AI Strategy


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.

AI Strategy
aligned with overall strategy

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.
AI Use
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 Choosing the fields of application for AI is one part of setting an AI vision. Therefore it is necessary to decide whether applications should focus on product/service-centric AI or process-centric AI.
Product/service-centric AI includes augmentation of existing products and creation of new AI-driven products.
Process-centric AI Choosing the fields of application for AI is one part of setting an AI vision. Therefore it is necessary to decide whether applications should focus on product/service-centric AI or process-centric AI.
Process-centric AI includes support of existing processes and disruptive transformation of processes.
Competitive Positioning Understanding the competitive positioning and industry dynamics is one element to define an AI vision.
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.
Ideation and Priorization The first step in that phase is to identify use cases that build upon strategic goals (opportunity driven) as well as existing strength (asset and capability driven).
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.
Value chain configuration After strategically identifying and prioritizing AI use cases these prioritized use cases need to be executed. For this, companies should develop a clear plan considering a value chain configuration.
Organization Organization is one of the factors that are necessary to execute and scale use cases. Therefore companies need to set up the right AI organization.
This part again can be subdivided into structure and governance.
Structure One part of the right organizational setup is the company’s internal structure. E.g. location and hierarchy of teams need to be adapted in accordance with the AI vision to execute the AI use cases.
Governance To successfully integrate AI applications a right governance structure is needed. This includes e.g. changes to the board roles - which can be very long and difficult processes.
People The employees of a company play an important role when it comes to organizational changes which include the application of an AI strategy. Not only can it be hard to recruit the right personal but also to change employees’ minds and cultural thinking as AI can change the way people collaborate.
Knowhow &
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.
Culture &
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.
Technology Besides organization, people and ecosystem a company needs to build up the required technology. This includes the AI infrastructure as well as the data.
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.
Data Data certainly is the key element of applying AI, as training AI models requires a great deal of it. If a company does not already have well-defined data governance it is often unlikely to have useful data. You’ll need to identify data sources, build data pipelines, clean and prepare data, identify potential signals in your data, and measure your results.
AI ecosystem Besides internal considerations it is also necessary to address the ecosystem. At this point, no company has truly comprehensive experience when it comes to applying AI. Therefore, a company should exchange knowledge externally – with startups, academia, and other companies.
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.
Execution To implement AI use cases a well defined plan to execute them is needed. During the whole phase KPIs and use cases portfolio should be reviewed since AI systems learn continuously as new data is fed into the system.


The elements of a comprehensive
AI strategy

You find more details in our white paper “Applying AI: The elements of a comprehensive AI strategy”.