Archetypes of Artificial Intelligence Utilization – How companies create and capture value from a novel business technology

Abstract

Artificial Intelligence (AI) has gained tremendous interest and traction in business use during few recent years. This has created a demand for more strategic understanding of its use and its capabilities in a business context. The core question, then, is how does AI create value for the companies who use it in their business and how do the companies capture that value?

In order to understand and materialize the value creation mechanisms of AI in business use, this thesis constructs archetypes of utilization for the technology. These archetypes serve as templates that include both examples of use within the single archetype and the strategic reasoning behind the utilization. They are general enough to provide a large variety of companies with valuable information on AI use but specific enough to also deliver real managerial value.

Academically this thesis is rooted in the scholarly discussions on innovation management, technology strategy and business model research. These domains of knowledge are studied carefully in order to understand what the salient dimensions are to assess the business decisions that have gone into the use of a certain technology, in this case AI, in various business use cases. Business model research is especially important in this regard as a source of literature because of its focus on the questions of value creation and capture from technologies and products with latent value.

The main research question is the titular question: What are the archetypes of AI utilization? In order to map out answers to these questions, 12 industry experts were interviewed from four different companies. These companies included both AI vendors and AI end users and the informants came from a wide variety of backgrounds, from data science to business development.

The first archetype of AI utilization that is identified by this thesis is the Cost-Saving archetype. The utilizations that are classified under this archetype aim to reduce the costs of the organization by using AI, typically machine learning, technologies and thus create value for the organization. This value then can be passed on to the customer of the organization by the way of either lowered prices or higher R&D investments which may translate into improved products. The second archetype is the Customer Engagement archetype. Here, AI is integrated into the core product of the organization to produce a better customer experience and to drive sales and/or customer retention, making it an investment towards the core product. Lastly, the Auxiliary Benefits archetype relies on AI projects generating auxiliary benefits to the organization, such as ammunition for marketing or organizational learning for the future. These archetypes are depicted using a composite model built from the literature examined by this thesis and contain a large amount of strategic information that managers and organizations can exploit at will.