While 76% of financial organizations are actively deploying artificial intelligence, a new report warns that weak governance and poor data quality are stalling real business value. For investors, this creates a clear divide between firms successfully scaling AI and those just adding costs without clear, profitable results.
Financial institutions are moving quickly to implement artificial intelligence for financial planning, reporting, and analysis. However, the speed of adoption is creating a critical imbalance, according to a recent report by KPMG. While 76% of organizations have now integrated AI into their finance functions, the infrastructure and governance frameworks needed to manage these tools are struggling to keep pace.
It is important for investors to distinguish between broad adoption and actual performance. While 71% of firms report that their AI initiatives are meeting return on investment expectations, only 23% are seeing results that exceed their initial targets. This suggests that simply implementing AI software does not automatically lead to a significant competitive advantage or cost efficiency.
The report highlights three major operational risks that could weigh on financial performance: governance, data quality, and the talent gap. Many firms continue to struggle with fragmented or low-quality data, which remains a primary barrier to generating accurate, AI-driven financial forecasts. To manage these risks, 33% of companies are increasing human oversight—effectively adding manual checks to AI processes to ensure regulatory compliance and maintain professional judgment.
There is also a noticeable trend in how firms are managing their workforce. About 38% of companies are focusing on upskilling their existing employees to work alongside AI tools, while only 28% are actively hiring for new skill sets or redefining roles for human-AI collaboration. This preference for training current staff over bringing in new talent may impact the speed at which firms can effectively scale their AI capabilities.
For investors, the key monitorable moving forward is operational discipline. Success in this sector will likely depend on a firm's ability to integrate AI while maintaining strict data governance and oversight. Shareholders may want to monitor management commentary in future earnings reports to see if AI investments are leading to measurable productivity gains and better decision-making, or if they are simply increasing operational complexity without delivering a clear bottom-line benefit.