Abstract
Marco Iansiti, the David Sarnoff Professor of Business Administration at Harvard Business School, and co-founder of Keystone Strategy, a strategy and economics consulting firm, and Karim R. Lakhani, the Charles E. Wilson Professor of Business Administration and the Dorothy and Michael Hintze Fellow at Harvard Business School, offer a compelling account of how artificial intelligence (AI) is changing the existing (and future) structure across industries and global business organizations. In their path-breaking book, Iansiti and Lakhani explain how data, analytics, machine learning, and AI will be the drivers of how firms will be reinventing themselves, eliminating many of the constraints of organizational “silos” that have limited business growth in the past. This 10-chapter book, however, is not written for the technologist but for the corporate executives leading firms.
The focus of this book is creating a framework around reimagining business and operating models. Specifically, the authors explain how this amalgam of data, analytics, and AI digital technology is transforming value, capture, and delivery in both the firm business model and operating model. In the business model, value creation is exceptional, with increasing consumer value created through personalization and engagement, while value capture and sharing is a product of the number of users, user engagement, and monetization across many multisided markets. The operating model is composed of scale (involving zero marginal cost, network effects), scope (aggregation and modularity across markets), and learning effects (constant innovation and AI/machine learning driven improvements). The authors believe that by digitizing the most critical processes in operating models, it removes traditional operational “bottlenecks” and enables higher levels of scalability, scope, and learning for the firm.
AI-powered firms require new business architecture, characterized as an “AI Factory,” which is built upon the data, software, and connectivity that resides within a secure, robust, and scalable computational infrastructure. Furthermore, the operating architecture for an AI-powered firm consists of a hierarchy, with a base of data inputs, followed by AI/machine learning technology component libraries, at the next level by application programming interfaces, and with the top level occupied by agile teams utilizing the AI Factory. This data and technology platform is easily and rapidly deployable to create or connect to new digital agents in applications that can address a variety of use cases. The process used to develop these applications is driven by small, agile teams equipped with data science, engineering, and product management capabilities; such agile processes and digital operating architecture go hand-in-hand. Also, such digital operating models are characterized by continually improving their performance through learning, as well as by promoting modularity and reuse of the software and algorithms developed to perform various operating tasks.
Iansiti and Lakhani identify a new strategic problem in the digital age of industry analysis: network analysis. Network analysis involves understanding the open and distributed connections and networks across firms, and over time, these firms accumulate the value creation dynamics of both network and learning effects, as well as the benefits of clusters. Yet, the enemies of value capture dynamics in digital network-based profitability scenario include multihoming, that is, when users or service providers in a network form ties with multiple platforms, whereby the first network’s hub’s ability to capturing value is challenged, and disintermediation, wherein nodes, that is, other firms or customers, in a network can easily bypass the firm to connect directly. In contrast, network bridging, involving making new connections across previously separate economic networks, makes use of more favorable competitive dynamics and a different willingness to pay. These various elements in network analysis open new strategic options for managers and can transform the way a firm creates and captures value.
The authors also explain the broader competitive implications of what happens when a firm featuring a digital operating model encounters and collides with a more traditional firms. A strategic collision occurs when a firm with a digital operating model targets an application (or use case) that has traditionally been served by a conventional firm. This is significant because digital operating models are characterized by different scale, scope, and learning dynamics from those of traditional firms, and such “collisions” can completely transform industries and reshape the nature of competitive advantage. Examples of such “collisions” occurring include Airbnb and Booking challenging traditional hotel chains such as Marriott and Hilton, Amazon competing with Walmart and other traditional retailers, and Apple Music, Spotify, and other digital entertainment providers challenging traditional music distribution companies.
Iansiti and Lakhani do not ignore the daunting ethical issues that accompany the widespread adoption of digital operating models. These include problems with digital amplification (“serving content that reinforces biases”), algorithmic bias (including both selection and labeling bias), cybersecurity (including brute force, web hacking, and DDoS attacks), platform control (Cambridge Analytica violating the Facebook terms of use), and fairness and equity (anticompetitive behavior issues). As a partial solution, the authors argue for their keystone strategy proposal, which aligns the health of a business ecosystem with those of its network. Moreover, the leadership challenge for companies will involve transformation (embracing the new digital operating model in traditional industries and building a deep foundation of safety, security, and sustainability in digital organizations), entrepreneurship (in applications of blockchain technology), regulation (including privacy and competition policy), community (encouraging open-source approaches), and collective wisdom (philosophically, counting on the collective health of the firm’s business ecosystem).
One criticism of the authors’ approach is a lack of a clear definition between “business strategy” and “operations strategy.” A business strategy encompasses the business model, but also competition and sociopolitical factors. Likewise, an operations strategy (and model) is subsumed under the business strategy. Overall, this book delivers on what it hoped to address: providing an accessible explanation for general managers of how data, analytics, and AI transforms both the operating and business models of traditional industries. Additionally, it offers a comprehensive overview of the ethical and competitive issues that tech-oriented companies (and their leadership) are now facing. These ethical challenges—and not instituting new business architecture—will remain the most daunting strategic challenges facing executive management in the future.