Corporate boards embrace AI, but bottom-line impact lags

AI applications are also being deployed to support corporate social responsibility and sustainability initiatives. By automating data collection and analysis, AI can help firms track environmental performance, monitor supply chain risks, and assess compliance with ethical standards. In theory, these capabilities strengthen corporate accountability and align business operations with stakeholder expectations.

New research suggests that while AI adoption is expanding across industries, its promised financial gains have yet to materialize in measurable and consistent ways.

The study, titled “Emerging Use of AI and Its Relationship to Corporate Finance and Governance,” published in the Journal of Risk and Financial Management, examines whether companies that make extensive use of artificial intelligence actually outperform their peers. The research offers one of the most up-to-date assessments of AI’s financial impact by analyzing firm-level performance during the 2024 fiscal year.

AI’s expanding role in corporate finance and governance

Artificial intelligence has moved well beyond experimental applications and into the operational core of modern corporations. The study outlines how AI systems are now widely used in corporate finance to support tasks that were traditionally labor-intensive, error-prone, or slow. These include assessing credit risk, analyzing financial documents, forecasting corporate default, detecting fraud, and supporting audit functions.

In the governance domain, AI is increasingly positioned as a tool that can strengthen board oversight and decision-making. By processing large volumes of financial and operational data in real time, AI systems can enhance transparency, improve risk monitoring, and provide directors with more timely insights into firm performance. The researchers emphasize that AI’s analytical capacity allows boards and executives to move beyond static reporting and toward continuous oversight of strategy execution, compliance, and sustainability goals.

The study situates this expansion within broader economic and organizational trends. Competitive pressures, shareholder expectations, and the pace of technological change have all incentivized firms to adopt AI as part of their strategic innovation portfolios. Rather than evaluating AI projects solely through short-term financial metrics, many firms now frame AI adoption as a long-term investment aligned with strategic priorities such as resilience, efficiency, and governance quality.

AI applications are also being deployed to support corporate social responsibility and sustainability initiatives. By automating data collection and analysis, AI can help firms track environmental performance, monitor supply chain risks, and assess compliance with ethical standards. In theory, these capabilities strengthen corporate accountability and align business operations with stakeholder expectations.

Yet the study makes clear that adoption alone does not guarantee effective integration. Many firms use AI in isolated functions rather than embedding it across decision-making processes. This fragmented approach may limit AI’s ability to generate measurable financial returns, even as it improves internal controls and governance practices.

Financial performance shows no clear AI advantage

To evaluate whether extensive AI use translates into superior financial outcomes, the researchers conducted two complementary analyses. First, they compared firms identified as leading AI users with industry averages across key performance metrics. Second, they matched these AI-intensive firms with comparable companies operating in the same industries to assess differences in profitability, efficiency, and market risk.

Across both analyses, the results were consistent. Firms that make heavy use of AI did not demonstrate statistically significant advantages in gross profit margin, net profit margin, return on equity, or market risk compared with their peers. While some AI-using firms showed higher average performance on certain measures, these differences were not strong enough to support claims of a systematic financial edge.

The findings align with emerging evidence from other recent studies suggesting that AI’s financial impact is often delayed. In many cases, firms incur substantial upfront costs related to AI development, data infrastructure, and organizational change. These investments may take years to translate into productivity gains or improved financial outcomes, particularly if AI systems are not fully integrated into core business processes.

The study also highlights a critical distinction between operational benefits and financial results. AI can improve accuracy, speed, and oversight without immediately boosting profits or shareholder returns. For example, enhanced fraud detection or better risk management may reduce losses or volatility rather than generate new revenue. These benefits are real but may not be captured in traditional financial performance metrics over short time horizons.

Another factor limiting measurable impact is uneven organizational readiness. The researchers note that relatively few corporate directors currently possess deep knowledge or experience with AI. This gap can constrain boards’ ability to oversee AI strategy effectively, evaluate risks, and ensure that AI systems are aligned with corporate objectives. Without informed governance, AI adoption may remain superficial or underutilized.

The findings also echo concerns raised in prior research on generative AI, which has shown that many firms investing heavily in AI tools fail to realize meaningful returns because systems are not sufficiently tailored to organizational needs. AI that operates as a standalone tool rather than an adaptive component of business processes is less likely to drive sustained performance improvements.

Governance gains may precede financial returns

The study finds no immediate financial premium associated with AI adoption, but it identifies significant potential benefits in the realm of corporate governance. AI systems can enhance board performance by improving access to timely, data-driven insights and enabling more rigorous oversight of management decisions.

The research underscores AI’s value in risk management, where predictive analytics can help firms anticipate supply chain disruptions, regulatory changes, and labor market trends. By identifying emerging risks earlier, AI supports more proactive decision-making and reduces the likelihood of costly surprises. These governance improvements may not directly boost profits but can contribute to long-term stability and resilience.

AI also plays a growing role in auditing and financial reporting. Automated analysis and continuous monitoring can reduce errors, strengthen internal controls, and increase confidence in reported results. For boards and investors, these improvements enhance trust and transparency, which are essential foundations for sustainable value creation.

In fraud detection, AI’s pattern recognition capabilities allow firms to identify anomalies and suspicious activity more effectively than traditional methods. Preventing financial misconduct protects both firm value and stakeholder confidence, even if the financial benefits are primarily defensive rather than growth-oriented.

The study suggests that these governance-related advantages may represent the early stage of AI’s impact curve. As firms gain experience, refine implementation strategies, and develop AI expertise at the board level, stronger links between AI use and financial performance may emerge. The researchers caution against interpreting current results as evidence that AI lacks value, emphasizing instead that adoption is still in a relatively nascent phase.

Importantly, the findings challenge the narrative that AI is a guaranteed path to superior financial outcomes. They point to the need for more realistic expectations and more rigorous evaluation of AI investments. Firms that treat AI as a strategic capability rather than a quick fix may be better positioned to capture long-term benefits.

The study calls for further research to track AI’s impact over longer time horizons and across broader samples of firms. Future analyses could examine how financial performance changes before and after AI adoption, how market reactions respond to AI-related announcements, and how organizational factors such as governance quality influence outcomes.