Artificial intelligence (AI) is expected to fundamentally alter how manufacturing firms create, deliver, and capture value. However, many manufacturers are having difficulty successfully integrating AI capabilities into their business models and operations at scale.
We must shift our value propositions toward customer process optimization. To enable this, we are increasing our investments in machine learning, AI, and deep learning capabilities, and we are well ahead of the curve in integrating these into our operations. The final step is to scale the business models.
AI-Driven Business Model Innovation
It is necessary to comprehend how industrial manufacturers can transform and innovate business models by incorporating AI capabilities into their core business processes. A business model is defined as a firm’s “design or architecture of value creation, delivery, and capture mechanisms.” Although some preliminary suggestions exist, there has been little attention paid to the development of theory on the topic of AI and business model innovation in digital servitization.
As a result, our research examines how manufacturing firms integrate AI into their value-creation, value-delivery, and value-capture mechanisms. For example, profiting from a digital capability such as AI “is not so much a technological challenge as it is a challenge to harness knowledge to create the organizational knowledge to continually optimize the value that can be derived from digital technologies.”
The creation of offerings and value propositions for customers is referred to as value creation. This would refer to AI-enabled services designed to optimize the use and maintenance of products (or fleets of products) in customer operations in the context of digital servitization. The value to customers as researchers is an important consideration. For example, emphasize that any value offers based on digital technologies must be (co-)created in an agile and customizable manner based on customer needs.
As a result, there is a need to evaluate AI applications systematically and their potential value to customers and end-users. Indeed, the use of AI may allow providers to create value closer to the customer’s operations, as providers can use data from a fleet of equipment to identify areas for improvement in the customer’s ongoing operational processes, such as equipment optimization and condition-based maintenance.
The establishment of operational processes and activities to deliver the value promised is referred to as value delivery. It could refer to using AI capabilities to improve the work processes of front-line and back-line service staff and technological support systems for manufacturers engaged in digital servitization. AI capabilities, for example, can be helpful in monitoring product flows, process flows, and maintenance processes. However, because full-scale AI implementation is still uncommon among industrial manufacturers, understanding the principles required to leverage AI within the core processes of the business model is essential.
For example, Iansiti and Lakhani (2020) emphasize the importance of transforming operating models to expand the scale, scope, and learning opportunities AI provides within the organization. Nonetheless, many businesses fail to consider the value delivery dimension fully. To adapt to emerging opportunities revealed by AI capabilities, the organization must work as a unit, collaborating with other companies and suppliers.
Cost structures, potential revenue streams, revenue models, and financial liabilities are examples of value capture elements. In the case of digital servitization, AI implementation may provide significant new revenue sources from AI-enabled services, but there is also the potential for high maintenance costs.
The advancement of AI capabilities has the potential to drive business model innovation, new revenue streams, and increased competitiveness for manufacturers in digital servitization. Despite this, many challenges and uncertainties confront this transformation, with only a few research insights in place to guide the way forward. As a result, this research investigates how manufacturing firms can develop AI capabilities and business model innovation to scale AI in digital servitization. The methods, findings, and contributions of this research are described in the following sections