As I mentioned in my previous posts, the relationship between different strategies is manifest. If you want to do AI you need to take care of data. And from there to the broad topic of Digital Transformation it’s not too far. Boyan Angelov gives very good examples on how to start with those topics in his book. Reading recommendation.
🤔 𝗛𝗼𝘄 𝗱𝗼 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗼𝗻 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗲𝘃𝗲𝗹𝘀 𝗿𝗲𝗹𝗮𝘁𝗲 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿? Yesterday I did some thinking with Christina Albrecht. At the moment it feels like the use cases that are the product of the AI strategy are the value drivers, but we should not forget the full picture. 🎯 For the long-term success of any AI products we need to remember that there is a hierarchy of strategies in any organization, and they need to be aligned (there are always feedback loops between them, but they are rarely efficient). Here are the four lessons: ⏺ Align the strategies on all levels (with KPI value trees) ⏺ Shorten the value chain ⏺ Data and domain knowledge silos need to be removed ⏺ Establish the same language at all levels (for example, using Domain-Driven Design) There is space here for software solutions. Data catalogs become the norm (i.e. Databricks Unity), and there is very exciting work from companies such as YOOI and Mindfuel. I'll be looking into solutions, so if you have ideas let me know! #datastrategy
Data and AI Strategy Leader | O'Reilly Author
2moThank you so much for sharing, Dubravko!