The elements of a comprehensive
„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.
Vision The first and central element of any AI strategy is the AI vision. It describes how a company wants to apply AI and sets high-level goals of AI applications.
AI vision should also be in line with the overarching corporate strategy. The two should be closely linked – but also monitored.
Cases Based on its AI vision, a company needs to identify relevant use cases. These use cases are a clearly defined set of activities to reach a specific goal.
They are closely linked to enabling factors since use cases a company wants to implement determine the requirements for data, talent, and infrastructure.
factors Enabling Factors are needed to execute and scale use cases. This includes the organization, the people, the technology, and the AI ecosystem required to execute and scale the use cases.
The factors are closely linked to AI use cases as the available resources also determine which use cases can be implemented in the short term.
Product/service-centric AI includes augmentation of existing products and creation of new AI-driven products.
Process-centric AI includes support of existing processes and disruptive transformation of processes.
Thereby it should be considered how AI is currently used and how AI will be used by one’s own company and industry competitors, which assets and capabilities are available at one’s own and at competitors’ company and which new digital disruptors (might) move into the industry.
Then, these use cases need to be defined in a clear and comprehensive way.
After this can each use case be prioritized based on its potential value for the implementing company as well as the expected level of complexity.
This part again can be subdivided into structure and governance.
Talent It is needed to get employees ready for AI and recruit the necessary talent. New roles are emerging: AI engineers are required who can build learning systems from an engineering perspective. Getting the right people with the right skills is currently a major challenge. As hiring is hard, reskilling your existing data scientists or software engineers might be an important option.
Collaboration Employees, including the executives, need to have a basic understanding of what AI will enable and how it will change their working lives (e.g. changing collaboration). You will have to bring everyone on board, as silent resistance at various points within a company can be detrimental for the success of an AI project due to the iterative approach that is required.
Infrastructure The right IT infrastructure is needed. One principal decision is whether to use one’s own servers and GPUs or rely on the cloud. This question is not only about data security but also about cost and economic feasibility.
companies Working with startup companies and exchanging knowledge with them can have significant potential for both parties and lead to synergetic effects.
partners Besides startup companies and academia companies can also be searched for other partners to e.g. cooperate and profit by each other’s experiences and knowledge.