2026年全球金融业人工智能研究报告_决策优势_22页_4mb

KPMG Global AI in Finance 2026 Summary

Core Content

KPMG's AI in Finance 2026 report explores how AI is transforming the finance function, emphasizing its role as a decision-engine rather than a cost lever. The findings are based on a survey of 1,013 senior finance leaders across 13 sectors and 20 countries, highlighting the maturity gap between AI adoption and performance realization.

Main Findings

• AI Adoption Growth: Active AI use across the finance function has more than doubled in two years, with over 75% of organizations now using AI in financial planning, reporting, and commercial analysis.
• Performance Gains: The strongest performance improvements are in judgment-heavy areas such as decision-making quality, forecast accuracy, and responsiveness. Agentic AI, which can plan, reason, and act autonomously, delivers the most significant gains, with organizations using it reporting 32% to 40% stronger performance in these areas.
• Sector Differences: There are notable gaps in performance between sectors. Banking and Technology lead in areas like close efficiency and forecast accuracy, while Healthcare and Consumer lag significantly, particularly in forecast accuracy (27-point gap) and ROI.
• ROI Expectations: 71% of organizations report that AI is meeting or exceeding ROI expectations, but only 23% report it exceeding expectations in key metrics, indicating a disparity between adoption and impact.

Key Themes

1. AI as a Decision-Engine

• AI is not just about automation or cost reduction. It's enhancing judgment and decision-making in finance.
• Organizations that deploy agentic AI see stronger performance gains, especially in areas that require strategic thinking and risk assessment.
• The real value of AI lies in sharpening human judgment, not merely speeding up tasks.

2. Governance and Controls Build Confidence

• Strong governance, controls, and human oversight are critical for trust and scalability in AI deployment.
• Assurance-ready organizations report 3 to 6 times the rate of significant improvement in key metrics.
• The ability to produce audit trails and evidence is a key differentiator for high-performing finance functions.

3. The Assurance Readiness Gap

• Only 42% of all organizations are strongly assurance-ready for AI-enabled finance processes.
• 60% of agentic AI leaders are assurance-ready, but a significant gap remains.
• Assurance readiness is no longer an internal concern but a commercial and regulatory requirement.

4. Data Quality and Workforce Gap

• Data quality is the most cited barrier and opportunity for AI in finance.
• Organizations are struggling with fragmented data sources, slow integrations, and legacy systems.
• Only 28% of organizations are rethinking the types of talent they need, indicating a workforce gap in AI capabilities.
• Human oversight remains crucial, especially in ensuring trust in AI outputs.

Sector Performance Comparison

Recommendations for Finance Leaders

• Focus on Judgment-Heavy Work: Prioritize areas where AI can enhance decision-making, not just automate routine tasks.
• Invest in Governance and Controls: Build a Trusted AI framework that includes fairness, transparency, explainability, accountability, and data integrity.
• Track AI-Related KPIs: Formal tracking of AI performance metrics is a strong indicator of success.
• Develop a Total Workforce Model: Move beyond training to rethinking the human-AI operating model.
• Address Data Quality: Focus on cleaning priority data for AI use cases, not the entire data estate.

KPMG's Role

KPMG is helping organizations:

• Modernize their data estates.
• Implement governance and control frameworks.
• Build assurance readiness through risk assessments and audit trails.
• Scale AI responsibly with ethical AI principles and human oversight.

Conclusion

AI in finance is moving beyond efficiency and into decision-making. The key to success lies in trust, governance, and judgment enhancement. While adoption is widespread, performance is uneven, with Banking and Technology leading the way. The assurance readiness gap and data quality issues remain critical challenges, especially for sectors like Healthcare and Consumer. KPMG emphasizes the need for a holistic approach to AI in finance, integrating technology, people, and processes to achieve sustainable value.