THE CONVERGENCE OF BUSINESS AND technology strategies in other industries (for example, Walmart, Amazon, Tesla) is also transpiring in asset management. This convergence implies that the business strategies and technology strategies are not developed separately but instead are viewed as integrated, interdependent, and identical. That is why the performance of AI business strategy is measured by looking at the two components: (1) Was our overall strategy influential and are we achieving results? and (2) Were we successful in deploying and operationalizing our strategy via AI project implementation and building industrial-scale enterprise machine learning? The first one is a measure of business results and denotes our ability to formulate good strategies. The second one is a measure of our execution and operationalization of the stated strategies.
We have come a long way. Our journey began with strategy and design—which linked our business strategy with our AI strategy. Our design and data practices enhanced our AI capabilities symbiotically. When we manage data effectively, we can build more AI artifacts faster, and our artifacts perform better. When we come up with good designs, we can create value through automation and insights. Once we had data under control, we entered the modeling stage. In modeling we recognized that we are dealing with a large set of potential learning models and approaches. We learned about the three primary approaches for teaching ...
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