The 6 Key Elements of an AI Strategy
Artificial intelligence (AI) is revolutionizing the way organizations will operate in the future. It is a cornerstone of the digital transformation and will significantly change many business areas we know today. Based on a recent study conducted by Accenture, three quarters of nearly 1500 interviewed C-level executives are afraid of going out of business unless they scale AI [1]. Companies around the globe face the challenge to successfully anchor and spread AI technology in their organization in a value-adding manner.
Creating value from AI not only requires operational measures such as technology and infrastructure ramp-up. Softer factors, such as the ability to keep up with this exponentially changing technology and an effective management of the related organizational change is equally important. Due to the significance and impact of AI in general, as well as the corresponding complexity, companies must engage in developing an AI strategy [2].
An AI strategy helps to plan and steer the actions that need to be taken in order to unfold the impact and change of AI on different parts of the organization.
Developing a strategy for AI adoption across the organization is a non-trivial task. Many companies struggle to identify and discuss the relevant key aspects of such a strategy. At my company STATWORX, we are commonly working with many organizations on their AI strategy endeavors. Based on our many years of AI consulting and development experience from over 300 projects, we have identified six key elements that companies need to consider when developing their AI strategy.
1) Data: Data has become value-added raw material and must be considered as an asset to unfold competitive advantages. It is the foundation for any AI initiative and must be fostered, embraced, and properly governed.
2) Use Cases: The power of AI originates from significant use cases. Those use cases may, but do not have to, extend to data-driven business models. A structured approach for identifying and prioritizing use cases along the different stages of AI maturity is critical for scaling AI to all important areas of business within the organization.
3) Team & Skills: Successful AI teams require skills and roles from various fields that are changing along the path of AI product maturity. The ability to assemble, retain, and grow a multidisciplinary team of experienced talents has a significant impact on the company’s AI success.
4) Infrastructure: A flexible and scalable IT infrastructure for AI endeavors sets the preconditions to execute projects efficiently and to adapt quickly to new emerging needs.
5) Organization: The dynamics of AI projects call for agile project organization forms and the willingness and ability to change the organization’s culture towards data-driven decision making. This also includes decisions that have to be made towards an organizational and operational model.
6) Governance: Ensuring compliance with ethical principles, core regulatory requirements for data an AI, and technical robustness is mandatory for developing and operating trustworthy and sustainable AI systems.
Based on those six key elements, the most important strategic decisions for the successful implementation of AI initiatives can be derived. However, an AI strategy is not static but more of a moving target that continually has to be re-aimed.
In our AI strategy model, six dimensions have to be discussed and considered for action making. Within those six dimensions, companies typically show different levels of maturity that must be assessed before defining target scenarios and deriving corresponding action items.
Data has to be thought of as a raw product that has to be served across business domains. It is the cornerstone for the application of artificial intelligence within organizations. Extracting valuable information from data is a challenging task due to its increasing volume, variety, and velocity.
