Finance departments with successful artificial intelligence results are more likely than their peers to assign people to roles that directly support the technology’s scaling and execution, according to a presentation at Gartner’s 2026 Finance Symposium/Xpo.
“These organizations have realized that we need to have people specifically owning AI initiatives,” Marco Steecker, a senior director in Gartner’s finance research practice, said during a May 29 session focused on strategies for ensuring AI success in finance.
According to research presented during the session, finance organizations report that their AI initiatives succeed only about 50% of the time. To better understand why some organizations outperform others, Gartner segmented respondents into two groups: those with AI success rates below 60% and those above that threshold.
The analysis found that outcomes were less about spending levels or access to tools and more about how organizations structured AI execution.
Below are four tips from the session on building successful AI teams in finance:
1. Assign a dedicated AI leader
High-performing finance organizations tend to establish a dedicated AI leader to accelerate AI-driven innovation and ensure alignment with the broader enterprise AI vision, Steecker said.
The role is responsible for defining how AI is delivered within the function, including the types of teams required and the processes used to develop and deploy tools. It also involves managing the organization's AI portfolio, allocating resources, and overseeing investments in both near-term AI initiatives and longer-term foundational capabilities.
Another key responsibility is driving AI adoption across the finance function. This includes overseeing AI literacy initiatives and supporting change management to ensure broader uptake of AI tools.
In terms of qualifications, organizations typically look for individuals with 10 or more years of business or technology management experience, along with at least five years leading multidisciplinary teams, as well as a background in operating model innovation and change management.
“You're not going to find many folks who have all of these boxes checked. But from my experience, the last two are much more important than the first two,” Steecker said. “I've seen a lot of people with enough ambition leap into these AI leader roles, provided that they have enough competencies around an understanding of how to innovate and how to drive change.”
AI leadership roles do not need to be defined by deep technical expertise, Steecker said. He suggested providing structured development time — about one day per week over three months — for the selected leader to complete AI training and build foundational understanding.
2. Build out your team with key execution roles
After installing an AI leader, successful finance organizations then begin to build a small, execution-focused AI delivery team that can grow as use cases scale over time.
An essential member of the team is the product manager, responsible for driving AI initiatives from end to end and accountable for delivery and outcomes. This role serves as the tactical counterpart to the AI leadership role, translating strategy into execution.
AI product managers spend significant time refining products and shaping development roadmaps by staying current on industry developments and collaborating on new ideas. They also coordinate closely with development teams through regular stand-ups, and continuously review usage data and end-user feedback to identify opportunities for improvement and guide iteration.
Another key component for the team is technical expertise, which may come from a data scientist for analytics-driven use cases or a developer or coder for automation-focused initiatives. The exact profile depends on the type of AI work being pursued.
3. Tap into in-house IT talent
When it comes to acquiring technical AI and data science expertise, successful finance organizations start by leveraging existing technical staff from elsewhere in the company rather than relying primarily on external consultants.
This “borrowing” model provides an immediate boost in capability and helps finance teams move faster by tapping into established expertise. It also brings three key advantages: stronger understanding of shared data systems, standardized methodologies for building AI tools, and familiarity with existing centralized governance and IT requirements.
By relying on in-house experts — at least in the short term — finance organizations can boost success rates by avoiding some of the technical hurdles they may face early on, according to Steecker. Hiring of outside talent can be used as a longer-term strategy, he said.
4. Design for scale as AI adoption expands
As AI usage increases across finance, team structures tend to become more specialized.
Organizations often split responsibilities across product management, technical development, integration and end-user engagement. This helps manage growing portfolios of AI tools while maintaining alignment with business priorities.
The shift reflects a broader principle highlighted in the session: AI should not be treated as a one-time implementation, but as an ongoing capability that requires continuous oversight and refinement.
“It's far too easy to treat AI as a side-of-the-desk activity that we come to when we have some time to dabble,” Steecker said. “It's this kind of mindset that actually dooms a lot of AI initiatives.”
