Measuring ROI Of AI Initiatives: Essential Strategies For Business Leaders

As artificial intelligence (AI) increasingly infiltrates the business world, many executives are grappling with a critical question: how can we measure the return on investment (ROI) of our AI initiatives? With predictions that nearly one-third of AI projects could be abandoned by 2025 due to soaring costs that outstrip perceived benefits, the stakes are high. In this environment, CFOs are under growing pressure to justify AI investments in terms of financial and strategic gains. But as João Carvalho, Managing Director of SAP Concur in Southern Europe, Middle East, and Africa, points out, “Determining the return on investment (ROI) of AI can often feel like solving a puzzle with missing pieces.”

Carvalho adds that “AI in the workplace is still in an early phase, with few established templates for implementation and success,” which introduces both potential and complexity. With variable market conditions, fluctuating adoption rates, and considerable operational costs, CFOs are challenged to frame the true value of AI initiatives. For finance leaders navigating this maze, Carvalho offers four essential tips to help organizations maximise AI’s potential:

1. Adopt Multi-Disciplinary Standards for Success

Successful AI initiatives are seldom confined to a single domain—they span across data management, governance, security, and results tracking. As Carvalho notes, achieving robust returns on AI investments requires a multi-faceted approach akin to that of an Olympic decathlete excelling in multiple events. A report by Deloitte underscores that top-performing companies maintain high standards in these areas, using quality data to power AI models, rigorous results tracking to adapt strategies, and comprehensive security protocols to protect data and uphold ethical standards. By setting a broad foundation of standards, organizations can foster a fertile environment for sustainable AI ROI.

2. Secure Early Wins and Leverage Initial Insights

The transformative potential of AI often prompts organizations to focus on grand-scale impacts. However, emphasizing early wins is critical to demonstrate value and secure continued investment. Early successes in pilot projects can serve as proof points, helping to keep stakeholders engaged and justify additional resources. Carvalho advises finance leaders to view AI ROI as “more art than science,” with the need to balance quantifiable returns with strategic insights. By expanding on successful pilots, organizations can more clearly align AI insights with overarching business goals, creating a pathway to sustainable, long-term investment.

3. Tailor Metrics to Organizational Goals

Traditional ROI metrics can fall short when applied to AI. Standard financial indicators may not capture the nuanced benefits of AI, such as quality improvements, innovation, and compliance. As a result, tailored metrics that align with an organization’s specific goals are crucial. According to a report by IDC, it often takes around 14 months post-deployment for organizations to realize AI ROI, indicating that patience is as important as precision. By moving beyond conventional KPIs and considering diverse factors, finance leaders can develop a measurement framework that better reflects AI’s multi-dimensional value.

4. Acknowledge the Human Aspect of AI ROI

People are central to any successful AI initiative. In fact, talent availability and development play an essential role in realizing AI’s ROI, with 52% of companies citing a lack of skilled workers as a primary challenge. Beyond technology investment, AI requires continuous workforce training and upskilling. For many organizations, the goal is not to replace human roles but to enhance team capabilities and attract new talent. As the Institute of Management Accountants notes, neglecting AI can risk losing young professionals eager to work with advanced tools. Embracing AI also presents a unique opportunity for talent retention and attraction, essential elements in today’s competitive landscape.

“Evaluating AI projects with a portfolio-based approach—rather than assessing one project at a time—allows organizations to consider factors like talent attraction and development,” explains Carvalho. Cross-departmental collaboration, especially with IT, is equally vital, as it ensures that AI initiatives are well-integrated and strategically aligned. According to the SAP Concur CFO Insights report, 53% of finance leaders highlight the importance of partnering with IT, with 49% focusing on digital transformation and 24% on cybersecurity.

Build or Buy? Finding the Right AI Strategy for ROI

For organizations at the crossroads of AI implementation, the decision to build or buy AI solutions can impact ROI. Developing custom AI solutions offers a level of exclusivity but requires significant time and resources, while purchasing pre-built solutions can deliver quicker results. As Carvalho notes, “Organizations today have many options for AI implementation, and a key consideration is whether to build or buy. Partnering with experts to understand the ROI equation can help you calculate the best mix for your organization—wherever you are on the AI journey.”

Whether an organization is just beginning its AI journey or is looking to optimize its existing initiatives, understanding how to measure ROI is essential for sustainable success. By balancing financial metrics with strategic goals, acknowledging the human factor, and collaborating across departments, finance leaders can capture a clearer picture of AI’s true value. AI’s return on investment may not always be easy to quantify, but by adopting these strategies, organizations can navigate the complexities and unlock AI’s transformative potential.