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As author Ryan Cohen was on the verge of launching an online jewelry business with cofounder Michael Day, he had a revelation: He was standing in a local pet store with his toy poodle, Tylee, discussing her food with the proprietor, when he realized that millions of other pet owners were equally concerned about their animals' well-being. Clearly, the opportunity was huge--and he cared much more about pet food than about jewelry. He went door-to-door in Silicon Valley looking for funding, but not until Chewy had beaten sales projections did the company find a significant backer, at Volition Capital. Cohen describes that first round of funding as a major watershed. "From that point on, the mission was larger," he writes. "I was even more committed to making Chewy an industry leader because it was no longer just our own money on the line. . . I felt that responsibility." The financing allowed him and his team to scale up the operation rapidly and plan for an IPO, but before that happened, they sold Chewy to PetSmart for $3.35 billion. Its early-stage investors made huge gains, and later-stage ones made significant money. "Those investors put their trust in me and my vision," Cohen writes, "and I repaid them with returns."

Today markets are being reshaped by a new kind of firm--one in which artificial intelligence runs the show. This cohort includes giants like Google, Facebook, and Alibaba, and growing businesses such as Wayfair and Ocado. Every time we use their services, the same thing happens: Rather than relying on processes run by employees, the value we get is delivered by algorithms. Software is at the core of the enterprise, and humans are moved off to the side. This model frees firms from traditional operating constraints and enables them to compete in unprecedented ways. AI-driven processes can be scaled up incredibly quickly, allow for greater scope because they can be connected to many kinds of businesses, and offer very powerful opportunities for learning and improvement. And while the value of scale eventually begins to level off in traditional models, in AI-based ones, it never stops climbing. All of that allows AI-driven firms to quickly overtake traditional ones. As AI models blur the lines between industries, strategies are relying less on specialized expertise and differentiation based on cost, quality, and branding, and more on business network position, unique data, and the deployment of sophisticated analytics.

Discussions about reining in health care costs invariably turn to the sickest 5% of the population, who account for 50% of all health care spending. Most of these patients have multiple chronic conditions, and the hope is that through disease-management programs that use registered nurses and social workers to monitor and help them, we can care for them better and achieve big savings. But these programs are expensive. Typically each focuses on just one disease, which means that many patients deal with multiple teams. The programs also operate outside primary-care practices, so they often duplicate doctors' work as well. And in the experience of the authors, the former CEO and associate executive director of Kaiser Permanente (KP), they do not reduce net costs. KP has come up with a better approach: providing coaching and support to patients through IT and inexpensive assistants who are integrated into primary-care practices--avoiding duplication. KP applies it judiciously, focusing only on patients whose chronic conditions can truly be improved (about a third of the most expensive 5%). This strategy has not only led to better medical outcomes but cut costs so much that KP has been able to lower premiums for millions of members by 10% to 15%.

Culture is easy to sense but hard to measure. The workhorses of culture research--employee surveys and questionnaires--are notoriously unreliable. Studying the language that employees use in electronic communication has opened a new window into organizational culture. New research analyzing email, Slack messages, and Glassdoor postings are challenging prevailing wisdom about culture. Some of the findings: (1) Cultural fit is important, but what predicts success most is the rate at which employees adapt as organizational culture changes over time. (2) Cognitive diversity helps teams during ideation but hinders execution. (3) The best cultures encourage diversity to drive innovation but are anchored by shared core beliefs.

In today's fast-changing business environment, employees' continual development is essential. That's leading to a new and more powerful role for chief learning officers: They're not just trainers anymore; they're transformers who are reshaping organizational capabilities and culture. The authors interviewed CLOs at 19 large companies and found that transformers are revamping their organizations' learning goals, learning methods, and learning departments. Their efforts offer a road map for anyone wishing to build a stronger workforce. With respect to learning goals, the aim should be not just to teach employees specific skills but to cultivate broader capabilities, including strong leadership, digital literacy, and a growth mindset. With respect to learning methods, the trend is to move away from traditional classroom instruction and adopt approaches that can provide learning to more employees, in more-customized ways. (Think online and audio courses, videoconferencing, interactive simulations, and formal opportunities for employees to reflect on and apply what they've been taught.) Finally, with respect to transforming learning departments, the key is to make them leaner, more agile, and more strategic by curating useful external content, encouraging peer teaching, measuring the impact of training, and providing tools to tailor learning plans to individuals.

Many executives assume that customer data can give you an unbeatable competitive edge. The more customers you have, the more data you can gather, and that data, when analyzed, allows you to offer a better product that attracts more customers. You can then collect even more data, repeating the cycle until you eventually marginalize your competitors. But this thinking is usually wrong. Though the virtuous cycles of data-enabled learning may look similar to those of network effects--wherein an offering increases in value to users as more people adopt it and ultimately garners a critical mass of users that shuts out competitors--they are not as powerful or as enduring. Nevertheless, under the right conditions, customer data can help build competitive defenses. It all depends on whether the data offers high and lasting value, is proprietary, leads to improvements that can't be easily imitated, or generates insights that can be quickly incorporated. Those characteristics do give firms an advantage. And when learning from one customer rapidly improves an offering for others (think Google Maps), people will care about how many other people are adopting it, and it will enjoy those sought-after network effects.

Judgment--the ability to combine personal qualities with relevant knowledge and experience to form opinions and make decisions--is "the core of exemplary leadership," according to Noel Tichy and Warren Bennis (the authors of Judgment: How Winning Leaders Make Great Calls). It is what enables a sound choice in the absence of clear-cut, relevant data or an obvious path. Likierman believes that a more precise understanding of what exactly gives someone good judgment may make it possible for people to learn and improve on it. He approached CEOs at a range of companies, from some of the world's largest right down to start-ups, along with leaders in the professions: senior partners at law and accountancy firms, generals, doctors, scientists, priests, and diplomats. He asked them to share their observations of their own and other people's exercise of judgment so that he could identify the skills and behaviors that collectively create the conditions for fresh insights and enable decision makers to discern patterns that others miss. As a result, he has identified six key elements that collectively constitute good judgment: learning, trust, experience, detachment, options, and delivery. He describes these areas and offers suggestions for improvement in each one.

It can be cognitively demanding to understand how a system or organization made up of many very different interconnected elements actually works. But the fact that such systems or organizations are difficult to understand doesn't make them inherently bad. In addition to its more obvious costs, complexity confers critical benefits, especially in dynamic and uncertain environments. The authors draw on their experience and perspectives in business, biology, and physics to offer some reflections on the nature, benefits, and costs of complexity and provide some guidance on managing it. Their recommendations: In growing your organization, make sure that it remains modular in structure and that all components and connections conform to a small number of simple operating principles. Embed a bias for change, avoid imposing too many controls on your people, and let the market judge which changes work. Finally, always optimize your organization globally and keep fixing, repairing, and pruning.

To conduct international commerce, businesses have built an intricate system of networks that move money, information, and components around the world. These networks may look decentralized, but all too frequently, they have major choke points. The majority of global finance transactions, for instance, are relayed through a single organization in Belgium. Many global tech firms depend heavily on the chips Qualcomm makes for Android devices. A huge proportion of global communications are routed through private servers on U.S. soil. Increasingly, these choke points are being turned into political weapons by governments, and companies are getting caught in the cross fire. The stakes are high: Firms can go out of business if they're cut off from critical networks. They need to analyze their exposure and develop a strategy to protect themselves.

Most of us think of ourselves as good people. We set out to be ethical at work, and we hope that in pivotal moments we will rise to the occasion. But when it comes to building an ethical career, good intentions are insufficient. Decades' worth of research has identified psychological processes and biases that cloud people's moral judgment, leading them to violate their own values, and often to create contorted, post hoc justifications for their behavior. How can we ensure that we will consistently do the right thing in our professional lives? The authors argue for a mindset shift to what they term "moral humility"--the recognition that we all have the capacity for ethical transgressions if we aren't vigilant. They suggest a three-stage approach for staying on the straight and narrow: Prepare in advance for moral challenges, including instituting proper safeguards; make good decisions in the moment; and reflect on and learn from moral successes and failures.

During his 20-year career as a professional boxer, Sugar Ray Leonard won world titles in five weight classes, became the first in his sport to earn more than $100 million in prize money, and helped fuel fans' interest in boxing following Muhammad Ali's retirement. In this interview he talks about bypassing boxers' traditional reliance on promoters, why he retired repeatedly, which of his fights is most worth rewatching, and more.

Large established organizations need to innovate as much as small entrepreneurial firms. The problem is, it takes real creativity to build something new out of something old. Harvard Professor and innovation expert Gary Pisano introduces the concept of 'creative construction', which entails redesigning an enterprise in a fundamental way while still operating the core business. He defines four types of innovation that large enterprises should embrace, and argues that all four types likely have a place in every organization's innovation portfolio.

Data science may well prove to be the most rewarding and exciting profession of the 21st century. That's because advances in computer processing speeds and reductions in data storage costs are enabling us to reach conclusions from large data sets in ways that were simply not possible 20 or 30 years ago. However, the author shows that this seismic shift in business comes with several key issues for society: data privacy, biased algorithms, ethics and transparency. In the end, he says, a continuing challenge for the human race will be to address these issues and figure out how to partner with machines in ways that benefit rather than harm humankind.

We often assume that only purely digital companies like Google or Netflix can be called 'algorithmic'. But the reality is that every type of organization, at every scale, will soon live and breathe by its capacity to leverage data, automation and algorithms to be more effective and create better customer experiences. The author shows that as a result, a new breed of leader is emerging with an ability to use machine intelligence to transform organizations - and reinvent the world. He argues that these 'algorithmic leaders' are different from traditional leaders in three key ways.

Since 2006, China has been in fast-forward mode as government ministries have rolled out ambitious plans to make the country an innovative society, with a growing emphasis on science and technology and the declared aim to become a world leader in advanced manufacturing and artificial intelligence (AI). Can China meet its lofty goals? In this article, the author summarizes some of the advantages China enjoys over other countries -as well as the challenges it faces in making its vision a reality.

Disruption is no longer a once-in-a-career problem to be dealt with. It's a constant cycle. Technology is increasingly creating the tools your competitors - incumbents and entrepreneurs alike - are using to build new digital products and services that target and release latent demand and serve unmet needs. The authors, senior leaders at Accenture, call that potential revenue 'trapped value' - and they argue that if you don't get to it before others, you may find not just your future growth disrupted, but your current business as well.

No one would argue that that online and mobile banking have transformed the financial services industry. Tech-savvy customers welcome online banking for its convenience, and banks view it as a way to increase efficiency. But the author argues that despite the rapid growth of digital tools and solutions, personal relationships remain critical in the financial sector. She describes her research findings, which indicate that there are hidden costs to abandoning personalized banking relationships - and that such relationships between borrowers and lenders can benefit both parties.

Despite widespread agreement on the importance of creativity, leaders often fail to recognize creative ideas-or worse, reject them outright. The authors summarize research pointing to a variety of conditions that bias managers toward judging creative ideas negatively. Heeding the call for tools that aid in the recognition of creativity, they then describe their model for successfully pitching ideas in organizations, which, assuming true idea novelty, features two critical proactive behaviours on the part of the employee: idea enactment and influence tactics.

In a world plagued by information overload, it is not surprising that we often resort to categorical thinking to simplify incoming information. The problem is, this habit can manifest itself in a tendency to group individuals on the basis of social categories including race, gender and age. Further, such categories are often imbued with associations and expectations - a.k.a. stereotypes. One little-discussed sub-category of stereotypes is 'occupational stereotypes' - collections of traits or attributes with which individuals associate members of different occupations. The authors describe their research into these stereotypes, showing how they lead to occupational segregation and impact vocational choice, recruitment and selection.

The billionaire founder of Sun Microsystems Vinod Khosla talks to Creative Destruction Lab Founder Ajay Agrawal about the AI-generated opportunities (and dangers) that lie ahead. In particular, he points to the potential of AI to increase income disparity to dangerous levels that could lead to societal unrest. He also argues that lower skilled jobs like truck driver and restaurant worker will likely be fine, but that higher skilled knowledge-based jobs like radiologists are in trouble. On the bright side, AI could eradicate unsatisfying work; we are at the beginning of a continuous 'hypercycle' of innovation; and the future is shapeable: Technology doesn't rule; it serves. We get to decide on its goals.