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At a recent roundtable with CFOs from private equity-backed businesses, finance leaders discussed the growing role of AI within the finance function. While interest is high, most organisations are still in the early stages of adoption, with experimentation focused on operational efficiencies such as automation and faster data analysis. Key challenges remain around governance, data quality, and culture, but the discussion highlighted that AI is increasingly becoming a CFO-led agenda that will reshape finance teams toward more analytical and strategic roles.
The conversation brought together senior finance leaders to explore where AI is already delivering impact, where challenges remain, and how the role of the CFO is evolving as the technology matures. While enthusiasm for AI is high across organisations, one theme emerged consistently throughout the discussion: most finance teams are still in the early stages of implementation.
Across the group, there was a clear sense that the industry is moving from experimentation towards something more structured, but the journey is far from complete.
Below are some of the key insights that emerged from the session.
Most CFOs recognise the transformative potential of generative AI, yet adoption within finance functions remains relatively limited.
Across many organisations, AI is currently being used more extensively in operational or customer-facing areas than within finance itself. Within the finance function, activity tends to centre on experimentation and isolated use cases rather than widespread implementation across core processes.
Many barriers continue to slow progress, including challenges around data quality, governance frameworks, cultural resistance, and the absence of clearly defined implementation plans. As a result, while interest and momentum are strong, practical execution is still developing.
Participants widely agreed that AI will reshape the structure and focus of finance teams over time.
As routine data processing and reporting tasks become increasingly automated, the emphasis within finance will naturally shift toward business partnering, predictive analytics, and forward-looking insight. Rather than acting primarily as reporters of historical performance, finance teams are expected to play a more active role in supporting strategic decision-making.
This transition will require a different mix of capabilities. Alongside traditional accounting expertise, finance professionals will increasingly need stronger data literacy, technology fluency, and analytical storytelling skills.
Despite adoption still being relatively early, several participants shared examples of meaningful efficiency improvements already being delivered through AI.
In one case, a data production process that previously required eight weeks was reduced to just one week using AI-enabled tools. Other examples included automating manual FP&A tasks, improving access to advanced analytics, and enabling real-time data processing that accelerates decision-making.
However, the group also debated whether AI has yet materially improved the quality of strategic decisions. The prevailing view was that the most measurable benefits today are operational – increased speed, improved workflows, and productivity gains – while the strategic impact is still emerging.
One of the more nuanced discussions focused on how organisations should measure the success of AI adoption.
Some companies are currently tracking employee usage of AI tools and encouraging experimentation across teams. Others questioned whether activity metrics alone truly demonstrate value.
The consensus was that meaningful success ultimately needs to be measured through return on investment and tangible business outcomes. Clear use cases, quantifiable productivity improvements, and measurable gains in speed, cost, or quality are essential for demonstrating real progress.
Without these measures, it becomes difficult to distinguish meaningful implementation from experimentation.
Governance emerged as one of the most significant themes throughout the discussion.
AI tools, particularly large language models, can produce inaccurate or unreliable outputs, which creates clear challenges in environments where precision and accountability are critical. For many organisations, the question is not whether AI should be used, but how it can be used responsibly.
Several CFOs described operating with a “human in the loop” approach to oversight. One interesting reframing from the discussion was the idea of “human accountability in the loop”, recognising that responsibility ultimately sits with finance leaders even if processes become increasingly automated.
In many ways, the challenge is less about the technology itself and more about adapting governance models to support innovation while maintaining appropriate controls.
Beyond governance and technology, culture continues to play an important role in slowing adoption.
Many finance teams remain heavily reliant on established tools such as Excel, and resistance to change can limit the pace of experimentation. Skills gaps also remain a challenge, particularly where teams lack experience with data science, automation tools, or coding languages.
Participants agreed that successful adoption will require strong leadership from the top, combined with clear internal communication about the benefits of AI and practical examples of how it can improve workflows.
Another recurring theme was the need for finance professionals to develop new skills.
Traditional finance qualifications are not yet fully aligned with the requirements of an AI-enabled finance environment, meaning that much of the necessary upskilling will need to happen within organisations themselves.
Examples discussed included embedding data scientists within finance teams, introducing basic coding capabilities such as Python, and building broader AI literacy across the function.
Over time, these skills are expected to become increasingly important as AI becomes more embedded in everyday finance operations.
Perhaps the clearest conclusion from the session was that AI strategy needs to be CFO-led.
As adoption accelerates, AI is rapidly becoming a board-level topic. If CFOs do not take ownership of this agenda, responsibility will likely shift elsewhere within the organisation.
The CFO of the future will increasingly need to act as a technology leader, data champion, and transformation driver – while maintaining strong governance and accountability across the function.
For CFOs in private equity-backed businesses in particular, the conversation often comes down to two key questions:
Those who can answer both confidently are likely to be best positioned to deliver long-term value.
While AI adoption within finance remains at an early stage, the momentum behind it is undeniable.
The next phase will be defined by organisations moving from isolated experimentation toward structured implementation, supported by stronger governance frameworks, targeted investment in skills, and clearer strategic direction.
For finance leaders, the opportunity extends far beyond efficiency. As AI capabilities mature, the finance function has the potential to evolve into a more proactive, insight-driven partner to the business.
The technology may still be developing, but the direction of travel is already clear.
