sizes. That approach positions the CFO as a strategic partner to the CEO, directing capital toward initiatives that drive growth and efficiency, Buchanan said.

Durn framed Adobe’s AI strategy around increasing “organizational velocity”—compressing the time from insight to action in a data-rich environment. Embedding AI across operations allows teams to detect signals faster and respond more effectively, he said. But technology alone is not enough; success also depends on culture, continuous learning, and leaders who bring intellectual curiosity rather than relying on static playbooks, Durn explained.

For Wasserman, AI adoption in a heavily regulated bank requires balancing speed with risk. With model capabilities advancing rapidly, even a short delay can create a significant competitive disadvantage, he warned. Huntington has responded by building a generative AI risk framework, prioritizing use cases by risk level, and requiring human oversight for higher-impact applications, he said.

Those executive perspectives underscore a broader shift now playing out across finance organizations: CFOs are moving from AI experimentation to execution.

The data problem beneath the AI push

Sommer Frazier, managing director of finance transformation at KPMG US, explained during the webinar that while most companies have adopted AI in some form, many remain stuck between pilot projects and scaled deployment, she said. Data quality issues, weak governance, infrastructure gaps, talent shortages, and cybersecurity concerns are among the most common obstacles, Frazier said.

The challenge is elevating finance’s role as a steward of enterprise data. According to a recent KPMG AI Pulse survey, 82% of executives now cite data quality as the top barrier to AI success. Frazier said finance leaders must help establish standards and governance frameworks, while business teams retain day-to-day accountability for the data they generate and maintain.

Generative AI, once a novelty, is already embedded in everyday enterprise tools, from productivity software to core financial systems, she said. Finance teams are using it to summarize meetings, draft variance commentary, analyze contracts at scale, and identify pricing and payment-term patterns that can improve financial performance.

Looking ahead, Frazier expects 2026 to mark a shift toward scaled AI agent orchestration, with employees increasingly managing networks of AI agents rather than discrete processes. The result, she said, will be a reallocation of finance talent toward higher-value analysis and decision-making.

You can learn more about the additional topics discussed by watching the complete webinar here.

Sheryl Estrada
sheryl.estrada@fortune.com

This story was originally featured on Fortune.com