Harvard Business Review Special Issues Summer 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.

United States
Harvard Business School Publishing


how to thrive in a tech-driven world

WE’VE ALL HEARD how much we need “digital skills.” But what are these skills? They’re more than just a change in behavior: Thriving in the digital age requires a shift in mindset. To take advantage of emerging technologies, leaders are reconceiving how they go about developing strategy, how their organizations are structured, and how work gets done. Managers are rethinking how they manage their virtual, cross-functional teams. And ambitious leaders at all levels are learning to work with data and the new tools that permeate the workplace. All this adds up to digital intelligence. If you’re overwhelmed by the concept, the articles we’ve curated in this issue should help. Begin by getting a sense of the latest technological shifts and how they’re reshaping organizations. For example, companies that are serious about AI…

building the ai-powered organization

ARTIFICIAL INTELLIGENCE IS reshaping business—though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon. The technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable. The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade. Yet, despite the promise of AI, many organizations’ efforts with it are falling short. We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in…

idea in brief

THE PROBLEM Many companies’ efforts to scale up artificial intelligence fall short. That’s because only 8% of firms are engaging in core practices that support widespread adoption. THE SOLUTION Cutting-edge technology and talent are not enough. Companies must break down organizational and cultural barriers that stand in AI’s way. THE LEADERSHIP IMPERATIVES Leaders must convey the urgency of AI initiatives and their benefits for all; spend at least as much on adoption as on technology; organize AI work on the basis of the company’s AI maturity, business complexity, and innovation pace; and invest in AI education for everyone.…

organizing ai for scale

Governing coalition A team of business, IT, and analytics leaders that share accountability for the AI transformation Hub A central group headed by a C-level analytics executive who aligns strategy Responsibilities Talent recruitment and training strategy Performance management Partnerships with providers of data and AI services and software AI standards, processes, policies Gray area Work that could be owned by the hub or spokes or shared with IT Responsibilities Project direction, delivery, change management Data strategy, data architecture, code development User experience IT infrastructure Organizational capability assessment, strategy, funding Spoke A business unit, function, or geography, which assigns a manager to be the AI product owner and a business analyst to assist him or her Responsibilities Oversight of execution teams Solution adoption Performance tracking Execution teams Assembled from the hub, spoke, and gray area for the duration of the project Key roles Product owner Analytics translator Data scientist Data engineer Data architect Visualization specialist UI designer Business analyst…

10 ways to derail an ai program

1. They lack a clear understanding of advanced analytics, staffing up with data scientists, engineers, and other key players without realizing how advanced and traditional analytics differ. 2. They don’t assess feasibility, business value, and time horizons, and launch pilots without thinking through how to balance short-term wins in the first year with longer-term payoffs 3. They have no strategy beyond a few use cases, tackling AI in an ad hoc way without considering the big-picture opportunities and threats AI presents in their industry. 4. They don’t clearly define key roles, because they don’t understand the tapestry of skill sets and tasks that a strong AI program requires. 5. They lack “translators,” or experts who can bridge the business and analytics realms by identifying high-value use cases, communicating business needs to tech experts, and…

in the ecosystem economy, what’s your strategy?

WHEN NESTLÉ WAS preparing to go mainstream with Nespresso, its single-use espresso capsule, it knew that users would need a machine specifically designed to work with the pod. So the company cultivated a network of manufacturers. It didn’t tell customers to buy a Jura, a Krups, or a Braun—it just decided which manufacturers could be on the list. And because the capsule and its interface were patented, other manufacturers could not make Nespresso-compatible machines without permission. Nespresso was creating—designing—an ecosystem: an orchestrated network spanning multiple sectors. The firms involved work to shared standards, sometimes on a shared platform, to make their products and services compatible. And they create links among themselves that make it difficult for outsiders to break in. Designed ecosystems like Nespresso’s are increasingly important, owing to the convergence of…