As featured on BusinessMirror: AI in finance: The next competitive advantage
AI adoption across the finance function is broad. More than three-quarters of organizations are leveraging AI in financial planning, reporting and commercial analysis. According to the KPMG Global AI in Finance 2026 report, 71 percent of senior leaders surveyed found that AI is meeting or exceeding ROI expectations in their finance function. But adoption breadth and exceptional performance are not the same thing. The share of organizations reporting AI is exceeding expectations sits at 23 percent — a narrower group than the broader satisfaction figure suggests. This mirrors what KPMG's Q1 2026 Global AI Pulse observed at the enterprise level: AI adoption is moving faster than organizations' ability to translate it into enterprise-wide performance at scale.
What stands out is where the gains are concentrating. The strongest improvements are in decision-making quality, forecast accuracy and responsiveness. These are judgment-heavy areas, not transactional processes. Organizations deploying agentic AI report at least 32 percent stronger performance across key finance metrics, rising to nearly 40 points on forecast accuracy and ROI. This shows that AI in finance is operating as a decision-engine, not a cost lever.
AI as a decision-engine, not a cost lever
Most assumptions about AI in finance start with efficiency: faster close, fewer errors, lower cost. The data tells a different story. Active AI use in the finance function has moved from 30 percent to 75 percent in two years.
But adoption is moving faster than organizations' ability to realize enterprise-wide value at scale. Traditional ROI measurement — money in, money saved — does not capture where AI is actually producing value in finance today. Where agentic AI is generating measurable value, it clusters around capacity for growth, responsiveness and improved customer experience.
The organizations pulling ahead are not the ones adopting AI most broadly — most organizations already are. They are the ones reaching the orchestrating phase of deployment, directing AI into the work where judgment matters most: planning, forecasting, risk assessment. The finding parallels KPMG's Q1 2026 Global AI Pulse: at the enterprise level, competitive advantage has shifted from adopting AI to orchestrating it.
Finance points AI where it matters
AI is producing the largest gains in judgment-heavy work: planning, forecasting, risk assessment. Performance gains are clustering in decision-heavy work: decision-making quality (70 percent), decision-making speed (71 percent) and forecasting accuracy (64 percent). Transactional processes are improving too, but at smaller margins. Judgment-heavy work carries the most accumulated weakness in the finance function. It runs on inconsistent data, under-invested tooling, and the manual judgment built into the numbers. AI has more to gain here, and more leverage to deliver gains when it does. Banking and Technology lead on close efficiency and forecast accuracy, where structured data and regulatory discipline have already built the foundation AI needs.
Healthcare and Consumer trail Banking and Technology by double-digit margins on decision quality, close efficiency, and forecast accuracy. The forecast accuracy gap alone is 27 points. The reason is not effort or ambition. It is data: fragmented sources, slower integrations, and legacy systems that limit what
AI can act on. The implication for finance leaders is straightforward. AI's strongest returns in finance come from improving judgment, not from cutting transactional cost.
The next constraint is not technological. It is governance: the controls required to trust what AI produces. Building that trust requires strong governance, effective controls and human oversight to ensure that AI-generated outputs are appropriately reviewed and challenged. These foundations can help organizations scale AI more effectively while improving the consistency and quality of outcomes.
In the Philippine Context
AI adoption among financial institutions in the Philippines remains at varying stages, with many institutions still in the exploration stage compared with more advanced markets. However, the gap may narrow quickly as local institutions gain a better understanding of AI capabilities and move from experimentation toward practical use cases that lead to actionable business insights. From financial metrics and operational outcomes analysis, modeling and provisioning, and cash flow forecasting, financial institutions are also moving towards more complex applications in risk management, customer due diligence, fraud management, investment research, trading workflows and wealth management.
At the regulatory level, the Bangko Sentral ng Pilipinas (BSP) has expanded its use of AI to support more data-driven monetary policy by analyzing market sentiment and enabling faster and more informed decisions. At the same time, experts emphasize that AI should complement—not replace—human judgment, highlighting that its greatest value lies in improving the quality of decisions.
The BSP also recently introduced voluntary AI governance principles that encourage financial institutions to strengthen transparency, accountability, security, and human oversight. These developments reinforce that realizing value from AI requires more than adopting new technology. It requires trusted data, strong governance, and effective collaboration between finance, business, and technology leaders to ensure AI delivers meaningful business outcomes.
As AI continues to reshape the finance function, competitive advantage will increasingly depend not on how widely organizations adopt AI, but on how effectively they use it to improve decision-making and create long-term business value.
This article draws on insights from the KPMG Thought Leadership publication “KPMG Global AI in Finance 2026.”
© 2026 R.G. Manabat & Co., a Philippine partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. All rights reserved.
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This article is for general information purposes only and should not be considered as professional advice to a specific issue or entity. The views and opinions expressed herein are those of the author and do not necessarily represent KPMG International or R.G. Manabat & Co.