The data explosion and advance of AI is positioning finance as the strategic enabler of insight and value. This is a once-in-a-generation opportunity for finance professionals and the finance function but, a new report warns, significant hurdles need to be addressed, particularly around skills.
The report from ACCA and CA ANZ, ‘Enabling finance insight: Bridging skills and data gaps for AI-enabled finance’, provides a technology and data-centric perspective of the evolution of finance and the future of the finance function (which was explored in an earlier report from ACCA, CA ANZ and PwC).
AI must be used to generate value rather than just efficiencies
The study is based on a global survey of 1,600 finance professionals, alongside insights from roundtable events and interviews. The findings show how the finance function is embracing diverse data types, from real-time operational metrics to unstructured internal text data, to meet strategic priorities and regulatory demands.
Value goal
But the report warns that it is ‘imperative’ that finance leaders strategically deploy AI technologies to generate value rather than just automating out existing inefficiencies.
‘The finance function stands at an unmissable opportunity,’ stresses the report. ‘Stakeholders are increasingly demanding proactive leadership – requiring finance to evolve from a retrospective reporting engine into a strategic enabler of enterprise-wide insight.’
‘Execution is often hampered by a lack of formal frameworks’
The survey found that finance teams are increasingly using real-time operational data, with over 60% of respondents saying they have increased its use in the past two years. But internal text data, generated by meeting minutes, contracts and transcripts, are also being used more frequently than before.
‘This high usage reflects the opportunity presented by natural language processing technologies, and why GenAI is so quickly becoming embedded in daily work,’ says the report.
Triangle of frustration
It argues that finance leaders are simultaneously planning offence and defence – supporting new business models or mergers and acquisitions, while ensuring the organisation complies with increasingly complex global compliance and standards. ‘This dual pressure necessitates a forward-thinking data strategy,’ the report says.
‘While the ambition to use real-time and unstructured data is clear, execution often stalls due to fundamental structural and capability gaps,’ the report says, adding that the research data reveals ‘a triangle of frustration’, where data quality issues, lack of appropriate skills, and difficulty in integrating multiple sources are the primary barriers.
The report says that while the desire to leverage data and AI is strong, ‘execution is often hampered by a lack of formal frameworks for data evaluation and intentional upskilling’.
A shortage of the right skills in finance is a key focus of the report. The full potential of AI in finance cannot be fully realised without ‘a significant shift in capabilities’, it argues. It identifies ‘a critical gap in GenAI literacy and predictive analytics skills, exacerbated by an overreliance on informal learning’.
Over 70% of respondents to the survey, for example, report low levels of GenAI skills – 38% say they have only basic GenAI skills while 34% have no experience at all.
‘There is a notable gap between the importance placed on future skills (particularly GenAI and predictive analytics) and current capability,’ the report concludes. ‘Critical thinking, sceptical validation and contextual storytelling are essential to reduce automation bias, anchoring bias and deskilling risks.’
Strategic risk
It adds that ‘an overreliance on self-directed, informal learning could be a strategic risk’ for the profession. The vast majority of upskilling takes place through on-the-job training and self-initiated learning, with almost half the workforce learning about AI independently or in their spare time.
‘Formal training programmes and dedicated budgets are needed to build consistent capability across the finance function,’ it says
It makes the additional point that upskilling can mitigate risk, as the adoption of AI and new data types shifts the risk landscape. The report identifies a ‘trust deficit’, with finance professionals deeply concerned about the integrity and verifiability of AI-generated outputs.
‘Finance leaders must embrace a proactive, architectural role’
The report makes a number of recommendations for finance leaders, including:
- Invest in data foundations as the bedrock for AI.
- Champion AI governance and measure tangible ROI – finance is ideally placed to champion responsible AI adoption across the organisation.
- Proactively develop critical capabilities for finance evolution, including the design of intentional upskilling programmes that target critical skills gaps, particularly in GenAI literacy, predictive analytics and data management.
- Foster collaborative ecosystems and shift the role of the finance team from one of ‘doers’ to ‘enablers’.
‘To thrive in this evolving landscape and truly enable the future of insight,’ the report concludes, ‘CFOs and finance leaders must embrace a proactive, architectural role. This involves strategic investments, intentional capability development and a steadfast commitment to governance and critical judgment.’
More information
This findings of this report are explored further in one of the sessions of ACCA’s half-day AI conference, ‘The AI realignment’. Register to watch on demand and earn free CPD units.
Read more in ACCA’s AI Monitor series: ‘How is AI reshaping finance and accounting work?’, ‘Skills to drive responsible AI adoption’, ‘Shining a light on AI’s ethical threats for finance professionals’, and ‘Risk and responsibility’.
