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AI Strategy

AI strategy is about having a clear view of how to enable the organization to utilize AI capabilities. An AI strategy should encompass the following subsets of strategies.

  • Data Strategy Data strategy covers data management, accessibility, and utilization of data.
  • Machine Learning Strategy Machine learning strategy covers the implementation of infrastructure, the serving of models, inferencing, and monitoring of machine learning models, and implementation of machine learning operations and platforms.
  • Data Science and Analytics Strategy. The Data Science and Analytics strategy covers the implementation of a guideline for data science and integration with business. Data Science should bridge business use cases and the implementation of analytical and statistical methods.

Note: The use of AI strategy instead of machine learning strategy is because of the more technical nature of the term machine-learning versus the more ambiguous term AI

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Thoughts

  • It is more important for business stakeholders to focus on viable business use cases and make measurable KPIs than finding out what machine learning can be utilised for. Machine Learning is just a means to an end, not the end goal itself. Let the machine learning engineers figure out the way there, that is what they are paid to do.
  • Machine learning projects fail because there are no actionable effects from the output. The output of an AI project in itself is worthless; it is the business value that matters.