Artificial Intelligence and Innovation Management

The following sections are included:

• About the Editors
• About the Contributors
• Contents

The objective of this book is to contribute to the ongoing debate among innovation scholars and practitioners focusing on the potential impact of Artificial Intelligence (AI) on the ways companies and organizations do business, operate, and innovate. It considers AI as a source of innovation both in terms of innovation within the field of AI itself (AI innovation) and in terms of how it enables or disrupts innovation in other fields (AI-driven innovation). The thematic focus on the relationship between AI and Innovation Management (IM) emerged within the context of the activities of the Special Interest Group on AI and IM of the International Society for Professional Innovation Management (ISPIM), which we happen to represent…

This chapter is an honest conversation between an innovation manager and an AI technologist, exploring some of the misunderstandings between the two professions and pointing at the need for increased knowledge sharing between the two. In the chapter, different possible similarities, intersections, and benefits of collaboration between AI technologists and innovation managers are covered. As organizations move from early stages of AI maturity toward more advanced levels of integrated AI, the competencies of innovation managers become more important. Innovation managers can help both build the right mindset for wider adoption of AI in an organization and support other organizational aspects, such as strengthening cross-functional cooperation, agile self-organizing teams and innovative leadership, and development of a wider innovation ecosystem for enabling AI integration.

At more than 50 years of age, Artificial Intelligence cannot be called a new technology. However, recent advances in AI have made the field widely available and applicable to the extent that it has become a major driver for technology-based innovations. This chapter investigates the characteristics underlying AI technology that are most relevant for innovation management. It addresses challenges and potential showstoppers, such as data requirements, explainability, and ethical questions. These questions require a strategic approach to managing AI innovation, i.e., a long-term perspective and a careful consideration of the epistemic flow of information from the problem domain to the application. We propose an epistemic framework for the AI innovation process that helps in understanding and improving key dynamic capabilities for strategic AI innovation management.

This chapter focuses on the utilization of AI to identify and monitor innovation and technology trends to support decision making. There are high expectations from the advances in AI to provide better, more timely and more complete information and insights on innovation and technology trends to support decision making. There is, however, less discussion of what it takes to develop such AI-supported monitoring of innovation, and how such new ways are introduced and affect decision-making processes. We pay specific attention to the challenges of moving towards the adoption of such AI-supported innovation monitoring capabilities. We refer to these challenges as “AI traps.” Addressing such traps is important in the evaluation of the promise of AI for innovation trend spotting, as well as in preventing any disappointments with its potential outcomes. The insights developed in this chapter are therefore meant to help innovation managers realize the full potential of AI for innovation trend spotting, monitoring and, ultimately, decision-making.

This study uses topic modeling to analyze contents of 159 “expert talks” videos on artificial intelligence (AI), obtained from TEDx Talks channel on YouTube, in order to examine the feasibility of social media video analysis for entrepreneurial opportunity discovery, identify prominent niches and suggest potential entrepreneurial opportunities in AI. The study identifies six AI application areas based on the results from the analysis and interprets them using relevant scholarly literature. Further, the chapter provides insight into entrepreneurial opportunities within those application areas and concludes with lessons learned. The chapter contributes to the book’s theme by examining how AI technologies could help the early stages of the innovation management process.

The main purpose of this chapter is to discuss AI-driven innovations. Generally, the links between AI technology, AI usage, and organizational performance remain unclear. To improve the efficiency of artificial intelligence implementation, reduce costs and shape new business opportunities, firms should understand the features, capabilities, and the impact of these innovations on business models, business processes and employees. This area however is very little studied. To address this gap, an AI-driven value chain framework is proposed, which makes it possible to create a reference model (taxonomy) of AI-driven innovation processes and innovation types. Finally, an AI organization maturity model and an AI-driven maturity framework are discussed.

Artificial intelligence is changing the way we invent. Although it is not commonplace today, artificial intelligence is being used for product innovation, for example, in the design of physical objects and software. However, a far less explored area is applying artificial intelligence to process innovation. Developing new processes can be transformative, such as the invention of the moving assembly line. This chapter presents how the same artificial intelligence algorithms, specifically evolutionary computing, used for product innovation can be leveraged for process innovation. The design of self-organizing materials is used as an example to guide the reader through topology optimization (product innovation) to the creation of complex systems (process innovation). The principles from designing complex systems using artificial intelligence are abstracted — constraints, time, and noise — and discussed in the context of Industry 4.0.

Mobilizing a knowledge-based perspective of the firm, this chapter outlines how artificial intelligence (AI) technologies can be seen as new innovation capabilities allowing firms to learn from existing and new knowledge assets. This approach allows managers and scholars to adopt a strategic perspective regarding the development of their AI assets and capabilities. We identify in particular four types of organizational learning strategies, considering whether to build analytic capabilities (both technical and organizational) on the one hand to best exploit the knowledge embedded in their existing data assets and on the other hand to explore — identify and capture — new sources of knowledge.

This chapter explores how Artificial Intelligence (AI) will potentially disrupt how Innovation Management (IM) research will be conducted. In the recent decades, the research process has remained virtually unchanged, i.e., a structured, stepwise process. Recently, AI has shown new opportunities that affect this process. AI applications have been developed supporting researchers for specific tasks to ease their work, for example, database searching, transcribing and data management. For example, an autonomous literature review (i.e., database search, selection, analyzing, concluding, and writing) has been conducted by AI, vastly exceeding what human research could achieve in the same time frame, which paves the way for interesting reflections on how AI applications affect research from here on going forward. As we have explored AI techniques and AI applications and discussed their potential for conducting research, three themes emerged regarding how future research might be conducted: simplification thorough the use of computational power, augmentation of a researcher’s capabilities, and replacement by automation: Three critical questions are identified: (1) The future use of AI applications in research; (2) Exploring the trust in AI — The black box dilemma, and; (3) Benefits and limitations of AI applications. These topics are discussed, paving a way towards a research agenda.

A newly developed value proposition is the best expression of a company’s innovative capacity — its ability to coordinate the combination of resources to develop new products and services, and shape valuable market offers to address specific customer needs. How a new company can use artificial intelligence to innovate by enhancing its value propositions for customers, investors and other stakeholders is not well known. To address this question, we systematically reviewed the value proposition development, business ecosystems, and the artificial intelligence business value literature streams and used the results to develop 182 assertions — statements about what a new company should do to scale its value early and rapidly. We then applied topic modeling to examine the assertions and produce a framework for value proposition development in the context of a new company that wishes to scale its value early and rapidly. Finally, we made explicit the link between a new company’s resources and capabilities in artificial intelligence and the seven elements of the framework. The results suggest that resources and capabilities in artificial intelligence can be used to enhance stakeholders’ value propositions, improve foreign market entry performance, increase customer base, and continuously improve individuals, operations, and infrastructures of new companies. The chapter provides a guide of the business value that AI can offer a new company committed to scale early and rapidly.

This chapter focuses on the role of management in the process of AI-driven innovation, suggesting that the heart of an AI-enabled organization is the building of a dynamic relationship between leaders and workforce — one based on trust, empathy and participatory relationships. Transformation to digitally and AI-enabled organizations necessitates this dynamic approach. Based on qualitative and quantitative work, we will suggest that today we must become active learners, while leaders need to create inclusive cultures that support and invest in this progressive environment. New relationships between leaders and employees rest on responsiveness, resilience, empathy and an openness to change. This is particularly relevant in overcoming crises, for example, building the type of post-COVID-19 world that heeds its lessons and creates cultures of inclusion and success.

The Digital Transformation is a human transformation, not just a new industrial revolution, allowing potential access to all human knowledge. Social transformations appear at a pace never witnessed before, with challenges that can no longer be tackled by one organization alone. This makes the flourishing of paradigms of open innovation possible, where the integration of multiple stakeholders around the citizen-centric perspective is a key element. In this context, the impacts that AI will bring to our societies are still to be defined, or more precisely, there is a pending action of unveiling the social impact of AI, and this task has to be done prior to the development of new models in which AI-based automation and automatic decision making will play a fundamental role. Particularly, as an inherent part of the innovation management framework, this will affect strategy, leadership, institutional culture, processes and more — widely beyond the software design tasks. Dimensions such as capacity building, identity(ies), responsibility and creativity are now to be reshaped due to AI. The Living Lab approach appears as a powerful instrument for this mission, and in this chapter we will explore clear examples on how to tackle the unveiling of the impacts on such dimensions.

Artificial Intelligence (AI) technologies have an immense potential to transform the way governments work, and the way they innovate, at the local, national and international level. However, in spite of the efforts both from governments and from supranational organizations, uptake of this key general-purpose technology has its limitations. This chapter lays out the advantages of using Innovation Procurement to foster the development and deployment of AI technologies for the provision of public services, while also addressing potential bottlenecks. Overarching socio-ethical challenges are discussed, mainly around privacy and accountability, based on real-world procurements. The use-cases provide examples and inspiration from the Innovation Management perspective, as Innovation Procurement can support the public sector in spurring and scaling collaborative, inclusive and human-centric innovation in AI. In accordance with the practical perspective of this chapter, in the end, the authors provide a series of recommendations that could help in avoiding common pitfalls in Innovation Procurement of AI.

The following section is included:

• Index