Artificial Intelligence and Innovation Management contributes 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 book's content is driven by several important conclusions: •AI has a great potential for innovation, but this potential is associated with significant challenges. •There are different views on how to implement AI in innovation management but little agreement and guidance on how to do it. •AI not only has the potential to produce radically new innovations, but also to rethink innovation management in general. •Deeper knowledge about the innovative impact of AI technologies would be highly relevant to both practitioners and academics within the field of Innovation Management (IM). It is therefore both necessary and timely to explore the different aspects of the relationship between AI and IM. The contributors to this book include both scholars and practitioners from multiple countries and different types of institutions. They were selected based on their ability to provide a relevant distinctive perspective on the relationship between AI and IM; the degree of their professional engagement with the field; their ability to contribute to the thematic and contextual diversity of the contributions; and their ability to provide actionable insights for both innovation scholars and practitioners. Helena Blackbright (Mälardalen University, Sweden) and Stoyan Tanev (Carleton University, Canada) are chairing the Special Interest Group on AI and IM at the International Society for Professional Innovation Management (https://www.ispim-innovation.com/).
Author(s): Stoyan Tanev, Helena Blackbright
Series: Series on Technology Management, 38
Publisher: World Scientific Publishing
Year: 2022
Language: English
Pages: 327
City: London
Contents
About the Editors
About the Contributors
Introduction
Chapter 1 What AI Can Do for Innovation Managers and Innovation Managers for AI
1. Introduction
2. An Honest Introduction — Could Innovation Management and AI Be Friends?
3. Building Bridges, Increasing Diversity, and Crafting a More Holistic Approach
4. The Roles of AI Change Agent and Innovation Manager
5. Mindset Growth and Building a Learning Organization
6. Machine Learning Lifecycle vs. Iterative Innovation Process
7. From Continuous Improvement and Exploitation Toward Exploration and Innovation: Bolt-on vs. Integrated AI
8. Ecosystem, System of Systems
9. Wrapping up
Acknowledgments
References
Chapter 2 A Knowledge-Based Perspective of Strategic AI Innovation Management
1. Introduction
1.1. Characterizing AI systems
1.2. Understanding the surge in applications
2. Strategic Challenges of AI for Innovation Management
2.1. Previous work on strategic AI management for innovation
2.2. Barriers and challenges of AI innovation
3. The AI System Building Process: An Epistemic Framework
3.1. An epistemic process model from problem to AI-based innovation outcomes
3.2. Specific AI modeling challenges for strategic innovation management
3.2.1. Understanding the modeling process
3.2.2. Managing data
3.2.3. Ethical issues
3.2.4. Competence-enhancing versus competence-destroying AI-based innovation
4. Strategic Resources Supporting Dynamic Capabilities for Successful AI Innovation
4.1. Knowledge and human resources
4.2. Data curation capabilities
4.3. Process and expectation management
4.4. Regulation
5. Conclusion
References
Chapter 3 Addressing AI Traps: Realizing the Potential of AI for Innovation Trend Spotting, Monitoring and Decision-Making
1. Introduction
2. The Potential of AI for Innovation Trend Spotting, Monitoring and Decision-Making
2.1. The potential of AI
2.2. The potential of AI for innovation trend spotting, monitoring and decision making
3. Realizing the AI Innovation Potential and Addressing the AI Traps
4. Conclusion
References
Chapter 4 Social Media Video Analysis for Entrepreneurial Opportunity Discovery in Artificial Intelligence
1. Introduction
2. Method
3. Results
3.1. Saving biodiversity
3.2. Automation
3.3. Diplomacy
3.4. Quantum computing
3.5. Healthcare
3.6. Societal impacts
3.7. Insight into entrepreneurial opportunities
3.7.1. Environmental applications
3.7.2. Industrial applications
3.7.3. Analytical applications
3.7.4. Quantum computing applications
3.7.5. Medical applications
3.7.6. Educational applications
4. Conclusion
4.1. Social media video analysis aids opportunity discovery
4.2. AI presents numerous entrepreneurial opportunities
References
Chapter 5 AI-Driven Innovation: Towards a Conceptual Framework
1. Introduction
2. What Is AI?
2.1. Definitions
2.2. Ways of AI usage
3. Research Design and Approach
4. AI-Driven Multidimensional Value Chain
5. AI-Driven Innovation Taxonomy
6. AI-Driven Organizational Innovation and Maturity
7. Conclusion
References
Chapter 6 Automating Innovation
1. Introduction
2. Background
2.1. Evolutionary computing
2.2. Self-Assembly
2.3. Topology optimization
3. Creating Creativity
3.1. Staged self-assembly
3.2. Evolving staged self-assembling systems
3.3. Thought experiment
4. Conclusion
References
Chapter 7 Artificial Intelligence as a Strategic Innovation Capability
1. Introduction
2. Theoretical Background
2.1. Innovation management, knowledge and data
2.2. Knowledge as a strategic resource for innovation
3. AI Fostering Organizational Learning
3.1. Exploitation: AI as a new way to learn from available data
3.1.1. Exploration: AI as a new way to learn from (new) data
4. Managing AI as a Strategic Innovation Capability
5. Conclusions, Perspectives and Limitations
References
Chapter 8 Disrupting the Research Process through Artificial Intelligence: Towards a Research Agenda
1. Introduction
2. Approaches in Innovation Management Research
2.1. Research ethics
3. Introduction to AI and Applications
3.1. AI technologies
3.1.1. Search and optimization
3.1.2. Logic
3.1.3. Probabilistic methods for uncertain reasoning
3.1.4. Classifiers and statistical learning methods
3.1.5. Neural networks
3.2. Integrating AI in the research process
4. Towards a Research Agenda for the Future of AI in IM Research
4.1. The future use of AI applications in research
4.1.1. Simplification through the use of computational power
4.1.2. Augmentation of a researcher’s capabilities
4.1.3. Replacement by automation
4.2. Exploring the trust in AI — The black box dilemma
4.3. Benefits and limitations of AI applications: The pedagogical dilemma
5. Contribution
5.1. Practical implications
5.2. Potential directions for future research
References
Chapter 9 The Potential of AI to Enhance the Value Propositions of New Companies Committed to Scale Early and Rapidly
1. Introduction
2. Literature Review
2.1. Value propositions in the context of new scaling companies
2.2. The business value of AI resources and capabilities
2.3. Ecosystem perspective on VP development
2.4. What is known and what is not known
3. Research Methodology
4. Topic Modeling of the Actionable Value Proposition Insights
5. Shaping a Value Proposition Framework
5.1. Three VP elements related to customer base growth
5.2. VP development is a continuously improved process
6. Can AI Enhance the VPs of Companies Willing to Scale Early and Rapidly?
7. Conclusion
References
Appendix A. Topic Modeling Results
Chapter 10 Fair, Inclusive, and Anticipatory Leadership for AI Adoption and Innovation
1. Introduction
2. Leadership for Conventional Technological Innovation
3. Leadership for AI Innovation
4. Case Study
5. Conclusion
References
Chapter 11 Unveiling the Social Impact of AI through Living Labs
1. The Digital Transformation as a Human Transformation
1.1. Potential access to human knowledge through the internet
1.2. Community development through knowledge and data access: A new open paradigm
1.3. Automation, automatic decision making and data
2. Regulation and Observation: The Missing Action
2.1. The approach to responsible and trustworthy AI
2.2. The observatories for ethics in AI: What to observe
3. Open Innovation and Living Labs to Drive the Social Impact of AI
3.1. Living Labs and the quadruple helix model
3.2. Main features of Living Labs
3.3. Systemic transformation and AI
4. Some Relevant Dimensions of the Social Impact of AI
4.1. Labor: Capacity building as building block
4.2. Responsibility: Addressing an acceptable perspective to innovation in AI
4.3. Creativity: Reloading arts, IP and business models
4.4. Identity: Exploring our digital dimensions
5. Conclusions
6. What’s Next?
References
Chapter 12 Innovation Management and Public Procurement of AI
1. The Potential of AI for Governments
1.1. The world is resetting
1.2. AI for the improvement governments’ performance
1.3. Limitations of AI from the government’s user perspective
2. Overarching Challenges
2.1. Privacy and transparency
2.2. Ethical concerns
3. Innovation Procurement, an Instrument for AI Innovation
3.1. Strategic procurement
3.2. Innovation procurement for AI and IM
4. Common Pitfalls in Innovation Procurement of AI
Acknowledgment
References
Index
