Strategy and Leadership When Algorithms and Networks Run the World
Marco Iansiti & Karim R. Lakhani. Harvard Business Review Press, $32 (288p) ISBN 978-1-633-69762-1
In this timely analysis of the next phase of the digital revolution, Harvard Business School professors Marco Iansiti and Karim Lakhani examine the implications of broad adoption of artificial intelligence and machine learning across industries.
Competing in the Age of AI focuses on the impact of this rapidly expanding technology on the business world and offers a framework for how to survive and thrive in this new landscape.
The book is written in plain English – not weighted down by technical jargon and overly complicated explanations of back propagation or convolutional neural networks. It is instead written for managers who will be charged with leading the next wave in the digital revolution where successful firms are fueled by data, analytics, and artificial intelligence. These three cornerstones of an AI-first company, the authors contend, allow businesses to break through previous limitations of scale, scope and learning to drive vastly superior business performance.
In classic HBS fashion, the authors’ points are bolstered by a series of mini case studies featuring practical examples from Ant Financial, Microsoft, Amazon, Google, Airbnb, and Ocado. They address the opportunities and risks faced by these firms, using them to build a roadmap for navigating new challenges and responsibilities for leaders of both digital and traditional companies. As such, the book is an excellent primer for managers and champions for change in companies of all sizes.
The implications covered in Competing in the Age of AI range from industry at large to individual firms and leaders within the firm.
Lakhani and Iansiti use numerous examples from today’s leading companies to show that we’ve entered a new age in which networks and algorithms are woven into the fabric of firms; and that these changes are impacting how industries function and how the new economy operates.
“AI is becoming the universal engine of execution. As digital technology increasingly shapes ‘all of what we do’ and enables a rapidly growing number of tasks and processes, AI is becoming the new operational foundation of the business – the core of a company’s operating model, defining how the company drives the execution of tasks.”
While many firms still see AI as a supporting tool used to help process and analyze troves of data and facilitate decision making, AI-first firms are using this new technology to change how their companies operate by embracing a new operating architecture — one that redefines how they create, capture, share, and deliver value.
This requires a new way for managers to think about their business strategy. “It used to be that strategy expressed itself in the way a firm managed internal resources. Now strategy is shifting to the art of managing the firm’s networks and leveraging the data that flows through them.”
Lakhani and Iansiti assert that it is imperative for companies to “leverage data to learn about users’ needs and respond with digital services to address them.” This, they say, is the new template for the 21st century firm.
The traditional value of a firm comes from coordinating workers to create complex products and services. But the traditional organization’s ability to deliver value faces a curve of diminishing returns as production is limited by capital constraints. This has been the case for hundreds of years in business, but for digital firms the horizon has shifted.
This is because the digital firm is transforming the critical path in the delivery of value.
As firms seek to deliver value and optimize scale, scope, and learning, Iansiti and Lakhani argue that competing in today’s business climate requires deployment of a fundamentally new kind of operating model — one that enables the firm to reach new levels of scalability, achieving a vastly broader scope, and learning and adapting at a much faster rate than a traditional firm.
The core of this new operating model is a “sophisticated, integrated data platform” that amalgamates data sources on existing platforms to explore new opportunities.
In a digital operating model, the employees do not deliver the product or service; instead, they design and oversee a software-automated, algorithm-driven digital “organization” that actually delivers the goods — thereby removing the human bottleneck.
This digital operating model “fundamentally changes the architecture of the firm.” It is modular and connectible, and “when fully digitized, a process can easily be plugged into an external network of partners and providers, or even into external communities of individuals, to provide additional, complementary value.”
Putting AI at the Core
In order to achieve this state of operations, a firm must be fully committed to making artificial intelligence central to its organization. The authors are quick to note that implementation of this strategy does not require a reinvention of the wheel. Conversely, the tools of artificial intelligence and machine learning are readily available today. The real limitation is in management’s understanding and acceptance of the power of this technology.
In the most compelling and clear illustration of what is required to put AI at the heart of the organization, Lakhani and Iansiti detail how to set up an “AI factory” at the heart of an organization. The AI factory, as they describe, is the scalable decision engine that powers the digital operating model of the modern firm – digitizing many processes historically carried out by humans.
The AI factory treats decision-making as an industrial process. “Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide or even automate a variety of operational actions.”
AI Factory Components
- Data pipeline: This is the firehose of data that comes in on customers, products, processes and interactions. Firms must learn to collect, clean and sort this data in a scalable way.
- Algorithm development: The authors call this the “beating heart of the digital firm.” It is the use of data science and algorithms to make predictions about future states.
- Experimentation platform: This platform is where analysis and predictions are tested on a subset of the data collected. Here, theories and expectations are fleshed out and proven.
- Software infrastructure: Once theories are tested and proven, the entire system is embedded into a consistent and componentized software and computing infrastructure that can be connected to internal and external users.
Managers needn’t be able to write the algorithms or implement the strategy themselves. They must, however, understand AI’s power, its limitations, and its risks.
“Many of the best managers will have to retool and learn both the foundational knowledge behind AI and the ways that technology can be effectively deployed in their organization’s business and operations models.”
“In contrast with the current wealth of data, analytics, and AI, we still appear to be suffering from a shortage of managerial wisdom.”
Lakhani and Iansiti caution that managers must find wiser ways to lead the increasingly digital firm. The adoption of the technology is not enough. In this new landscape, we have already reengineered the economics of business and subjected the rate of transformation to Moore’s law.
It is imperative, therefore, that we find better ways to manage the new assets and capabilities that are being created and deployed, every day, across every organization.
The transformation starts at the top with “motivating and grooming a generation of leaders to do the hard work involved.”
Adoption of this new paradigm requires fundamentally changing the organization, transforming its operating architecture, and building the right skills, capabilities, and culture to drive an increasingly digital operating model.
This type of change is only possible when leaders are able to inspire a full, ongoing commitment to the transformation. It is incumbent on senior leadership to be ready to build bridges across the inevitable fractures.
Because, the authors insist, organizations must transform in order to survive.
In order to achieve this transformation, managers must start with an understanding of the digital systems they are creating and leading, and they must have a full appreciation of the organizational, ethical, economic, and political consequences of making the shift to an AI-first company. They do not need to become engineers or data scientists, but they need to understand the power of AI and how it works. And perhaps most importantly, AI leaders still need to master the human side and understand the critical issues that arise in an increasingly digital environment.
It is important for readers to understand what this book is and what it is not.
Competing in the Age of AI is not meant as the single reference for all things AI. It was not written with the goal of making readers experts in machine learning and artificial intelligence, or necessarily to be a reader’s first exposure to the topic. Very little exposition is given on the nature of AI/ML or how it works, so it is safe to say the authors assume a basic understanding of the concept and its general applications.
The book offers several real world examples that show how companies have used AI to transform operations to rapidly improve scale, scope and learning. Lakhani and Iansiti have crafted a framework in this text that outlines how and why businesses should move to an AI-first mentality.
The framework is a broad strategy that is meant to be adapted to individual organizations based on the particulars of each organization. Competing in the Age of AI is the satellite view of the landscape and route map.
The intricacies of the journey are up to the managers themselves.
Four out of Five Robots
I, for one, welcome our robot overlords.
To learn more about artificial intelligence and machine learning, check out the following titles, which range from high level overviews to hands-on engineering manuals.
- The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas H. Davenport
- Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
- Artificial Intelligence: What Everyone Needs to Know by Jerry Kaplan
- Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow
- Machine Learning by Tom M. Mitchell
- Machine Learning for Absolute Beginners: A Plain English Introduction by O. Theobald