What if your next financial advisor is an AI? Individuals could ask how much tax they owe, whether an investment is too risky, whether they can afford a home loan, or which financial product suits their needs. For companies, the questions get bigger: Can AI forecast cash flows? Which customers are likely to default? Where is fraud happening? What tax risks are sitting in the books? According to KPMG's 2026 Global AI in Finance report, much of this is no longer theoretical.
AI adoption in finance doubled in two years
According to KPMG's 2026 Global AI in Finance report, based on 1,013 senior finance leaders across 20 countries and 13 sectors, active AI use in finance has more than doubled since 2024, from 30% to 75%. The report also found that 76% of organisations now use AI in financial planning, 70% reported improved decision-making quality, 71% faster decision-making and 64% better forecasting accuracy.
| Metric | Percentage of organisations |
|---|---|
| Active AI use in finance (2024) | 30% |
| Active AI use in finance (2026) | 75% |
| Using AI in financial planning | 76% |
| Improved decision-making quality | 70% |
| Faster decision-making | 71% |
| Better forecasting accuracy | 64% |
Where AI takes over routine finance work
According to Business Today's reporting, the first layer of AI finance is the least dramatic but easiest to scale: routine work. AI can increasingly handle data-heavy finance tasks such as bookkeeping, reconciliation, invoice processing, reporting and compliance, identifying anomalies and producing summaries with less human intervention. Pei Fu Hsieh, Co-founder of AI Accountant, said businesses his company works with are seeing finance teams spend less time on data entry, transaction categorisation and reconciliation.
"The gains can be measured through faster processing, fewer manual interventions, quicker book closures and more timely financial information." — Pei Fu Hsieh, Co-founder, AI Accountant
Hsieh also sees a larger shift in how business owners access financial information, with owners increasingly able to ask simple questions such as "How much cash do I have?" or "Who owes me money?" Instead of waiting for information to be compiled, finance professionals can spend more time on analysis, forecasting, cash-flow planning and business decisions.
Where human judgment still rules
Swaroop Repaka, VP Product at ClearTax, said: "The pattern is consistent, and it has an order to it." He described the first wave of AI adoption as focused on "tax notices, litigation support and tax research", while the bigger impact is now emerging in high-volume, rule-bound work based on structured data. But AI's role remains limited in judgement-heavy areas.
"Tax positions, treaty interpretation, transfer pricing: anything that needs a defensible view rather than a fast answer" still requires greater human involvement, Repaka said.
Repaka added that the next phase will move from individual tasks to achieving larger goals, with AI potentially enabling continuous monitoring of cash leakage, vendor risk and audit processes. The biggest gains so far include faster reconciliations, higher input tax credit realisation, fewer notices and greater automation. The barriers, he said, are fragmented data, poorly defined use cases, and treating AI as a software licence rather than an operational capability.
What technology leaders should watch
For enterprise technology leaders, the KPMG survey and the expert commentary outline a clear division of labour: AI handles rule-bound, high-volume tasks such as reconciliations and invoice processing, while humans oversee judgement-heavy areas such as transfer pricing and treaty interpretation. The challenges Repaka listed — fragmented data, poorly defined use cases, and treating AI as a licence rather than a capability — are the practical points where adoption stalls. Business Today's report asked who keeps watch as technology becomes the new financial advisor; based on the survey data and expert commentary, the watchers are the finance leaders who define the use cases and validate the data.
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