In the rapidly evolving landscape of technology, generative AI has emerged as a transformative force. However, despite its potential, many executives face significant challenges in quantifying the returns on their investments in this innovative field. A recent survey conducted by Wakefield Research on behalf of Informatica revealed that over 97% of organizations struggle to showcase the tangible business value of their AI initiatives. This article delves into practical strategies that digital leaders can utilize to effectively measure and articulate the ROI of their AI projects.
Understanding the challenges of measuring AI ROI
One critical insight shared during a panel discussion at the Informatica World Tour event in London was the need for business leaders to have sufficient data to determine the viability of their AI efforts. Gro Kamfjord, the head of data at Jotun, emphasized the importance of data infrastructure in enabling informed decision-making. By modernizing their data systems through a collaboration with Informatica and Snowflake, Jotun created a centralized data hub. This upgrade not only facilitated rapid development but also allowed teams to prepare more effectively for AI integration.
Establishing a clear framework for evaluation
Kamfjord noted that while quantifying every detail of a project may not be essential, having enough information to halt a project if necessary is crucial. “Starting with a small, manageable initiative allows for scaling or cessation based on performance,” she explained. This pragmatic approach empowers leaders to focus on strategic decisions rather than getting bogged down by precise numerical values.
Engaging stakeholders in the ROI conversation
According to Nick Millman, a senior managing director at Accenture, assessing the overall value of AI projects can be complicated. He pointed out that emerging technologies often require foundational investments in data infrastructure that may not yield immediate returns. Millman highlighted the necessity of engaging with stakeholders across the organization to build support for AI initiatives. “Convincing decision-makers that AI is a worthwhile investment often hinges on effective communication and relationship-building,” he stated.
Three-pronged strategy for measuring ROI
Millman proposed a comprehensive approach to measuring ROI in terms that resonate within the organization. First, he advised using practical metrics that make sense for the specific business. “Tracking every detail through complex spreadsheets can be overwhelming. Instead, focus on straightforward measurements that align with your organization’s goals,” he suggested.
Secondly, engaging business stakeholders is vital. Millman warned against data teams presenting value findings in isolation. “Collaboration ensures that the perceived value of the project is recognized across departments, maintaining its credibility.” Lastly, he recommended involving finance professionals early in the process, as their expertise in constructing business cases can significantly enhance the investment appeal of AI projects.
Long-term vision and stakeholder alignment
Boris van der Saag, EVP of data foundation at Rabobank, echoed the sentiment of patience in ROI realization. He underscored the importance of focusing on long-term benefits rather than immediate returns. “Articulating the narrative around long-term goals is essential when discussing ROI with senior management, who are often less patient,” he remarked.
Van der Saag highlighted the necessity of fostering a two-way dialogue between finance and data teams. By asking, “What can I do to leverage the data?” the CFO becomes more invested in the success of the AI initiatives, leading to more fruitful discussions about potential opportunities.
Storytelling as a powerful communication tool
Farhin Khan, head of data and AI at AWS, emphasized the significance of storytelling in conveying the value of AI projects. “When discussing outcomes, pivot from a purely numerical ROI perspective to understanding the broader impact on business objectives,” she advised. Tailoring the narrative to resonate with specific stakeholders can enhance the effectiveness of communication.
Khan encouraged leaders to illustrate how AI use cases contribute to overarching business transformations. “If your organization is exploring new markets, connect the dots to show how each AI initiative supports that goal,” she suggested. This cohesive narrative can foster greater buy-in from stakeholders and enhance the perceived value of AI investments.
Collaboration and accountability for success
Kenny Scott, a data governance consultant at EDF Power Solutions, pointed out that successful measurement of AI ROI relies on robust collaboration among all involved parties. He stressed the importance of clarifying roles and responsibilities to prevent isolated efforts that could derail progress. “Consistent questioning and communication ensure that everyone is aligned and aware of their contributions,” he stated.
Scott’s experience in establishing a modern data infrastructure demonstrates that effective value delivery hinges on setting clear targets and managing expectations. “Outline the anticipated costs and returns, and adhere to timelines to avoid scope creep,” he advised. A well-coordinated approach will help maintain focus and drive successful outcomes.
