Finance teams are increasing their use of real-time data and AI to improve business insights and support decision-making, according to new research from the Association of Chartered Certified Accountants (ACCA) and the Australian and New Zealand Institute of Chartered Accountants (CA ANZ).
This global study, based on responses from 1,600 finance professionals, found that more than 60% of finance teams have expanded their use of real-time operational data in the past two years.
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The findings show that finance functions are moving away from retrospective reporting to more up-to-date, forward-looking analysis.
This change is being driven by access to a wide range of data, including live operational metrics and unstructured internal text, and the increased use of AI tools to analyze and interpret information.
The report also found that finance teams are working more closely with other departments. Nearly 60% of respondents said they now work closely with data and IT teams, suggesting that traditional silos are breaking down.
However, as the use of AI expands, concerns about the quality of its output are widespread.
The survey found that 93% of financial professionals are concerned about the integrity and verifiability of insights generated by AI.
Issues cited include illusions, inaccuracies, incomplete datasets, lack of transparency, and bias.
ACCA CEO Helen Brand said: [chief financial officers] Finance teams must lead the responsible adoption of AI across the organization and ensure robust training and governance is in place. Critical thinking, skeptical examination, and an ethical approach are essential. ”
Recognizing these risks still requires stronger skills development in a fast-changing sector, the report says.
CA ANZ CEO Ainsley Van Onselen said: “AI is now at the core of the finance toolkit, but it’s not a shortcut. CFOs and finance teams need to leverage AI to not just speed up old processes, but sharpen their judgment and create real value.”
“That means investing in structured learning and working more closely with IT and data teams. Upskilling is not an option; it’s a way to manage risk.”
In Australia and New Zealand, the findings highlight skills gaps. Approximately 70% of respondents said they had only basic generative AI skills or none at all.
Additionally, more than one-third said they had no formal training in data storytelling.
At a global level, the main drivers for the increase in data analytics were strategic priorities cited by 45% of respondents and regulatory requirements cited by 43%.
The report also identified key obstacles to improving business insights. 42% of respondents cited data quality issues. The same percentage cited a lack of appropriate skills, and 40% said integrating multiple data sources remains difficult.
