Maximizing ROI from AI: Strategies for Finance Leaders

Mar 22, 2026, 2:26 AM

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The integration of Artificial Intelligence (AI) and Generative AI (GenAI) has gained momentum in finance departments, yet many organizations struggle to achieve satisfactory returns on their investments. While enthusiasm for AI's potential is high, actual returns often fall short of expectations. A recent survey by Boston Consulting Group indicates that only 45% of finance executives can quantify their ROI from AI initiatives, with many reporting returns below 10%.

To navigate this complex landscape, finance leaders must adopt targeted strategies that focus on delivering value and aligning AI initiatives with broader business objectives. Here are some proven tactics to enhance ROI from AI in finance.

Focus on Value from the Start

Successful finance teams prioritize value creation over mere experimentation with AI technologies. Instead of pursuing open-ended learning, high-performing organizations emphasize quick wins that can demonstrate immediate value. This approach can increase the likelihood of successful AI implementation by approximately 6 percentage points.

Allocating a dedicated budget for AI initiatives is crucial. By treating AI investments with the same rigor as any major financial commitment, organizations can ensure that projects demonstrating tangible value receive continued funding. This disciplined approach helps avoid wasted resources on less impactful initiatives.

Systematic Tracking of ROI

While some finance leaders acknowledge the importance of tracking ROI, many struggle to set up effective measurement systems. Common challenges include quantifying benefits from intangible improvements, such as enhanced insights or streamlined operations. However, finance teams can use proxies, such as reductions in resource load or improved forecasting accuracy, to gauge impact effectively.

This systematic tracking not only anchors AI initiatives but also reduces the risk of teams diverting their efforts toward unnecessary features. By continuously monitoring performance metrics, finance leaders can make informed decisions about which initiatives to scale and which to abandon.

Broaden the Transformation Perspective

Integrating AI into a comprehensive finance transformation strategy enhances the probability of success. Rather than treating AI initiatives as isolated projects, organizations should adopt a "string-of-pearls" approach that connects various use cases. This interconnected strategy allows the underlying investment in data and technology to yield greater returns, facilitating broader organizational redesign and efficiency improvements.

For instance, a consumer goods company's CFO restructured the financial planning and analysis department by implementing a driver-tree model that links operational metrics to financial outcomes. This model allowed for quicker report generation and more accurate forecasts, demonstrating the power of a comprehensive transformation strategy that leverages AI effectively.

Promote Deep User Adoption and Change Management

Achieving transformational scale requires deep user adoption of AI systems. Finance teams must understand how to utilize these new tools effectively, including their limitations and the importance of validating results. Change management emerges as a critical factor for success; investing time in stakeholder management can help ensure buy-in from all levels of the organization.

Leaders must communicate the benefits of AI clearly and provide structured training to equip employees with the skills needed to leverage these technologies. While many finance professionals feel confident using AI tools, only 39% of companies currently offer formal training programs, highlighting a significant gap that needs to be addressed to maximize the impact of AI initiatives.

Collaborate with IT and External Partners

Finance functions should not attempt to develop all AI solutions in-house. Successful organizations often collaborate closely with IT departments and external technology partners to leverage their expertise. This collaboration can enhance the effectiveness of AI implementations, as finance teams can focus on strategic activities rather than getting bogged down in technical details.

By working closely with IT, finance leaders can ensure that the necessary infrastructure and technical support are in place, leading to higher success rates for AI projects. Additionally, considering available solutions from software vendors rather than building custom tools can improve ROI significantly.

Address Implementation Challenges Head-On

Despite the promise of AI, organizations often face significant barriers to implementation, including data quality issues, compliance requirements, and high costs. A proactive approach to addressing these challenges is essential for unlocking AI's full potential. Many finance leaders cite concerns about data integrity and the need for clear governance frameworks as critical hurdles to successful AI adoption.

To mitigate these risks, finance teams should develop comprehensive policies for risk management, ensuring that AI initiatives are transparent, accountable, and compliant with regulatory standards. By addressing these concerns early in the process, organizations can build trust and reduce the likelihood of disruptions.

Conclusion

As finance functions increasingly adopt AI technologies, the potential for enhanced ROI is significant. However, realizing this potential requires a strategic approach that emphasizes value creation, effective tracking, broad perspective, user adoption, collaboration, and proactive risk management. By implementing these strategies, finance leaders can navigate the complexities of AI adoption and drive transformative change within their organizations.

With the right focus and dedication, AI can elevate finance from a transactional role to a strategic partner, delivering substantial returns on investment and paving the way for future growth.

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