In the second circle are our business process areas such as finance, commercial operations and aftersales, procurement, R&D, production and logistics, and more. These are the mature domains where AI will provide the most business value. Data liquidity is the third circle. What available data sets do we have now that don’t need transformation? The overlapping area is where we have immediate value creation opportunities.
What’s been the early output of your approach to baking AI into your transformation?
In March, we were initiating AI within certain functions, and today we have more than 50 cross-domain, enterprise-wide business ideas in backlog, and we’re kicking off three beta use cases now. By clearly weighing business value alongside our ability to act and execute, rather than relying on generic decision criteria, we’re able to select lead management for our commercial business, spend analytics for procurement, and dealer-network customer service for aftersales. Using this lens, we also brought in tech, product, operations, and external partners into a co-innovation lab with pods for each business area. The goal of the lab is to develop an agile use-case delivery model, which we’ll fine-tune and scale beginning in 2026.