This podcast examines the challenges and strategies for deriving return on investment (ROI) from generative AI (GenAI) investments. It then highlights the significant economic potential of GenAI, citing projections of trillions of dollars inadded value, while also cautioning that enthusiasm currently outpaces understanding.

The podcast then delves deeper into specific challenges in measuring GenAI ROI, such as the lack of standardised metrics, the complexity of attributing impact, and the difficulty in quantifying intangible benefits. It advocates for a balancedapproach to key performance indicators (KPIs), incorporating both financial and non-financial metrics.

Furthermore, it provides a balanced approach to key performance indicators (KPIs), incorporating both financial and non-financial metrics. Furthermore, the podcast outlines a framework for setting up and measuring ROI, including defining objectives, establishing performance baselines, and continuously improving implementations; as well as the importance of addressing data complexity, ethical considerations and business environment changes.

The challenges are evident: a lack of standardization, complexities in attribution, and benefits that are often hard to quantify. However, a practical and iterative approach—guided by clear objectives, human oversight, and data-driveninsights—can unlock the full potential of generative AI.

Organizations that perceive ROI as an ongoing process, continuously refining their strategies and metrics, will be best positioned to transform AI investments into measurableimpacts.

The true value of generative AI goes beyond cost savings and efficiency gains—it lies in its ability to transform processes, stimulate innovation, and empower better decision-making. In this rapidly evolving landscape, those who succeed will bethose who redefine ROI, striking a balance between quantifiable financial outcomes and strategic, long-term contributions.