Harvard Business Review Special Issues

Winter 2021

Harvard Business Review OnPoint makes it fast and easy to put HBR’s ideas to work. Handpicked by HBR’s editors to bring readers the most relevant ideas and insight on a single business topic, these collections include full-text articles, summaries of key points, and suggestions for further reading, plus content selected from hbr.org.

Pays:
United States
Langue:
English
Éditeur:
Harvard Business School Publishing
Fréquence:
Quarterly
17,04 €(TVA Incluse)

dans ce numéro

2 min
managing our new robot overlords

DESPITE THE GREAT promise that AI holds for companies looking to create better, faster experiences for customers, adopting these technologies is one of the most complex management tasks of a generation. We’ve assembled this special issue to help leaders take control of the process and—maybe—the technology. From an automated Amazon that predicts purchases to bracelets that keep a finance firm’s traders from making purchase decisions on the basis of heightened emotions, applying AI technologies is a critical competitive advantage. The first section of this issue describes these and other company stories and the new ways of thinking about strategy that they’re ushering in. Even once you have a sense of how you could serve your market with AI, adopting and scaling these technologies within your organization is a behemoth of an undertaking.…

16 min
competing in the age of ai

IN 2019, JUST five years after the Ant Financial Services Group was launched, the number of consumers using its services passed the one billion mark. 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. Unlike traditional banks, investment institutions, and insurance companies, Ant Financial is…

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1 min
microsoft’s ai transformation

Microsoft’s transformation into an AI-driven firm took years of research but gained steam with the reorganization of its internal IT and data assets, which had been dispersed across the company’s various operations. That effort was led by Kurt DelBene, the former head of Microsoft’s Office business, who’d left to help fix the U.S. government’s HealthCare. gov site before returning to Microsoft in 2015. There’s a reason that CEO Satya Nadella chose someone with product experience to run IT and build the “AI factory” that would be the foundation of the firm’s new operating model. “Our product is the process,” DelBene told us. “First, we are going to articulate what the vision should be for the systems and processes we support. Second, we’re going to be run like a product development team.…

2 min
putting ai at the firm’s core

The transition from a traditional firm to an AI-driven organization cannot happen in a skunkworks or be spearheaded by some separate autonomous group. It requires a holistic effort. In our research and our work with a variety of companies, we’ve come up with five principles that should guide transformations (beyond common best practices for leading change): One strategy. Rearchitecting a company’s operating model means rebuilding each business unit on a new, integrated foundation of data, analytics, and software. This challenging and time-consuming undertaking demands focus and a consistent top-down mandate to coordinate and inspire the many bottom-up efforts involved. A clear architecture. A new approach based on data, analytics, and AI requires some centralization and a lot of consistency. Data assets should be integrated across a range of applications to maximize their impact. Fragmented…

23 min
the business of artificial intelligence

FOR MORE THAN 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies—a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models. The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML)—that is, the machine’s ability to keep improving its performance without humans having to explain exactly…

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6 min
1. how ai will change strategy: a thought experiment

HOW WILL AI change strategy? That’s the single most common question corporate executives ask the three of us, and it’s not trivial to answer. AI is fundamentally a prediction technology. As advances in AI make prediction cheaper, economic theory dictates that we’ll use prediction more frequently and widely, and the value of complements to prediction—like human judgment—will rise. But what does all this mean for strategy? Here’s a thought experiment we’ve been using to answer that question. Most people are familiar with shopping at Amazon. As with most online retailers, you visit Amazon’s website, shop for items, place them in your “basket,” pay for them, and then Amazon ships them to you. Right now, Amazon’s business model is shopping-then-shipping. Most shoppers have noticed Amazon’s recommendation engine while they shop—it suggests items that…

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