The elements of a comprehensive AI strategy
"In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy.“ Andrew Ng
There is little doubt that AI will become relevant for all companies, regardless of their industry or size. When it comes to creating value from AI by implementing AI applications, several pitfalls can be observed in practice – including the isolation of AI use cases, the lack of resources and capabilities, and a poor understanding of use cases and applications.
To avoid this, a systematic approach towards AI is needed. Therefore, from the very beginning, you need to be clear on the overarching objectives or purpose of your company: What is its goal? Furthermore, it is necessary to understand how AI can help to achieve your objectives.
A comprehensive AI strategy consists of three parts: an AI vision, a portfolio of AI use cases, and a clear strategy for the required enabling factors.
A company’s AI vision sets the high-level goals of any AI application to be developed or deployed. It includes an understanding of the current position of the company, of its competitive position and industry dynamics, and of potential changes to the industry’s business model. On this basis, it can be decided where the organization could benefit most from AI − within a specific product or service and/or by improving processes.
The vision needs to be translated into a portfolio of AI use cases. To build this portfolio, you need to identify and prioritized relevant use cases.
To execute the use cases a set of enabling factors is required concerning the organization, the people, the technology, and the AI ecosystem.