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Artificial intelligence (AI), particularly generative AI, is reshaping how organizations operate, manage risk and make decisions. For finance functions, the opportunity is not limited to faster processing: AI can automate components of routine work, accelerate analysis and strengthen forward-looking insight, enabling finance teams to devote more attention to interpretation, challenge and business partnering. However, capturing the full potential of AI often requires organizations to reconsider processes rather than simply add AI to existing workflows, while preserving clear ownership of the conclusions and decisions that follow. Trust is essential to turning AI capability into sustainable value. Generative AI may produce inaccurate calculations, incomplete analysis or plausible but unsubstantiated content; it can also create risks involving confidentiality, privacy, cybersecurity, intellectual property and bias. These risks are most significant where AI affects financial or sustainability reporting, regulatory compliance, forecasts or material business decisions.
Organizations should therefore adopt proportionate, risk-based governance. Lower-risk applications may be supported by clear guidelines and proportionate review, while higher-risk applications require stronger validation against reliable evidence, appropriate documentation, competent human oversight and explicit accountability. A value-risk assessment can help finance leaders prioritize applications without either constraining useful experimentation or accepting unmanaged risk.
Accountants remain essential in an AI-enabled finance function because the technology changes where professional judgement is exercised rather than removing the need for it. Their roles increasingly converge: as users, they apply AI to improve finance activities; as guardians, they protect the integrity of information and controls; and as business partners, they help management evaluate the value, risks and implications of AI-enabled change.
Finance leaders should begin by understanding where AI is already being used and assessing applications according to their value and risk. They should then establish clear protocols for approved tools, data use and review, supported by defined ownership and accountability. Together with investment in AI literacy and professional capability, these actions can provide a practical foundation for responsible adoption and sustained value creation.