Competing in the Age of AI

Marco   Iansiti; Karim R. Lakhani

Book | Forthcoming

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World

Abstract

In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value.
Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have constrained business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, drive massive scope increase, enabling companies to straddle industry boundaries, and enable powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions.
When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. The authors present a framework for rethinking business and operating models; explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition and altering the structure of our economy; show how these collisions force traditional companies to change their operating models to drive scale, scope, and learning; explain the risks involved in operating model transformation and how to overcome them; and describe the new challenges and responsibilities for the leaders of these firms.
Packed with examples—including the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is the essential guide for rethinking how your firm competes and operates in the era of AI.

Keywords: artificial intelligence; Technological Innovation; Change; Competition; Strategy; Leadership; Business Processes; Organizational Change and Adaptation;

Format: Print

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Citation:

Iansiti, Marco, and Karim R. Lakhani. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Boston: Harvard Business Review Press, in press.

Indigo Agriculture used a digital-enabled research and development (R&D) process to launch its initial product, microbial coatings for agricultural seeds, which increase crop yields while reducing the need for fertilizers. In doing so, the company developed direct relationships with farmers, in contrast to typical agricultural supply chains that use intermediaries. The company then launched a marketplace platform to link growers directly to crop buyers, again disintermediating the market. Indigo Agriculture is now considering launching an initiative to incentivize farmers to engage in regenerative agricultural practices by setting up a carbon market that could pay them for sequestering carbon into their soils. If scaled globally, the idea could sequester as much as one trillion tons (a teraton) of carbon dioxide from the earth’s atmosphere into agricultural soils, but pursing the idea has many risks.

Article | Strategy & Leadership | 2019

Overcoming Cultural Resistance to Open Source Innovation

John Winsor, Jin Hyun Paik, Michael Tushman and Karim R. Lakhani

Purpose: This article offers insight on how to effectively help incumbent organizations prepare for global business shifts to open source and digital business models.
Design/methodology/approach: Discussion related to observation, experience and case studies related to incumbent organizations and their efforts to adopt open source models and business tools.
Findings: Companies that let their old culture reject the new risk becoming obsolete if doing so inhibits their rethinking of their future using powerful tools like crowdsourcing, blockchain, customer experience-based connections, integrating workflows with artificial intelligence (AI), automated technologies and digital business platforms. These new ways of working affect how and where work is done, access to information, an organization’s capacity for work and its efficiency. As important as technological proficiency is, managing the cultural shift required to embrace transformative industry architecture – the key to innovating new business models -- may be the bigger challenge.
Research limitations/implications: Findings are based on original research and case studies. Insights are theoretically, based on additional study, interviews, and research, but need to be tested through additional case studies.
Practical implications: The goal is to make the transition more productive and less traumatic for incumbent firms by providing a language and tested methods to help senior leaders use innovative technologies to build on their core even as they explore new business models.
Social implications: This article provides insights that will lead to more effective ideas for helping organizations adapt.
Originality/value: This article is based on original research and case experience. That research and experience has then been analyzed and viewed through the lens of models that have been known to work. The result is original insights and findings that can be applied in new ways to further adoption within incumbent organizations.

Keywords: open source innovation; Organizational Culture; Organizational Change and Adaptation; Business Model; Technological Innovation; Collaborative Innovation and Invention;

Citation:

Winsor, John, Jin Hyun Paik, Michael Tushman, and Karim R. Lakhani. "Overcoming Cultural Resistance to Open Source Innovation." Strategy & Leadership 47, no. 6 (2019): 28–33. View Details

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This research considers the implications of knowledge sharing on knowledge production. We theorize that the “cognitive similarity” between two individuals’ prior knowledge is systematically related to the effectiveness of knowledge sharing on the knowledge production process. To estimate relationships, we designed and executed a natural field experiment at a medical symposium, in which exogenous variation was introduced to provide some of the 15,817 scientist pairs with opportunities for serendipitous, face-to-face encounters. Our data include direct observations of interaction patterns collected using sociometric badges, and detailed longitudinal data on the scientists’ publication records for six years following the symposium. We find both collaborative and competitive effects of cognitive similarity on knowledge production. Interacting scientists were more likely to transfer new knowledge concepts to each other if they shared some similarities in research interests, but were less likely to cite and diffuse each other’s knowledge if they worked in the same field or discipline of study. This suggests that knowledge sharing may boost organizational knowledge production when potential partners share some prior knowledge overlap, but too much overlap may impede it.

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Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World

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