COMPETING IN THE AGE OF AI: Strategy & Leadership When Algorithms And Networks Run The World (Marco Iansiti and Karim Lakhani)
HBR Press has a unique record of publishing seminal books written by Harvard Business School’s academics.
This review is of one in a line of three books books released roughly 20 years apart that have documented fundamental shifts in the way companies compete in their markets, deliver unprecedented value to their customers, and becomine phenomenally successful global companies. In the 1980s, Michael Porter published the first from his now famous Competition trilogy. “COMPETITIVE ADVANTAGE”, changed the way we view strategy. His later follow-on “Competitive Strategy” changed the way we formulate strategy. In the 2000s, Mike Cusumano’s book “COMPETING ON INTERNET TIME” provided a penetrating analysis of strategy-making and product innovation in the dynamic markets of commercial internet. Microsoft was a central player featured in this book, as a behemoth caught on the wrong foot by an emerging internet giant of the time. In 2020, Marco Iansiti & Karim Lakhani have again documented a fundamental shift in the way companies create value with “COMPETING IN THE AGE OF AI”. Its also of coincidental interest for us to note that Microsoft is again featured here – this time as a completely reinvented company that is writing the rules of market competition in the age of Artificial Intelligence.
What is a AI-driven Company?
Iansiti and Lakhani present a framework for rethinking how companies function – redesigning both their business and operating models. They describe how the core of the digital firm works with decisions powered by software, data, and algorithms, which they call the AI factory, and its implications for management and strategy.
Using a series of interesting case studies about companies that are household names, and a few that aren’t yet in the United States, the authors point to how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning–to drive ever more accurate, complex, and sophisticated predictions.
How are these companies different from traditional global corporations we have seen operating so far?
For one, these companies ensure there are no workers in its “critical path” of value delivery, leveraging an approach of AI-driven firm’s operating activities. At the core of this new kind of firm is a decision factory, the “AI factory.” In all cases digital decision factories handle some of the most critical processes and operating decisions. Software makes up the core of the firm, while humans are moved to the edge.
How does this approach create new competitive advantage?
The more customers you have, the more data you can gather, and that data, when analyzed with machine-learning tools, allows you to offer a better product that attracts more customers. You can then collect even more data and eventually marginalize your competitors in the same way that businesses with sizable network effects do. Gathering customer information and using it to make better products and services is an age-old strategy, but the process used to be slow, limited in scope, and difficult to scale up. That changed dramatically with the advent of the cloud and new technologies that allow firms to quickly process and make sense of vast amounts of data. Internet-connected products and services can now directly collect information on customers, including their personal details, search behavior, choices of content, communications, social media posts, GPS location, and usage patterns. After machine-learning algorithms analyze this “digital exhaust,” a company’s offerings can be automatically adjusted to reflect the findings and even tailored to individuals.
Scale, scope, and learning have come to be considered the essential drivers of a firm’s operating performance. In traditional operating models, scale inevitably reaches a point at which it delivers diminishing returns. Rather than relying on traditional business processes operated by workers, managers, process engineers, supervisors, or customer service representatives, the value we get is served up by algorithms.
Organizations driven by AI at their core excel at connecting businesses, aggregating the data that flows among them, and extracting its value through analytics and AI will have the upper hand. Traditional network effects and AI-driven learning curves will reinforce each other, multiplying each other’s impact.
Iansiti & Karimi pont to a Chinese company, Ant Financial, as an interesting case study to illustrate the power of learning-driven scalability. Spun out of Alibaba, Ant Financial uses artificial intelligence and data from Alipay—its core mobile-payments platform—to run an extraordinary variety of businesses, including consumer lending, money market funds, wealth management, health insurance, credit-rating services, and even an online game that encourages people to reduce their carbon footprint. The company serves more than 10 times as many customers as the largest U.S. banks—with less than one-tenth the number of employees. At its last round of funding, in 2018, it had a valuation of $150 billion—almost half that of JPMorgan Chase, the world’s most valuable financial-services company.
The authors also claim many other firms have begun the journey towards transitioning to AI-driven operating model. Many—including Nordstrom, Vodafone, Comcast, and Visa—had already made important inroads, digitizing and redesigning key components of their operating models and developing sophisticated data platforms and AI capabilities. According to them, you don’t have to be a software start-up to digitize critical elements of your business—but you do have to confront silos and fragmented legacy systems, add capabilities, and retool your culture.
Fidelity Investments is another company in using AI in delivering financial services by enabling processes in important areas, such as customer service, customer insights, and investment recommendations. Its AI initiatives build on a multiyear effort to integrate data assets into one digital core and redesign the organization around it.
What happens when AI-driven companies confront traditional companies in the marketplace?
Iansiti and Lakhani explain how “collisions” between AI-driven/digital and traditional/analog firms are reshaping industries, altering the course of our economy, and forcing traditional companies to rearchitect their operating models. They also point out one doesn’t have to be a software start-up to digitize critical elements of your business—but you do have to confront silos and fragmented legacy systems, add capabilities, and retool your culture.
It is a capability that larger mature companies could bring in as well by re-architecting the firm’s organization and operating model. For a long time, the involvement of IT as an enterprise enabler led to the creation of enterprise systems which often reinforced organizational silos and the divisions across functions and products, causing a fragmentation of data meant to create logical linkages piecing together an entire 360 customer view. The new firm aggregates the firms data assets by connecting federated data stores to enabling wider sharing through the extensive use of application programming interfaces and data catalogs to help create predictive models. These models in turn are prepared for deployment via hypothesis driven experiments rolled out across select samples of customers at scale and using learnings from these experiments to create rapid product/service prototypes that incorporate the insights gained from hypothesis testing. The outcome from this deployment approach has resulted in products that have received mass adoption on a global scale literally overnight, that in turn opened a gold-mine of new usage data invaluable to fine-tuning this new product while in flight.
However, in order to achieve this, companies must change their approach to formulating strategy as well.
Instead of focusing on industry analysis and on the management of companies’ internal resources, strategy needs to focus on the connections they could create across industries and the flow of data through the networks the firms use. Network analysis is an essential element of strategy development for competing in a AI world. However to be clear, network effects produce little value before they reach critical mass, and most newly applied algorithms suffer from a “cold start” before acquiring adequate data.
Finally in conclusion, the authors outline a few considerations business leaders and regulators must confront in this new world. Algorithmic bias, data security and privacy – as well as platform control and equity – are new challenges and responsibilities leaders of both digital and traditional firms need to address. “Our new meta is generating enormous opportunity, as evidenced by economic growth. But its also leaving us struggling to understand the full implications of the new rules, dealing with a range of new problems, and coping with an increasingly complex consequences”.