🏠︎ | Past Sessions | How AI Will Change the Role of CFOs
- Event: Finance Forum 25
- Date: 7 October 2025
- Speakers
- Wouter Born, GP at Born Capital, CFOTech investor, advisor and founder of finstory.ai
- Estimated read time: 6-7 minutes
Quick summary
This session set out an optimistic view of how AI will reshape finance and the CFO role over the next decade.
Wouter Born argued that the biggest shift is not just smarter AI reasoning, it is AI’s ability to write and execute code, which could let finance processes adapt in real time, even when data quality is imperfect.
He described a direction of travel towards autonomous finance, where agents can execute tasks such as close, reconciliation and forecasting. In that world, the CFO spends less time validating activity and more time on decision support, storytelling and business partnership.
Why the bull case matters for finance leaders
Born positioned AI as an opportunity to change what finance spends its time on. He emphasised that many finance teams are still trapped in non value add work, checking transactions, chasing approvals and managing manual exceptions.
The practical claim is that AI can reduce that friction, not by replacing judgement, but by freeing capacity. That capacity then goes into strategic planning, stakeholder conversations and the kind of work that improves decision quality.
This framing matters because it links technology adoption to leadership impact. It is not about using tools for novelty; it is about rebalancing time towards business value.
Autonomous finance and the changing shape of the CFO role
Born asked the audience to imagine a future where AI handles core finance activities through large numbers of agents. In that scenario, month end close, consolidation and forecasting can be executed by systems that do not just analyse, but act.
His point was not that finance disappears, but that the centre of gravity moves. If core processes become more autonomous, the CFO role becomes more explicitly about guiding the organisation, shaping narratives and partnering with the business.
He also noted an open question, how organisations develop future CFOs if fewer junior roles exist, but did not propose a detailed solution within this session.
The real disruption: AI that can code
Born argued that the most important breakthrough will come from AI’s ability to generate and run code, not simply from models scoring higher on benchmarks. He described coding as a practical capability that can change systems while work is happening, rather than waiting for planned technology projects.
He linked this directly to a common finance barrier, data quality. Instead of pausing work because mapping is wrong or context is missing, AI can, in theory, correct mappings, request clarification from the business and continue the process.
For finance leaders, this matters because it reframes AI from a separate tool into an operating layer that can keep improving workflows as they run.
FP&A becomes more live, more scenario driven
Born pointed to three drivers that could reshape FP&A. Increasing reasoning capability, expanding organisational data capture and AI’s move from text in, text out systems to tools that can execute tasks in the real world.
The implication is that scenario modelling can happen closer to the moment decisions are made. Rather than producing analysis after the discussion, finance can generate options during it, using richer context and wider datasets.
This is a shift from reporting to decision support, not only in output, but in timing. Finance becomes more central to how trade offs are evaluated in real time.
From legacy stacks to agent led architectures
Born described a future model where agents sit above a central data layer, which acts as the single version of the truth. Agents execute tasks and write outputs back into that shared layer, rather than relying on separate applications and manual handoffs.
He explained the distinction using bookkeeping, where an agent combines the work of the user and the system. In this view, processes such as close are not single tools, they are sequences of checking, reconciling and communicating, which agents could manage end to end.
For CFOs, the message is that data architecture and integration readiness will shape how quickly AI becomes useful, and how safely it can be deployed.
Security concerns and practical guardrails
Born addressed a common objection, sensitivity of finance data in AI systems. He distinguished between personal use of consumer tools and enterprise deployments, noting that enterprise versions can offer higher security assurances, and that teams should avoid using personal accounts for sensitive data.
He also suggested that even with more secure tooling, there is still judgement required about what to share, particularly for public companies close to earnings activity.
The practical takeaway is to treat AI enablement as both a technology and governance change, with clear processes agreed with security and compliance leadership.
What good looks like, practical actions you can take now
Born offered a set of steps to help finance leaders move from interest to progress, while recognising that getting to autonomous finance is a journey.
Questions to ask your team this quarter- Is our finance tech stack modern enough to integrate AI effectively, or do legacy constraints limit adoption
- Where are we spending the most time on checking and exceptions, and what would we stop doing if we trusted automation
- Which decision processes would improve most if FP&A could run scenarios faster and closer to meetings
- Do we have a clear view of data quality issues, and an owner for fixing them
- Whether AI pilots aim for strategic outcomes, not only task automation
- Whether your team is building capability in prompting, workflow design and basic coding literacy
- Whether data quality improves because processes are redesigned, not because teams work harder
- Whether business leaders bring finance into discussions earlier because insight is arriving faster
- Building AI proofs of concept that are impressive but disconnected from business value
- Automating broken processes without addressing root causes in data and controls
- Waiting for perfect data before starting, rather than improving data through usage
- Treating AI as an individual productivity hack instead of a finance operating model change
What good looks like in practice
A finance function where AI reduces time spent on transaction checking and approval chasing, freeing leaders to focus on planning, investors, stakeholders and business partnering. In that context, the CFO role becomes more influential, not less, because it is more tightly connected to strategy and decision making.
Conclusion: the CFO role will stay, but the job will shift
Born’s closing point was that the CFO role will still exist in a decade, but finance leaders will need to adapt their mindset and capability. He framed the competitive risk as being outpaced by someone who uses AI more effectively, rather than being replaced by AI itself.
“Not AI will replace your job, but someone with AI might come after your job.” Wouter Born, GP at Born Capital, CFOTech investor, advisor and founder of finstory.ai
Key takeaways
- AI’s biggest impact may come from coding and execution capability, not just smarter answers
- Autonomous finance shifts the CFO role towards storytelling, leadership and business partnering
- Data strategy and integration readiness determine how quickly AI becomes useful
- Security and governance need to be designed in early, not retrofitted
- The competitive risk is moving too slowly, not experimenting and learning
Speaker
GP at Born Capital, CFOTech investor, advisor and founder of finstory.ai, seasoned CFOTech investor and AI specialist focused on AI tools for the Office of the CFO through investing, advising and writing.