Karim R. Lakhani

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. Iansiti and Lakhani:

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; 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.

The last two decades have witnessed an extraordinary growth of new models of managing and organizing the innovation process that emphasizes users over producers. Large parts of the knowledge economy now routinely rely on users, communities, and open innovation approaches to solve important technological and organizational problems. This view of innovation, pioneered by the economist Eric von Hippel, counters the dominant paradigm, which cast the profit-seeking incentives of firms as the main driver of technical change. In a series of influential writings, von Hippel and colleagues found empirical evidence that flatly contradicted the producer-centered model of innovation. Since then, the study of user-driven innovation has continued and expanded, with further empirical exploration of a distributed model of innovation that includes communities and platforms in a variety of contexts and with the development of theory to explain the economic underpinnings of this still emerging paradigm. This volume provides a comprehensive and multidisciplinary view of the field of user and open innovation, reflecting advances in the field over the last several decades.

The contributors -- including many colleagues of Eric von Hippel -- offer both theoretical and empirical perspectives from such diverse fields as economics, the history of science and technology, law, management, and policy. The empirical contexts for their studies range from household goods to financial services. After discussing the fundamentals of user innovation, the contributors cover communities and innovation; legal aspects of user and community innovation; new roles for user innovators; user interactions with firms; and user innovation in practice, describing experiments, toolkits, and crowdsourcing, and crowdfunding.

When Google bought Nest, a maker of digital thermostats, for $3.2 billion just a few months ago, it was a clear indication that digital transformation and connection are spreading across even the most traditional industrial segments and creating a staggering array of business opportunities and threats.

The digitization of tasks and processes has become essential to competition. General Electric, for example, was at risk of losing many of its top customers to nontraditional competitors—IBM and SAP on the one hand, big data start-ups on the other—offering data-intensive, analytics-based services that could connect to any industrial device. So GE launched a multibillion-dollar initiative focused on what it calls the industrial internet: adding digital sensors to its machines; connecting them to a common, cloud-based software platform; investing in software development capabilities; building advanced analytics capabilities; and embracing crowd-based product development. With all this, GE is evolving its business model. Now, for example, revenue from its jet engines is tied to reduced downtime and miles flown over the course of a year. After just three years, GE is generating more than $1.5 billion in incremental income with digitally enabled, outcomes-based business models. The company expects that number to double in 2014 and again in 2015.

From Apple to Merck to Wikipedia, more and more organizations are turning to crowds for help in solving their most vexing innovation and research questions, but managers remain understandably cautious. It seems risky and even unnatural to push problems out to vast groups of strangers distributed around the world, particularly for companies built on a history of internal innovation. How can intellectual property be protected? How can a crowdsourced solution be integrated into corporate operations? What about the costs?

These concerns are all reasonable, the authors write, but excluding crowdsourcing from the corporate innovation tool kit means losing an opportunity. After a decade of study, they have identified when crowds tend to outperform internal organizations (or not). They outline four ways to tap into crowd-powered problem solving—contests, collaborative communities, complementors, and labor markets—and offer a system for picking the best one in a given situation. Contests, for example, are suited to highly challenging technical, analytical, and scientific problems; design problems; and creative or aesthetic projects. They are akin to running a series of independent experiments that generate multiple solutions—and if those solutions cluster at some extreme, a company can gain insight into where a problem’s “technical frontier” lies. (Internal R&D may generate far less information.)

Legislation in Congress could make it easier for everyday people to become investors in small companies. Harvard Business School’s Karim Lakhani and entrepreneur Slava Menn tell Kara why “crowdfunding” is all the rage.

For the forum's topic of collective intelligence, Karim Lakhani participated as a speaker in "a conversation about the theory and practice of collective intelligence, with emphasis on Wikipedia, other instances of aggregated intellectual work and on recent innovative applications in business."  A podcast and a webcast are available through the forum's website.

What is the status of the Free and Open Source Software (F/OSS) revolution? Has the creation of software that can be freely used, modified, and redistributed transformed industry and society, as some predicted, or is this transformation still a work in progress? Perspectives on Free and Open Source Software brings together leading analysts and researchers to address this question, examining specific aspects of F/OSS in a way that is both scientifically rigorous and highly relevant to real-life managerial and technical concerns.