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At the 30,000-foot level of the corporate suite, plotting digital change is heady, exciting stuff. Business leaders can almost smell the gains in efficiency and speed and the data-driven increase in customer satisfaction. But success depends less on inspired strategic planning than on the way people on the front lines implement new digital tools, and most leaders aren’t laying a foundation for those employees to succeed. In large part, that’s because senior managers don’t have any idea what really happens at the ground level. So when tools don’t get used the way they’re supposed to (or even at all), data-driven insights prove unremarkable, and anticipated gains fail to materialize, companies are caught by surprise. Their digital transformations become digital flops.
To avoid that fate, leaders must understand the six phases of digital adoption so that they can create an environment that provides optimal conditions. This article describes that process phase by phase and shows how planning in reverse can lead to change that sticks. Plenty of articles offer theories about and strategies for digital transformation. This one will help you anticipate and manage the gnarly, often-ignored details that destroy many a well-intentioned plan.
Rob Cross, Amy Edmondson, and Wendy Murphy
Many leaders believe that nothing engages and motivates people as much as the larger good they might be doing or the chance to change the world. Accordingly, they extol the higher virtues of their companies’ missions and the meaning of the work they offer. But the authors’ research over the past 20 years reveals that purpose is only one contributing factor to employee engagement; the level and quality of interpersonal collaboration actually has the greatest impact. This article explores why collaboration has that effect and how you can nurture it in your organization to spark change.
First, you must lay a strong foundation of psychological safety and trust. That is a necessary, but not sufficient, condition. Once trust is established, you must instill a sense of purpose — the conviction that the work being done has meaning and impact. And once purpose is established, you must generate energy — a day-to-day enthusiasm within the workforce. The authors have found that 27 leadership behaviors foster trust, purpose, and energy. By adopting those behaviors and rewarding them in others, you can fuel collaboration and boost engagement, creating the conditions for change.
With the rapid changes technology has brought to business and society, calls for employee learning have become more urgent. But people are ambivalent about it, if not outright resistant. We want to learn, but we worry that it will cost us too much. And what if, in the process, we’re found lacking? What if we simply cannot pick up the knowledge and skills we need? Furthermore, most organizations are not as hospitable to learning as their rhetoric suggests.
How can employers better support learning, and how can we as individuals do it more effectively? By understanding that there are two kinds of learning — incremental and transformative — and that each requires its own space and challenges us in different ways. For incremental learning, we need a more focused, less distracting, safer replica of our workplace — a boot camp (a training session, say, or a course) where we can practice the best possible way of doing things, get feedback, and try again. If we are after transformative learning, what we need is a familiar yet open frame — a playground (think offsite retreat) that magnifies our habits and the culture that breeds them, so we can examine both and imagine and try new ways of being. Most organizations promise to help their members learn, but only those that provide both kinds of spaces truly keep that promise.
Sigal Barsade, interviewed by Frieda Klotz
Although many companies display heightened concern for the well-being of their employees, not everyone is convinced that efforts to create and maintain a positive workplace actually pay off. However, to Wharton professor Sigal Barsade, the evidence is clear: Companies that want more satisfied employees and stronger performance need to invest in understanding what motivates people in their work lives and pay attention to the emotional side of organizational culture.
Over the past two decades, Barsade has studied a variety of related topics, including group affect, emotional contagion, and loneliness in the workplace. Through her research, she has found that emotions influence not just employee wellness and engagement but also business outcomes like productivity and profitability. The findings have implications for startups and established organizations alike and are relevant to everyone, from the senior management team to front-line workers. “Even when it comes to upbeat feelings,” she says, “managers should think about the kind of emotional culture that will work best for achieving their business goals. Different positive emotions lead to different outcomes.”
George S. Day and Paul J.H. Schoemaker
The costs of being slow to sense threats and opportunities on the competitive horizon can be devastating. Just ask RadioShack. After jumping into mobile phone distribution in the 1990s, the U.S.-based retail chain didn’t respond quickly enough to the rigors of e-commerce and failed to find a way to connect with the new generation of electronics tinkerers and makers. Following multiple attempts to regain its footing, the once-thriving company filed for bankruptcy in 2015 and never recovered. Businesses can avoid such perils by spotting directional shifts ahead of their rivals.
What sets the most vigilant companies apart are not the tools and methods they use but their systematic approaches to determining where to look and how to explore. They tend to take four basic steps. First, they strategically scope the environment, often scanning beyond their comfort zones, to begin identifying where relevant signals may come from. Second, they formulate guiding questions that direct the organization’s scarce attention to the places most likely to spawn threats or opportunities. Third, they conduct targeted analyses to better understand the sources and meanings of any weak signals they pick up. Finally, they track the most intriguing signals, amplifying and clarifying them sufficiently to act decisively when the fog clears.
Blockchain assures users that once information has been stored, it can never be deleted or falsified. This means that when people in finance, for example, pore over the history of a transaction, they feel content in the knowledge that illegalities have nowhere to hide. It means that people in the supply chain of a product trust that they can check its provenance without fear that misinformation has been slipped in along the way. In essence, blockchain promises not just complete data security but something more intangible: that we will never be conned.
But the truth is that blockchain is not as secure as it is believed to be, and its features can rebound in unfortunate ways. The author and his research partner cataloged 72 breaches reported between 2011 and 2018. These breaches cost users a grand total of more than $2 billion. Many of these breaches were possible because blockchain is actually vulnerable in some of the same ways that conventional centralized record-keeping systems are. The rest are even more troubling, because bad actors were able to exploit the very features that make blockchain revolutionary: transparency, distributed control, anonymity, and immutability. This article looks closely at both categories of vulnerabilities so that businesses can weigh the risks and decide whether to make use of blockchain.
Glen Urban, Artem Timoshenko, Paramveer Dhillon, and John R. Hauser
Deep learning is delivering impressive results in many AI applications. Given all this activity, many wonder how the underlying methods will alter the future of marketing. To what extent will they help companies design profitable new products and services to meet the needs of customers? The technology that underpins deep learning is becoming increasingly capable of analyzing big databases for patterns and insights. It isn’t difficult to imagine a day when companies will be able to integrate a wide array of databases to discern what consumers want with greater sophistication and analytic power and then leverage that information for market advantage.
To be sure, a lot of managers already use analytics with statistical models and focused databases to track brand performance, schedule promotions, and make spending decisions. So how is deep learning different? Is it a fundamental leap forward, or will it simply enable marginal gains? This article examines these questions in relation to a study the authors conducted involving credit cards. Their research suggests that while deep learning may not lead to large gains in predictive accuracy right away or in every setting, there are reasons for optimism.