The AI Finance Boom Has a Blind Spot: Accountability

Artificial intelligence is moving deeper into financial planning, forecasting and commercial analysis, with organisations reporting stronger decision-making and faster responses rather than only lower costs. A KPMG survey suggests, however, that the largest gains are concentrated among organisations with stronger governance, reliable data and the ability to produce evidence that AI-enabled processes can be audited.

Artificial intelligence is no longer confined to automating routine finance work. It is increasingly influencing forecasts, commercial assessments and decisions that determine where companies invest, how they manage risk and how quickly they respond to changing conditions.

Active AI use across finance functions rose from 30% in 2024 to 75% in 2026, according to a KPMG survey of 1,013 senior finance leaders across 20 countries and 13 sectors. More than three-quarters of organisations now use the technology in financial planning, reporting and commercial analysis, while 71% say it is meeting or exceeding expectations for return on investment.

The more consequential finding is not the speed of adoption, but where companies report the strongest gains. About 70% cited better decision quality, 71% reported faster decisions and 64% recorded improved forecasting accuracy. AI's corporate value proposition is therefore moving beyond cost reduction. The emerging prize is better judgment. That shift brings greater potential rewards, but it also raises the stakes when data, controls or accountability are weak.

Finance AI Is Moving From the Back Office to the Boardroom

The first wave of finance automation focused largely on repetitive work: processing transactions, reconciling accounts, producing standard reports and reducing administrative workloads. The latest adoption pattern points to a deeper role. Companies are directing AI towards planning, forecasting, risk assessment and commercial analysis, areas that inform business strategy rather than simply support it.

This changes how the technology should be evaluated. Saving time on a routine process produces a relatively visible benefit. Measuring whether AI improved a revenue forecast, sharpened a risk assessment or led to a better investment decision is far more difficult. Those outcomes depend on context, management judgment and events that may unfold months later. A faster decision is not necessarily a better one, and a forecast that appears more accurate in one period may perform differently when market conditions change.

Still, the reported gains suggest that finance executives increasingly view AI as an analytical capability rather than a productivity add-on. It can process large volumes of information, test scenarios and identify patterns more quickly than conventional workflows.

The challenge is ensuring that speed does not create false confidence. As AI moves closer to strategic decisions, organisations need clearer methods for measuring its contribution and identifying where human judgment remains indispensable.

Agentic Systems Are Raising Both Performance and Risk

The performance gap becomes wider among organisations using agentic AI—systems capable of coordinating and completing sequences of tasks with a degree of autonomy. Organisations at the orchestrating and multi-agent stages outperformed those still in early planning by an average of 32 percentage points, KPMG found. The difference approached 40 points in forecasting accuracy and return on investment.

The figures suggest that AI delivers more value when it is integrated into connected workflows rather than deployed through isolated tools. A coordinated system might collect data, conduct scenario analysis, identify anomalies and prepare recommendations as part of a continuous process.

This can reduce delays between detecting a change and responding to it. It may also allow finance teams to test more scenarios and update forecasts more frequently. But agentic systems complicate accountability. When several AI agents contribute to a recommendation, it may be harder to establish which system made a particular inference, what information it relied on and where an error entered the process.

The reported advantage of advanced adopters should therefore not be read as a simple argument for accelerating deployment. Organisations operating at these stages may also have stronger technology infrastructure, more reliable data, larger investment budgets and more mature governance.

The real divide may not be between companies that use simple AI and those that use sophisticated systems. It may be between organisations that can operationalise autonomy safely and those adding complexity faster than they can control it.

Assurance Is Becoming a Business Capability

Governance is often treated as a constraint imposed after a technology has been deployed. In AI-enabled finance, it may be one of the conditions that allows the technology to scale. Organisations capable of producing AI-related audit evidence efficiently reported significant improvements at three to six times the rate of those that were not assurance-ready. Yet only 42% were assessed as strongly prepared to provide such evidence for AI-enabled finance processes.

Assurance readiness means more than satisfying an auditor. It requires organisations to document how systems operate, what data they use, how outputs are reviewed and who is accountable for decisions influenced by AI.

These controls matter because financial outputs may be scrutinised by boards, investors, auditors and regulators. A company may obtain an impressive forecast from an AI system, but the result has limited institutional value if decision-makers cannot understand how it was produced or demonstrate that appropriate checks were applied.

Strong controls may also encourage adoption. Finance leaders are more likely to use AI in high-impact processes when they have confidence that systems can be monitored, tested and challenged. This places internal audit, risk, compliance and technology teams at the centre of finance transformation. Governance can no longer sit in a separate workstream. It must be designed into AI-enabled processes from the beginning.

Dirty Data Remains AI's Hardest Reality Check

The sophistication of an AI model matters little if the underlying financial data is incomplete, inconsistent or trapped inside systems that cannot communicate. Thirty-six per cent of organisations identified better data quality, integration and interoperability as their greatest opportunity to extract more value from AI. The finding exposes a familiar gap between technological ambition and operational readiness.

AI can analyse information rapidly, but it cannot reliably compensate for conflicting definitions, fragmented records or weak data controls. Poor inputs can produce polished outputs that appear authoritative while embedding existing errors.

Workforce strategy presents a related challenge. Some 38% of organisations are upskilling existing finance teams, compared with 28% hiring people with different capabilities.

The balance indicates that companies are not relying solely on specialist recruitment. They are attempting to combine financial knowledge with new skills in data interpretation, model oversight and AI-assisted decision-making.

This may be a more sustainable approach. Experienced finance employees understand the organisation's commercial context and reporting obligations. But they will need to become more capable of questioning algorithmic outputs, identifying unusual results and understanding when a model has moved beyond its reliable operating conditions.

AI is becoming widely available, reducing the advantage of adoption for its own sake. The stronger organisations will be those that connect the technology to trusted data, measurable outcomes and clearly assigned human responsibility. In that environment, governance is not the opposite of innovation. It is what turns an impressive system into a dependable decision-making capability.