Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World – Marco Iansiti and Karim R. Lakhani Book Summary

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World – Marco Iansiti and Karim R. Lakhani | Free Book Summary

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World – Marco Iansiti and Karim R. Lakhani

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World is a book by Marco Iansiti and Karim R. Lakhani which looks at how traditional firms can stay competitive in the modern business landscape.

The authors explore how firms can use data, analytics, and AI to increase their scale and scope and improve their ability to learn and adapt. They provide insights on how to make the most of AI-driven technologies and how to manage the risks associated with them. 

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Additionally, the book provides a framework for how traditional firms can leverage AI and data to gain competitive advantage.

The Challenge Ahead

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AI can render skills and talents obsolete, from driving a car to managing a traditional retail establishment. Digital networks can alter and transform accepted approaches to social and political interaction, from dating to voting.

The broad deployment of AI could threaten millions of jobs in the United States alone. And beyond the erosion of capability, threats to traditional skills, and other direct economic and social impact, we are increasingly vulnerable as an increasing portion of our economy and our very lives become embedded in digital networks.

The AI factory

The AI factory is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. Managerial decisions are increasingly embedded in software, which digitizes many processes that have traditionally been carried out by employees.

Experience from Netflix and other leading firms underlines the importance of a few essential AI factory components

The AI components

Data pipeline:

This process gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way.

Algorithm development:

The algorithms generate predictions about future states or actions of the business. These algorithms and predictions are the beating heart of the digital firm, driving its most critical operating activities.

Experimentation platform:

This is the mechanism through which hypotheses regarding new prediction and decision algorithms are tested to ensure that changes suggested are having the intended (causal) effect.

Software infrastructure:

 These systems embed the pipeline in a consistent and componentized software and computing infrastructure, and connect it as needed and appropriate to internal and external users.

Capability Foundations

The most obvious challenge in building an AI-centered firm is to grow a deep foundation of capability in software, data sciences, and advanced analytics.

Naturally, building this foundation will take time, but much can be done with a small number of motivated, knowledgeable people.

Network vs Learning Effect

Network effects describe the value added by increasing the number of connections within and across networks, such as the value to a Facebook user of having connections with a large number of friends, or access to a broad variety of developer applications.

The most important value creation dynamic of a digital operating model is its network effects. The basic definition of a network effect is that the underlying value or utility of a product or service increases as the number of users utilizing the service increases.

Learning effects capture the value added by increasing the amount of data flowing through the same networks—for example, data that may be used to power AI to learn about and improve the user experience or to better target advertisers.


The first and most important force shaping value capture is multihoming. Multihoming refers to the viability of competitive alternatives, specifically to situations wherein users or service providers in a network can form ties with multiple platforms or hub firms (“homes”) at the same time.

If a network hub faces competition from another hub connecting to a network in a similar way, the first network hub’s ability to capture value from the network will be challenged, especially if the switching costs are low enough for users to easily use either hub.


Disintermediation, wherein nodes in a network can easily bypass the firm to connect directly, can also be a significant problem for capturing value. From Homejoy to TaskRabbit—that provides only a connection between network participants.

After the first connection is made, most if not all of the value created is delivered, and it’s difficult to hold a user accountable to the network hub for ongoing rents.

Network Bridging

Network bridging involves making new connections across previously separate economic networks, making use of more-favorable competitive dynamics and different willingness to pay.

Network participants can improve their ability to both create and capture value when they connect to multiple networks, bridging among them to build important synergies.

Strategic Collisions

A collision occurs when a firm with a digital operating model targets an application (or use case) that has traditionally been served by a more conventional firm.

Because digital operating models are characterized by different scale, scope, and learning dynamics from those of traditional firms, collisions can completely transform industries and reshape the nature of competitive advantage.

An important movement in our socio-economic history

We live in an important moment in the history of our economy and society. As digital networks and AI increasingly capture our world, we are seeing a fundamental transformation in the nature of firms. This removes historical constraints on scale, scope, and learning and creates both enormous opportunity and extraordinary turbulence.

But despite all this newfound digital automation, it seems that we can’t quite do away with management just yet.

The challenges are just too great, too complex, and too amorphous to be solved by technology (or technologists) alone. But leading through these changing times will require a new kind of managerial wisdom, to steer organizations from full-scale firms to new ventures, and from regulatory institutions to communities.

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