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This article offers insight on how to effectively help incumbent organizations prepare for global business shifts to open source and digital business models.

Discussion related to observation, experience and case studies related to incumbent organizations and their efforts to adopt open source models and business tools.

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.

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.

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.

This article provides insights that will lead to more effective ideas for helping organizations adapt.

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.

This paper presents NASA’s experience using a Center of Excellence (CoE) to scale and sustain an open innovation program as an effective problem-solving tool and includes strategic management recommendations for other organizations based on lessons learned.

This paper defines four phases of implementing an open innovation program: Learn, Pilot, Scale and Sustain. It provides guidance on the time required for each phase and recommendations for how to utilize a CoE to succeed. Recommendations are based upon the experience of NASA’s Human Health and Performance Directorate, and experience at the Laboratory for Innovation Science at Harvard running hundreds of challenges with research and development organizations.

Lessons learned include the importance of grounding innovation initiatives in the business strategy, assessing the portfolio of work to select problems most amenable to solving via crowdsourcing methodology, framing problems that external parties can solve, thinking strategically about early wins, selecting the right platforms, developing criteria for evaluation, and advancing a culture of innovation. Establishing a CoE provides an effective infrastructure to address both technical and cultural issues.

The NASA experience spanned more than seven years from initial learnings about open innovation concepts to the successful scaling and sustaining of an open innovation program; this paper provides recommendations on how to decrease this timeline to three years.

This interview introduces the concept of the “AI first company”, a new breed of organization that is transforming the economy and defining the emergence of a new age.

An interview with Harvard digital strategy experts, Marco Iansiti and Karim Lakhani, about their new book, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Marco Iansiti is the David Sarnoff Professor of Business Administration at Harvard Business School (HBS) and heads the school’s Technology and Operations Management Unit and the Digital Initiative. Karim R. Lakhani is the Charles E. Wilson Professor of Business Administration at HBS, and the founder and co-director of the Laboratory for Innovation Science and co-founder of the Digital Initiative at Harvard. 10; 10;The authors were interviewed by Brian Leavy, emeritus professor of strategy at Dublin City University Business School and a Strategy & Leadership contributing editor.

Digital transformation is not about creating a digital function or spinning off a digital unit, it’s about fundamentally restructuring the core of the firm by building a data-centric operating architecture supported by an agile organization that enables on-going change.

Essentially, we are moving away from an era of core competencies that differ from industry to industry to an age shaped by data and analytics and powered by algorithms. The concept of network is superceding the concept of industry in defining strategic options for firms.

The emergence of the “Age of AI” has possibly created the greatest entrepreneurial opportunity in the history of civilization and the world is literally jammed with entrepreneurial activity. Marco Iansiti and Karim Lakhani suggest ways to maximize that opportunity.

The concept of a community form is drawn upon in many subfields of organizational theory. Although there is not much convergence on a level of analysis, there is convergence on a mode of action that is increasingly relevant to a knowledge-based economy marked by porous and shifting organizational boundaries. We argue that communities play an underappreciated role in organizational theory – critical not only to occupational identity, knowledge transfer, sense-making, social support, innovation, problem-solving, and collective action but also, enabled by information technology, increasingly providing socioeconomic value – in areas once inhabited by organizations alone. Hence, we posit that organizations may be in the shadow of communities. Rather than push for a common definition, we link communities to an organization's evolution: its birth, growth, and death. We show that communities represent both opportunities and threats to organizations and conclude with a research agenda that more fully accounts for the potential of community forms to be a creator (and a possible destroyer) of value for organizations.

Crowdsourcing – a form of collaboration across organizational boundaries – provides access to knowledge beyond an organization’s local knowledge base. Integrating work on organization theory and innovation, the authors first develop a framework that characterizes crowdsourcing into a main sequential process, through which organizations (1) define the task they wish to have completed; (2) broadcast to a pool of potential contributors; (3) attract a crowd of contributors; and (4) select among the inputs they receive. For each of these phases, the authors identify the key decisions organizations make, provide a basic explanation for each decision, discuss the trade-offs organizations face when choosing among decision alternatives, and explore how organizations may resolve these trade-offs. Using this decision-centric approach, the authors continue by showing that there are fundamental interdependencies in the process that makes the coordination of crowdsourcing challenging.

Rather than organize as traditional firms, many of today’s companies organize as platforms that sit at the nexus of multiple exchange and production relationships. This chapter considers a most basic question of organization in platform contexts: the choice of boundaries. Herein, I investigate how classical economic theories of firm boundaries apply to platform-based organization and empirically study how executives made boundary choices in response to changing market and technical challenges in the early mobile computing industry (the predecessor to today’s smartphones). Rather than a strict or unavoidable tradeoff between “openness-versus-control,” most successful platform owners chose their boundaries in a way to simultaneously open-up to outside developers while maintaining coordination across the entire system.

The purpose of this paper is to review the latest management developments across the globe and pinpoint practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

In the early eighteenth century, an English carpenter and clock maker, John Harrison, was awarded a phenomenal sum of money – £15,000 – when he made a great scientific and technological breakthrough. Harrison invented the marine chronometer which determined ships' longitude at sea. The Longitude Prize was established after the likes of Edmond Halley and Isaac Newton had failed to come up with a solution. Recalling the story in the Harvard Business Review, Kevin J. Boudreau and Karim R. Lakhari make the point that this is really an early example of what we now call crowdsourcing – getting ideas or creating new products and services by enlisting the help of a large body of people.

The paper provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.

Alibaba strategist Ming Zeng explains how the company operates its “smart business” in the era of business ecosystems

Alibaba today presents a vivid picture of the new business world emerging, one that operates using the data generated by the network of participants, as processed by machine learning, to automatically respond to customer behavior and preferences in real time.

At Alibaba link after link in the value chain is being modularized and reconfigured into technologically optimized networks and much of business decision-making is powered by algorithms.

Creativity becomes the crucial productive factor and thus the purpose of an organization is to enable, rather than manage creative people.

<abstract_text>In the emerging world of “smart business,” entrepreneurs and strategists must learn to assess the entire network as a whole when deciding how to position their business and create value. Then the strategy making process is one of generating, coordinating and modulating experimentation