These summaries will help you navigate our new slate of features.
Senén Barro and Thomas H. Davenport
Thoughtful adoption of intelligent technologies will be essential to survival for many companies. But simply implementing the newest technologies and automation tools won’t be enough. Success, the authors argue, will depend on whether organizations use them to innovate in their operations and in their products and services — and whether they acquire and train the human capital to do so.
Surveys show that most senior executives believe AI will substantially transform their organizations within the next few years, which means humans will need to find ways to work closely with machines. Few organizations have begun the necessary job redesign, re-skilling, or retraining programs, the authors say. Moreover, most individuals aren’t being adequately prepared for automation-enabled work. Smart organizations will need to adopt intelligent technologies, and they will need to recruit and retrain people for skilled roles and redesign tasks and jobs. What’s more, they will need to use artificial intelligence as an enabler of innovation in products, processes, and business models. Rather than being implemented systematically, the authors expect “innovation based on intelligent automation” to occur on a job-by-job, task-by-task basis.
While the potential for AI-enabled innovation exists in virtually every aspect of business and society, the authors say it is largely unrealized today. Technology vendors are conceiving and producing innovations ranging from self-driving cars and trucks to the “self-driving enterprise.” But few would-be adopters have even begun the process of envisioning how AI will change jobs in their companies and what new skills must be developed.
David Kiron and Michael Schrage
Executives intent on exploiting AI to enhance processes or products tend to focus on having a strategy for AI. But creating strategy with AI can matter as much or even more.
What does strategy with AI mean? Like any corporate strategy, it expresses what enterprise leaders deliberately seek to emphasize over a given time frame. It articulates how and why the organization expects to succeed in its chosen market. These aspirations might involve, for example, superior customer experience and satisfaction, increased growth or profitability, greater market share, or agile fast-followership.
Whatever the specific strategy, virtually all organizations create corresponding measures to characterize and communicate desirable strategic outcomes. In a machine learning era, enterprise strategy is defined by the key performance indicators (KPIs) leaders choose to optimize. Those are the measures organizations use to create value, accountability, and competitive advantage. AI can help determine what KPIs are measured, how they are measured, and how best to prioritize them. Indeed, world-class organizations can no longer meaningfully discuss optimizing strategic KPIs without embracing machine learning capabilities.
Because metrics drive strategy, determining the optimal “metrics mix” for key enterprise stakeholders becomes an executive imperative. Achieving KPI outcomes (and suggesting new KPIs) is what smart machines must do — and must learn to do. Of course, AI and machine learning are both a means to an end. The true strategic opportunity of these technologies is the chance to rethink and redefine how the enterprise optimizes value for itself and its customers.
Monideepa Tarafdar, Cynthia M. Beath, and Jeanne W. Ross
Artificial intelligence invariably conjures up visions of self-driving vehicles, obliging personal assistants, and intelligent robots. But AI’s effect on how companies operate is no less transformational than its impact on such products.
The use of AI to enhance business operations, or enterprise cognitive computing (ECC), involves embedding algorithms into applications that support organizational processes. ECC applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs.
Although business and technology leaders are optimistic about the value-creating potential of ECC, the rate of adoption is low, and benefits have proved elusive. Generating value from ECC applications is not easy — and that reality has caught many business leaders off guard. Often, companies that hope but fail to benefit from ECC have not developed the necessary organizational capabilities.
To help address that problem, the authors undertook a program of research aimed at identifying the foundations of ECC competence. They found that companies need to develop five capabilities in order to splice the ECC gene into their organization’s DNA: data science competence, business domain proficiency, enterprise architecture expertise, an operational IT backbone, and digital inquisitiveness. Organizations must then apply those capabilities to derive value from applications. Four practices in particular help them do that: Develop clear and realistic use cases, manage ECC application learning, cocreate throughout the application life cycle, and think “cognitive.” These practices create the conditions for applications — and their underlying AI algorithms — to deliver on their promise.
AI is a powerful technology that spares us from having to undergo many mundane, time-consuming annoyances. The problem is that such annoyances play a key adaptive function. Our interactions with people and the wider world of physical objects help us learn to adjust our conduct in relation to one another and the world around us. Engaging directly with a grocery bagger, for instance, forces us to confront her humanity, and the interaction (ideally) reminds us not to get testy just because the line isn’t moving as quickly as we’d like. Walking, bicycling, or driving in a crowded city teaches us how to compensate for unforeseen obstacles, such as varying road and weather conditions.
Through the give-and-take of such encounters, we learn to temper our impulses by exercising compassion and self-control. In countless occasions every day, each of us seeks out an optimal compromise between shaping ourselves to fit the world and shaping the world to fit ourselves. This kind of adaptation has led us to become self-reflective, capable of ethical considerations and aspirations.
But increasingly, AI takes such interactions out of our days, allowing us to off-load cognitive, emotional, and ethical labor to software. As a result, we may gradually lose the inclination and capacity to engage in critically reflective thought. This article considers that problem and provides a framework to help AI designers tackle it through system enhancements in smartphones and other products and services in the burgeoning internet of things marketplace.
Edgar H. Schein and Peter A. Schein
When social psychologist Edgar Schein joined MIT’s Sloan School of Management in the 1950s, it had just launched the great experiment of teaching management through formal disciplines like mathematics, social psychology, economics, and history. That was a radical departure from expounding “the practice of management” through cases taught by professors who had been managers most of their careers. The new approach sparked close, unlikely collaborations and deep, innovative thinking about leadership, group cultures, and organizational change — all nascent fields of study at the time. It was in this environment that Ed and his colleagues embarked on what he calls “an exciting quarter century of model building,” which helped define how people thought about and engaged with organizations.
Decades later, in a digital era, it is time for a new model, one that is built on close professional relationships, openness, and trust. That is what Ed and Peter Schein, his son and collaborator, have been working on for the past couple of years. Peter spent most of his career as a strategy executive in a number of Silicon Valley companies before he decided to join Ed in analyzing and describing the changes afoot as the tasks of management become more complex, interdependent, and volatile. In this conversation, they share their perspectives on organizational life, a brief history of ideas leading up to this moment, and their thoughts about the future.
George Westerman, Deborah L. Soule, and Anand Eswaran
Even though traditional organizations find much to admire and learn from in the cultures of born-digital companies, many are trying to embrace aspects of digital culture without abandoning everything that has made them strong. For instance, industrial giant Haier has spent years transforming its culture in pursuit of greater speed and innovation while maintaining the efficiency and stability of its traditional manufacturing and logistics processes. KBC Bank is adapting to fight fast-moving fintech entrants while complying with strict European privacy and employee protection regulations.
For many legacy companies, culture change is the biggest challenge of digital transformation. How can a company become more agile and innovative without exposing itself to the less-than-desirable qualities of Silicon Valley startups or wrecking the best of its existing practices? And what does it mean to have a digital-ready culture?
The authors have been studying these questions for the past three years. Based on their findings, they have developed a framework to guide traditional companies in any industry. The process begins with understanding the four critical values of digital culture: impact, speed, openness, and autonomy. It then involves adopting or refining a set of digital-ready practices, grounded in these values, which will shape employee actions and organizational performance.
Contrary to much that’s been written, incorporating the best of digital culture into a legacy culture doesn’t mean sacrificing integrity, stability, employee morale, or a company’s heritage. And traditional companies aren’t the only ones that can benefit from this framework. The insights also apply to startups striving to mature.
Martin Reeves, Lars Faeste, Daniel Friedman, and Hen Lotan
As long-term growth rates trend downward in many economies, business leaders are turning to acquisitions to fuel growth. Turnarounds are becoming imperative as well. Companies face a seemingly endless stream of disruptions from new technology, emerging competitors, shifts in consumer behavior, regulatory changes, slowing economic growth, and other threats, any of which can hurt performance and require substantial and prompt changes in operations and strategy.
However, most M&A deals fail to create value, and only about one in four turnaround programs leads to long-term improvements in performance. Although M&A deals and turnarounds are individually hard to pull off, combining the two can be even more challenging.
Yet based on an analysis of roughly 1,400 M&A-based turnarounds between 2005 and 2018, the authors have identified six factors (all within the control of management) that can help acquiring companies improve their odds of success: high investment in R&D, a long-term orientation, a well-defined purpose, sufficient investment in transformation, ambitious synergy targets, and a willingness to act quickly. While these six factors can improve post-deal performance on their own, combining them is even more powerful. Indeed, there is a direct relationship between the number of success factors deployed and three-year TSR (total shareholder return) performance.
Turnaround acquisitions have a high failure rate. But those that succeed bring considerable rewards. This group of winners generates gains in both revenue growth and profit margins, as well as significantly better returns.
Julian Birkinshaw, James Manktelow, Vittorio D’Amato, Elena Tosca, and Francesca Macchi
One of the most profound recent changes in the workplace has been the increase in age diversity. Large organizations have employees from as many as five generations. Age diversity, like other forms of diversity, the authors say, can bring significant benefits to the organizations that embrace it, but it also creates challenges. Different generations have their own expectations and demands, and working relationships can become strained: It’s not always easy to report to someone who is significantly older or younger than you.
The authors surveyed more than 10,000 managers ages 21 to 70 across multiple industry sectors to learn about their preferred styles of working. By asking managers to identify the techniques and tools they saw as most important, the authors discovered significant differences. They found that management style varied more with age than other factors (such as position in the organization and gender). Younger managers (typically in their 20s and 30s) took a more self-centered approach and put a lot of stock in making good first impressions and asserting themselves. They preferred concrete management techniques (such as knowing how to run an effective meeting). Older managers (typically in their 50s and 60s) favored a more inclusive and collaborative approach, and relied on more intuitive and holistic techniques. The framework, the authors write, can help people understand how their management styles align with those of their peers or boss and make it easier for individuals to navigate their working relationships.