The editors of MIT Sloan Management Review are pleased to announce the winner of this year’s Richard Beckhard Memorial Prize, awarded annually to the most outstanding MIT SMR article on planned change and organizational development. The 2018 award goes to “The Corporate Implications of Longer Lives,” by Lynda Gratton and Andrew Scott, both professors at London Business School.

We understand a lot about how to develop the “hard skills” of analysis, decision-making, and analytical judgment, but we know a great deal less about the genesis of “soft skills” like empathy, context sensing, collaboration, and creative thinking, which are becoming increasingly valuable in the workplace. Understanding the obstacles to developing these soft skills and then addressing those barriers is crucial for our schools, homes, and workplaces.

None of us know how our technological future will unfold. But whether there will be a net increase or decrease in jobs overall, it’s clear that these will be different jobs, requiring different skill sets. We need to act now to enable current employers and employees to gain the skills they are going to need in the brave world of AI technology.

Some leaders have failed to realize that the daily lives of those who work in their organizations will inevitably be transformed over the coming decades. But it’s the responsibility of leaders to create clarity about the future of work. That means being engaged with creating a narrative about the future of jobs, actively championing the learning agenda, and role modeling work flexibility — for instance, by taking paternity leave or working from home.

David Harcourt of Yum! Brands Inc. shares his research and experimentation with HR analytics and shows how to align strategic talent management goals with HR analytics projects and activities.

Some women who feel like they won’t “fit” a stereotypical job description will talk themselves out of wanting it. This process of negatively evaluating promotional opportunities is due to a process called “job crafting.” As a result, managers who wish to employ female executives at the highest levels of their organizations should be especially careful of the signals they might be communicating to potential applicants.

The “intangible assets” people bring to their jobs are valuable — but challenging to quantify. Understanding the complexities of assets such as a person’s capacity to continue to learn new skills and ability to manage the stress of work and home life can help organizations get a better handle on alternate ways of sustaining employees. Understanding the notion of intangible assets can also help individuals think more concretely about how they allocate their time and energy.

Current trends in both human longevity and technological innovation raise the possibility of people living until 100 and working until they are 80. It’s clear that much will have to change — both in how people understand and anticipate the evolving nature of work, and how they then respond. Providing access to lifelong learning demands a complex system involving stakeholders in education, government, and the corporate world.

Many executives believe they are good at identifying leadership talent. However, when asked how they make their decisions, they often cite intuition or “gut” instincts. Social science research, on the other hand, suggests that individuals are often prone to cognitive biases in such decisions. Rather than just relying on the subjective opinions of executives, some companies are using assessment tools to identify high-potential talent.

People are living longer and working longer — but few organizations have come to grips with the opportunities and challenges that greater longevity brings. Across the world, people are becoming more conscious of their lengthening working lives — but frustrated by their working context. The authors’ research suggests that while people know they will have to restructure their lives and careers, corporations are unprepared.

While there’s a boom in using analytics for HR decisions, predictive analytics hasn’t yet made substantial inroads in the place of its birth: academia. Tenure decisions for the scholars of computer science, economics, and statistics — the pioneers of quantitative metrics and predictive analytics — are often insulated from these tools. Now research we conducted with Dimitris Bertsimas and Shachar Reichman finds that data-driven models can significantly improve tenure selections and predict future research success.

Companies have yet to apply analytics to human resources — but that’s about to change. And lessons learned in applying analytics to customer-focused areas can help avoid mistakes in strategic workforce decisions.

Competition for digitally savvy talent has never been higher, but companies’ methods for acquiring and keeping the skilled employees they need are outmoded. Whether they want to develop capabilities in employees or tap on-demand talent markets — or some mix of both — human resources directors need to experiment with new talent management models.

Companies aiming to be competitive in the long term do not see safety and productivity as trade-offs. Research drawn from multiple studies conducted with the support of companies, unions, and regulators in the United States and Canada finds no evidence that protecting the workforce harms competitiveness. “Once companies understand that safety is not the enemy of efficiency,” the authors write, “they can begin to build organizational safety capabilities.”

In MIT Sloan Management Review‘s 2015 Digital Business Report, we found that lack of digital maturity has profound implications for talent acquisition and retention. The vast majority of employees (80%) say they prefer to work for digitally mature companies — which means that if your company isn’t there yet, it may soon cost you valuable talent.

To manage a first-rate data science or quant group, leaders need to build an engaging environment, get the team the resources it needs and balance being involved while also staying out of the way. In banking, for example, division managers generally don’t review loan applications. But in analytics, the most successful leaders engage regularly in hands-on research and continue to publish regularly even as they move up the executive ladder. By staying active in line research, analytics managers are able to hone their abilities to judge how difficult projects are and how long they will take.

The process of managing a data science research effort “can seem quite messy,” writes MIT Sloan’s Roger M. Stein. That can be “an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.” While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talent are structured and managed.

Intermountain Healthcare is leading the way in data driven healthcare. In an example from Intermountain’s own operating rooms, the use of data to measure the impact of standardized surgeon attire on infection rates resulted in a significant drop in those rates. The infection control scenario is just one result from decades of work at Intermountain to build a data culture. Over the years, clinicians have learned to work together on a concerted effort to bring data based insights to clinicians and managers.

A key finding from the 2015 digital business research report by MIT Sloan Management Review and Deloitte indicates that strategy, not technology, drives digital transformation. An infographic illustrates some of the primary features of a digitally transformed business, as highlighted in the report. The infographic also illustrates the importance of digital maturity to employees across all age groups and industries and the keys to making digital transformation happen.

There are clear signs that the movement to democratize data is making real progress. Barriers such as infrastructure, culture, tools, and governance that once kept data access limited are quickly eroding. But access to data isn’t enough: Data democratization also requires knowing how to work with data and understand data analysis tools and techniques. Without these capabilities, the data democracy is only an illusion — and most people are still unable to participate fully.