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For the accountancy profession, artificial intelligence (AI) has made a significant difference in enhancing efficiency. For example, AI has optimised business processes, attained greater accuracy in financial reporting, and achieved faster documentation turnaround. As AI becomes more embedded, finance teams are finding themselves with more time, intelligence and insights. Organisations that have progressed from pilots to using AI for higher-value work, such as forecasting, decision-making, and risk management, are also seeing improvements in enterprise value.
“Many CFOs are prioritising AI use cases focused on productivity and efficiency,” says Shankar Keshav, Principal Analyst in the Gartner Finance practice. “However, boards place greater emphasis on investments that drive growth, improve decision-making and deliver competitive advantage.”
Ironically, according to Gartner’s “The CFO Report: Gartner answers to top CFO challenges”, a report based on responses from 204 finance leaders, 45% of finance AI investments are concentrated primarily on productivity, compared with just 20% aimed at improving decision quality.
The reason for focusing on productivity first is understandable. Automating processes, generating reports and reducing manual effort produce benefits that are relatively easy to identify and measure. But, significantly, the Gartner findings also reveal that finance functions investing in AI initiatives intended to create new value propositions, products or markets are more than twice as likely to report high realised value from AI.
To better align with board expectations, finance heads should take a portfolio approach to AI investments, ensuring a good mix of initiatives that contributes to enterprise outcomes rather than just internal efficiency, says the report.
“CFOs need to shift more investment toward AI use cases that improve decision-making, enable scenario analysis, identify growth opportunities and build reusable assets such as data, models and knowledge,” Mr Keshav points out. This will require a shift in how AI initiatives are measured, to emphasise more on enterprise impact rather than just the number of pilots or hours saved.
Enterprises that have moved beyond AI experimentation are reaping the rewards, shares KPMG’s “2026 Global AI in Finance Report: The decision advantage survey”. Covering 1,013 senior finance leaders across 20 countries and 13 sectors, the findings show that active AI use in finance has risen from 30% in 2024 to 75% in 2026. Some 71% of respondents indicated that AI was meeting or exceeding their ROI expectations. Of note, respondents reported benefits in areas that required more than simple automation – 70% reported improved decision-making quality, 71% greater decision-making speed, and 64% better forecast accuracy.
Forecasting, planning, risk assessment and interpreting financial information have traditionally depended heavily on professional judgement. AI can augment these activities, but it does not remove the need for people who understand the assumptions behind the numbers – something finance professionals may want to note.
The KPMG report also highlights a fundamental condition for successful AI adoption: trust in the underlying information. It shows that 36% of organisations identified data quality as both a major barrier as well as an opportunity. Stronger assurance readiness is associated with substantially greater error reduction and confidence in scaling AI.
For the next generation of finance professionals, the findings make it clear that technical accounting and financial expertise will remain essential. Also important is the ability to interrogate AI outputs, challenge assumptions, recognise data limitations and translate analysis into business decisions.
The AI opportunity is bigger than merely speeding up the work. AI can help the finance team become more deeply involved in decisions about growth, risk and competitiveness. Technology alone will not make this happen; finance professionals are the real differentiator that knows where AI can add value, when its conclusions should be queried, and how better insights can translate into improved business outcomes.
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