Björn Röber’s Post

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Partner & Co-Head of Digital Practice

A month ago, I shared insights on AI strategy and the key building blocks of an AI roadmap. Today, I want to dive deeper into defining a quantitative performance target for AI transformations. To make this concept more concrete, let’s look at the example of Klarna, where CEO Sebastian Siemiatkowski has set a bold and much-discussed objective: reducing the company's workforce from 5,000 to 2,000—around a 60% decrease—in the medium term. This target sparked widespread attention, especially as Klarna, a multi-billion-dollar fintech, is still experiencing rapid growth with no signs of slowing down. When it comes to setting the right performance target for AI transformations, we typically apply a triangulation approach. I recommend combining three critical components: a top-down benchmarking, a bottom-up workforce analysis, and a validation of the financial implications. Each element offers a unique lens to ensure a holistic target-setting process: 1) Top-down benchmarking provides a clear view of how industry leaders and best-in-class competitors are performing. This serves as a solid foundation to identify where performance improvements can be made. 2) Bottom-up workforce analysis goes function-by-function and job cluster-by-cluster to assess the current potential for automation, as well as opportunities for effectiveness improvements. This analysis effectively defines the upper limit of what AI can achieve for the organization. 3) Finally, analyzing the impact on the financial plan helps determine whether the target is ambitious enough and aligned with the company’s long-term objectives. These three components are interconnected and collectively allow for a well-rounded determination of the right performance target for an AI transformation. Of course, the specifics of the approach need to be tailored to each organization’s unique situation. For instance, the workforce analysis must take into account the current level of automation and the complexity of the business. However, I hope this example provides a useful framework to help you think through the challenge of setting effective AI performance targets. #AI #Transformation #PerformanceTarget

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