Harvard Data Science Review Publish: How to Define and Execute Your Data Strategy

So far, the article has received acclaim with Harvard Data Science Review Editor-in-Chief and Harvard University Professor of Statistics, Professor Xiao-Li Meng, citing in their opening editorial, “To complete the metaphor ‘from playroom to boardroom’ (and this lengthy editorial), the article by Ulla Kruhse-Lehtonen and Dirk Hofmann on “How to Define and Execute Your Data and AI Strategy”is a must-read (and I rarely use this phrase, even for my own articles) for any business leaders. It makes a host of concrete and practical recommendations, “from setting the ambition level to hiring the right talent and defining the AI organization and operating model.” Effective and timely communications within a business organization are among the most crucial priorities, not surprisingly. Indeed, this is the first time I had heard of an ‘AI strategist’ position, and I am not alone. As Kruhse-Lehtonen and Hofmann wrote, “Most companies do not have this role, but we see it as one of the most critical roles in the successful execution of Data and AI projects. Without an AI strategist, the communication distance between people with a business background and the data scientists is often too wide and can take some time to align.”

The publication written by Dirk and Ulla provides a detailed guide on how to define and execute your data and AI strategy, guiding readers with the following eight possible tasks:

1. Translate your business and digital strategy into your data and AI vision and strategy highlighting the biggest opportunity areas optimizing your current business as well as new innovative businesses utilizing AI and data.
2. Identify the business processes (product development, production, sales & marketing, supply chain, pricing, HR, finance, etc.) where you want to use data and AI.
3. Understand the current state of your data and AI capabilities.
4. Describe the target state for your business processes once data and AI capabilities have been deployed.
5. Define new data-driven business and product ideas.
6. Define your execution roadmap, including investments.
7. Execute the first data and AI use cases by creating your AI playbook, aiming at production readiness.
8. Automate and scale up operations.