page image

CHAPTER III - Building Superminds

Employee Cost, Value, and Meaning: Building Superminds
Is it possible for an enterprise to create new value and meaning even as it reduces costs?

Achieving these disparate goals is possible but will depend, according to Tom Malone, Professor of Management at the MIT Sloan School of Management, on tapping into the power of “superminds.” Malone defines a supermind as a group of people acting together in ways that seem intelligent. Like individuals, but unlike computers, superminds exhibit what can be described as general intelligence, the ability to perform well across a variety of tasks.

Although the term may be unfamiliar, superminds are all around us. In fact, Malone believes that superminds run the world. For example, every company is a supermind. A company can be defined as a machine made of people, but also as “a mind made of people” who work together to carry out complex tasks. Think about how General Electric is able to build a jet engine, or how a movie studio produces a film, or how a shipping company can deliver a package anywhere in the world in a few days. All of these are tasks that are too large and complicated for any single person to accomplish on his or her own, but require the skills and the coordinated effort of scores of people.

In addition to companies, communities, democracies, markets and ecosystems are other kinds of superminds. Although they all consist of groups of people working together, they differ in terms of their structure and, particularly, their method of decision-making, a key aspect of collaboration:

  • Companies function through a hierarchical decision-making process, where people higher in the organization determine what people below them should do;
  • Communities (which may be geographic or communities of practice) typically make decisions through a process of informal consensus based on shared social norms; Democracies decide through voting, where the majority generally rules; while markets operate though a myriad of decisions between pairs of individuals (buyers and sellers) who agree to trade resources with one another;
  • Democracies decide through voting, where the majority generally rules; while markets operate though a myriad of decisions between pairs of individuals (buyers and sellers) who agree to trade resources with one another;
  • Ecosystems are yet another type of supermind, in which “decisions are made based on who has the most power and the greatest ability to survive and reproduce,” describes Malone. This kind of supermind activity can be found in military conflicts, but also in other types of conflicts between superminds, such as struggles for marketplace dominance between competing companies or in a scientific field where conflicting theories compete for acceptance.

Byron Auguste, Chief Executive Officer and Co-Founder of Opportunity@Work, noted that we are simultaneously members of multiple superminds, and that there are a variety of connections between different types of superminds: for example, companies function within and are influenced by communities, states, and markets. While corporations were historically answerable to states and communities that regulated their behavior, they are now primarily answerable to markets that determine their value.

Movements represent yet another type of supermind. Typically led by evangelists who help them to grow, movements tend to be more focused on a single goal than communities or other types of superminds that typically function as arenas in which their purposes are worked out. One useful way to think about movements (like gay rights or #MeToo) is as a sub-community that influences a larger community to change its values.

Not all superminds are equally effective. In earlier research, Malone and colleagues explored what determines the “collective intelligence” of a group.i It turned out that the individual IQs of the members of a group were less important to the group’s collective intelligence than three other factors: the average social intelligence of group members (their ability to “read” the emotional state of others, a factor that correlated closely to the percentage of women members in a group), the degree to which members participated about equally in group interactions, and the cognitive diversity of the group (the differences in thinking and perceiving styles of members).

Making Superminds Smarter
Although superminds have been around since the dawn of civilization (think about the pyramids or other wonders of the ancient world that required complex, coordinated effort over time), there is something distinctively new in recent times that has the power to make superminds smarter: the computer. Although computers have demonstrated the ability to function in surprisingly intelligent ways, Tom Malone at the MIT Sloan School of Management is skeptical that computer-based AI will achieve any real measure of “general intelligence” in the foreseeable future. But there are a number of ways in which groups can make use of computers to function more effectively… and more intelligently.

Among the activities that are vital to how groups function, there are a number that can be supported and enhanced by incorporating computers with people in what Malone describes as “cyber-human systems” that take advantage of the strengths of each. These activities include sensing the world (identifying important signals or spotting unseen patterns through mechanisms like the Internet of Things, neural nets or big data analytics); remembering the past (via historical records); creating options for action or deciding what actions to take (for example, via a system that generates and evaluates millions of possible strategies), and even learning from experience (by automating the process of experimentation and discovery).

Malone has also identified four distinct roles that computers can play to make a supermind smarter. In order of increasing sophistication, they are:

  • As tools that can either “increase the specialized intelligence of individuals,” as in the case of an accountant using a spreadsheet or facilitating more effective communication among members of a group. Connecting people has, in fact, been the most important contribution of computers to date and is likely to continue to be so for several more decades. Computers are now enabling people to be “hyperconnected,” linking them together in ways that were not previously possible.

    Applications such as email and texting are so pervasive that it is hard to imagine working (or living) without them. Newer tools, like Slack or Microsoft Teams, have been designed explicitly to support group work. But computers, along with computer networks, are enabling entirely new ways of collaborating. One good example is Wikipedia, the joint creation of a highly decentralized group of individuals who have compiled an unprecedented repository of human knowledge that is being continuously expanded and updated and is available at no cost to anyone on the Internet. With a few exceptions (see below), all of the content of Wikipedia has been created by computerlinked humans working independently but all following a relatively small set of rules and standards that ensure the coherence and reliability of the larger endeavor.
  • As assistants that can automate some of the routine tasks that can occupy much of the time of group members. Voice-based virtual assistants such as Apple’s Siri or Amazon’s Alexa can take on tasks and execute them without continuous supervision from a user. When customers call a bank or an airline, or almost any institution to seek support, they often begin by interacting with a voice-response system that gathers information about the customer’s account and particular concern before passing them on to a human. While the tasks that these AI-based systems perform today are relatively simple, future systems will be capable of carrying out much more complex tasks.
  • As peers that take on some of the functions of a fellow team member. For example, Wikipedia now employs bots that have the ability to make certain kinds of edits autonomously, essentially carrying out tasks that previously would have required human attention. Thus, one bot is able to undo edits that are likely to be instances of malicious vandalism (e.g., adding obscene comments), while another is able to check for content that may have been plagiarized.

    Another example of a tool to connect people in a new way is InnoCentive, an online “crowdsourcing” platform that enables organizations to seek help in dealing with difficult problems by posting challenges online and offering rewards for the best solutions. Interestingly, the majority of prizes have been won by individuals or groups who had not previously won any awards and who often lacked the kinds of credentials or experience that “experts” in the various fields would be expected to have. By widening the pool of potential problem solvers available to an organization, InnoCentive has also effectively widened the scope of potential solutions. Autodesk’s Generative Design software, described above, serves more like computer-as-peer, as it is able to independently generate new design solutions.
  • Finally, as managers that, like humans, assign tasks and coordinate and evaluate the work of people. In his book, Malone describes CrowdForge, an experimental computer-based system developed at Carnegie Mellon University that breaks down complex tasks into simpler micro-tasks, then automates the process of assigning these tasks and assembling the results into a finished product. The system has been used to create factual articles by dividing up a writing assignment among a group of individuals who all work online. It allocates to different individuals specific tasks that include producing an outline for an article, doing research, writing specific sections of the content, which the computer system then compiles into a complete article. Interestingly, readers judged the results of this automated management system as superior to similar articles written entirely by a single author.ii

Challenges for Superminds
As computers get smarter and more capable of taking on roles of greater responsibility, as they evolve from serving just as tools or assistants to acting as peers or managers, the issue of trust will inevitably arise. While humans have developed many strategies and systems for building trust in each other, what happens when machines take on a decision-making role? To what extent is it appropriate to trust their decisions? How can we determine how much to trust them? In an article in the MIT Technology Review titled “The Dark Secret at the Heart of AI,” author Will Knight explains that “no one really knows how the most advanced algorithms do what they do.”

The first generation of AI systems were built from a series of rules that specified how a decision would be made and whose logic could therefore be analyzed. But because of the large effort involved with constructing step-by-step decision-making processes, such systems proved to be of limited value. AI really came into its own with the development of techniques like deep learning that make use of very large amounts of data to develop the ability to find connections in ways that exceed human abilities and perhaps humans’ ability to understand how they do so. The innate complexity of deep learning systems makes it difficult to determine how a decision is made.

The problem is that these systems may be very powerful, but they are not infallible. For example, it may be true that self-driving vehicles are safer than human drivers, but AI-powered vehicles have already been involved in several fatal accidents. If the technology is to become fully legal and widely adopted, it will be necessary to be able to understand what went wrong and how it can be fixed. Knight describes the research of computer scientists like Carlos Guestrin at the University of Washington who are working to give AI systems the ability to automatically provide a rationale for their output in an effort to achieve “explainable AI.” Still, Guestrin acknowledges that “we’re a long way from having truly interpretable AI.”

Rather than working to build AI systems that solve problems autonomously, it may make more sense to build systems that enable humans and machines to work together to tackle the most critical and complex tasks. John Seely Brown pointed to free style chess as a good example of such a partnership. Instead of pitting computer programs against humans, freestyle chess allows individual players or groups of players to consult any expert they wish, including making use of computer-based chess programs. By combining the brute force of computer analysis with human intuition, freestyle players have shown themselves capable of levels of performance that can equal that of the world’s strongest players: “kids with computers can beat the best chess programs and grandmasters.”

In an article in Wired, Byron Auguste at Opportunity@Work argues that “the biggest technology opportunities have always augmented the work of humans rather than replaced it altogether” and predicts that ‘“augtech” will emerge as an important, widely recognized category of software akin to fintech or biotech. The biggest barriers to its emergence are persistent institutional biases—including “tax codes, accounting standards, executive compensation systems, dysfunctional training systems [and] exclusionary hiring practices”—that favor automation over augmentation.

Finding Meaning
Another critical dimension that remains unaddressed as people and machines learn to work together is that of meaning. How is meaning created and is there any role for computers in this process? According to Tom Malone, meaning is created when humans work toward a purpose that is larger than themselves. If “being smart” is measured by the ability to achieve goals, then wisdom is a matter of achieving goals that are worthwhile. Deciding what is good and worthwhile is a distinctly human activity, and one that would seem to be beyond the ken of computers. We may be in the age of artificial intelligence, but the prospect of artificial wisdom still seems distant.

Max Mancini, Executive Vice President of Automation Anywhere, proposed that while people are responsible for identifying a common purpose for themselves, automation can free up a group’s cognitive capability to enhance the pursuit of that purpose or even, perhaps, to go beyond it to seek a new purpose.

Inevitably, leaders are responsible for setting goals and managing the activities of their organizations. They are also responsible for creating a corporate culture that will determine whether workers feel empowered to take the initiative or are content (or resigned) to simply fill an assigned role.

The potential of superminds and the continued evolution of technology to enhance that potential represent a new management challenge. Robin Jones, U.S. Workforce Transformation Leader for Deloitte, suggested that leaders need to be educated on the variety of supermind models and the potential they offer. Seeing their organizations as a collection of superminds, each with its own capabilities, provides a new way of thinking about their role. The default “centralized hierarchical mindset” that typifies most corporate leadership might not be the best way to understand the challenge of managing a collection of superminds and, especially, the potential for building super-intelligent systems that blend human and machine intelligence.

How to best leverage the power of superminds is dependent on the context in which they operate. Lisa Chang, Chief People Officer of The Coca-Cola Company, noted that while superminds exist in all organizations, different organizations will prioritize different types. For example, the management structure in sports organizations is very hierarchical, while empowering all employees is more important in start-ups. Coca-Cola is a 134-year-old global enterprise with over 60,000 employees in The Coca-Cola Company, and approximately 700,000 people in the ecosystem including its bottlers. While the original founders of the company may be long gone the company culture is still quite strong. With so many employees globally the challenge now is communicating and embedding one singular purpose that unifies and inspires people, a task for which technology is likely to be of little use.

Shaun Smith, Senior Vice President and Chief Human Resources Officer at New York-Presbyterian Hospital, added that employees are more motivated and more committed when they feel that they are part of the decision-making process. His organization decided that it needed to become more agile, which required a substantial culture shift. Even as it was embarking on a major transformation, the organization remained true to its core values to guide decision-making. Making sure that everyone was included in the conversation about how the change would happen helped keep everyone aligned around the same goals.

One of the most useful things leaders can do is to simply listen to their own teams, then reflect on what they have heard. John Hagel at Deloitte suggested that one of the best ways to empower and inspire superminds is to “frame powerful questions” for them to take on. Unfortunately, asking questions is generally viewed as a weakness in a leader and few CEOs are comfortable in asking for help. Yet, it is unrealistic to expect that any leader will have all the answers. Rather than trying to go at it alone, it is better to create a culture that can generate options for action.

Preparing for Work in the Digital World
New ways of working require new kinds of education. The Iovine and Young Academy was established at the University of Southern California in 2013 to provide a “degree in disruption” that would equip graduates with the skills needed to succeed in the new world of work. Rather than being conceived as an interdisciplinary program, the Academy was specifically intended to tap into “the power of undisciplined thinking.” Specifically, the school’s curriculum was designed to “nurture critical thinking and unbridled creativity at the intersection of arts and design, engineering and computer science, business venture management and communication.” The Academy initially offered a four-year BS degree and has added a master’s degree in Integrated Design and Technology. It is also planning to add a minor in health innovation and a master’s degree in product innovation. In 2018, it became USC’s 20th professional school.

The Academy’s first class of 25 students enrolled in 2014 and graduated in 2018. Although it is difficult to generalize about a highly diverse group of students, Erica Muhl, the Academy’s founding dean, described one member of the class. To be admitted, each applicant is required to submit a 60-second pitch video that poses a problem and suggests a possible solution. Joseph May chose to develop a new means of supporting people with hearing problems, a condition that he himself suffered from. His proposed solution was to create an augmented reality system that would turn sounds into visual images.

During his freshman year at USC, May wanted to take a graduate course in optics. The professor who taught the course agreed to admit him with the provision that he demonstrate that he was capable of keeping up with what was being taught. He had taken all of the math he needed for the course before he started school, and he ended up completing the course with a grade of B+. By his sophomore year, May had created a prototype of his augmented reality headset, which went on to win several design prizes. By the time he graduated, May had founded a company called Mira that raised $2.5 million in venture capital funding. The company is now selling a smartphone-powered headset that is provided free with purchase of the company’s AR software package.

The Academy’s curriculum is built around an ethos of making and aims to inculcate an entrepreneurial “fail/learn/repeat” mindset in its students. In addition to teaching specific skills, the program is dedicated to building capabilities such as creativity and collaboration. The Academy is built on the assumption that the half-life of data science or other technical degrees is five years, so the program is designed to promote lifelong learning, which includes ensuring that the students are adept at adopting new technologies.

An important part of the program is experiential and service learning, which involves placing students in both businesses and nonprofit organizations. The goal is to produce strong problem solvers across a variety of domains. Graduates from the Academy’s first two cohorts have gone into a variety of careers, including working in tech, product design, content creation, analytics, and start-ups.

Muhl concluded by asserting that while the Academy’s approach to education is unusual if not unique, all higher education institutions need to consider how to accommodate students who arrive very familiar with technology and eager to work creatively and collaboratively with others.

The Arts and the Sciences
Several Roundtable participants raised the question of whether this kind of tech and innovation-focused educational program could or should replace a conventional liberal arts education. Margaret Levi at Stanford University worried that these students could end up ignorant about history, literature, and politics. Even at Stanford, she encounters faculty members with strong technical backgrounds but with little knowledge of the history of issues that they find themselves dealing with.

Tom Malone responded by suggesting that creating problem solvers, creative thinkers and lifelong learners is, in fact, the essential goal of a liberal arts education. Still, employers seem to value specific technical skills over those with broad liberal educations, which has narrowed students’ views of the career ladders they need to climb. Mickey McManus at Autodesk proposed that liberal arts may have failed “by having been too successful.” Liberal arts were the foundation for enlightenment culture but became dispensable when its people began to doubt its value in the marketplace.

But it may be premature to give up on the value of traditional academic disciplines, which still retain a lot of clout. There do not seem to be any academic journals for “undisciplined professors.” It is not clear how an academic culture that still values professional publication will measure their effectiveness. And there is still value in “going deep” in one specific discipline. Carnegie Mellon University’s program in game design, which is celebrating its 20th anniversary, serves as an example of an academic program that has successfully combined traditional studies with nontraditional subject matters.

Erica Muhl noted that in order to graduate, Academy students are required to take ten to twelve regular university courses in addition to participating in their own program. And the Academy’s curriculum is not exclusively focused on technology and business but includes an extensive process of self-analysis focused on exploring the implications of their projects.

John Seely Brown at Deloitte proposed that the question of the sciences versus the humanities should not be seen in terms of “either-or” but rather as a matter of both/and. To deal with the kinds of problems that workers are increasingly facing, they must transcend the distinction between the two fields and combine the skills and knowledge of both. In Design Unbound,iii Brown and his collaborator, Ann Pendleton-Jullian, describe a new set of practices that are required to deal with the complex problems that are increasingly common in a “whitewater world” that is rapidly changing, hyperconnected, and radically contingent.

Traditionally, when making long-range plans and strategies, individual problems could be identified and analyzed more or less in isolation and the solutions could be expected to remain valid for a significant period of time. But as the rate of change in our world has accelerated and connections have multiplied, the skills that are needed to survive and flourish are more like those of a whitewater kayaker who must be constantly attuned to the ever-shifting conditions that surround him and that demand responses that are holistic and instantaneous.

Moreover, we are now living in a hyperconnected “age of entanglement” in which everything is linked to everything else. We are no longer just dealing with complicated problems but with complex problems that morph as soon as we start to solve them: “you cannot learn about a problem without trying solutions, but every solution you try has lasting unintended consequences,” wrote Brown. In the words of philosopher Karl Popper, today’s most pressing problems are “clouds” not “clocks”:

To understand a clock, you can take it apart, look at its individual pieces, study the pieces.… A cloud you can’t take apart. A cloud is fundamentally a dynamic system. A cloud you can only study as a whole.

To work effectively in this world, Brown and Pendleton-Jullian make the case for development of a “pragmatic imagination” that is not limited by the linear logic of the sciences even as it grapples with the practical constraints of real-world problem solving. They describe this new approach as a “fusion” of the disparate fields that incorporates elements of both but has its own characteristics, just as fusing two different metals can produce a powerful new alloy with its own distinctive properties that differ significantly from either component.

Alternative Paths to Employment
One hopeful sign of change is that some companies are taking a new, broader view of the qualifications for a job. Sarah Gretczko at Mastercard noted that a growing number of employers are willing to hire students without four-year degrees if they have interesting nontraditional credentials. But more needs to be done: a study published in March 2020 by Opportunity@Work, Reach for the STARS, reports on research that finds that there is a large and underappreciated talent pool of 71 million Americans who are “Skilled Through Alternative Routes” (STARs). These are workers who are currently in low wage jobs but have “suitable skills sets to succeed in work that is more highly valued and therefore better paid than they work they do now.”

The main barrier that these workers face is that although they have high school diplomas, they lack a BA, which many employers require for higher-level jobs. The report found that there are five million “Shining STARs” who have managed to get higher-paying jobs despite their lack of a college degree; 30 million “Rising STARS” who have the skills now that should qualify them or a job in a higher wage category; and 36 million “Forming STARS” who have some skills needed for a better paying job but are not currently well situated to get a better job, a group that is “especially susceptible to the impact of automation.” The report notes that it is ironic that in a time when many companies see themselves engaged in a “war for talent” to keep themselves competitive, they are overlooking the vast potential of this pool of workers.

An Adaptable Workforce?
But what will happen to jobs in the future as AI becomes more capable and takes on a broader spectrum of tasks? Hans Peter Brondmo, Robot Whisperer at Google X, asked whether, as more and more jobs with standardized, repetitive components become automated, will we be creating a new “useless class” of workers? While the elite students in programs like the Academy will flourish in such a world, what will happen to the mass of workers with fewer skills? A recent research study provides a reason to be optimistic about the ability of workers to adapt to the changes that are coming in the wake of automation and other new technologies. In a 2019 article in the Harvard Business Review, a group of researchers led by Joseph B. Fuller, co-chair of the Project on Managing the Future of Work at Harvard Business School, reported on the results of a large survey on the future of work conducted in the U.S. and seven other developed countries in Asia, Europe, and South America.

The survey, which included 6,500 business leaders and 11,000 workers, found that “the two groups perceived the future in significantly different ways.” The business leaders who participated in the survey “felt anxious as they struggled to marshal and mobilize the workforce of tomorrow” and were worried about how they “can find and hire employees who have the skills their companies need and about what they should do with people whose skills have become obsolete.” By contrast, “the workers didn’t share that sense of anxiety. Instead, they focused on the opportunities and benefits that the future holds for them.” The surprising bottom line: workers were “much more eager to embrace change and learn new skills than their employers gave them credit for.” The authors use these results to argue that managements need to recognize workers’ willingness to change and to collaborate with them in reinventing their jobs.

Redesigning Work, Adding Value
Much like 30 years ago when John Clarkeson wrote about the need for new ways of working, most jobs today still consist of tightly specified routine tasks, precisely the kinds of jobs that are most susceptible to being automated. Having workers spend time with unexpected problems is often seen as a detriment to efficiency, a sort of necessary evil that must be accommodated but should be kept to a minimum. But creating solutions to unanticipated problems can provide valuable opportunities to create new value. And, as Fuller and his colleagues found, many workers are very open to taking on new challenges in their day-to-day jobs.

To illustrate the power of redesigning work, John Hagel described the experience of Quest Diagnostics, the country’s largest provider of clinical test services to health care companies, as it tried to deal with serious problems at the company’s call centers. In 2013, the company had reduced the number of its call centers from 20 regional centers to just two national centers located in Kansas and Florida. The centers were organized in a traditional hierarchical structure in which managers closely supervised the front-line workers who were regularly evaluated on the number of calls they handled per day and the average time spent in answering customer questions.

The two centers, which handled some 55,000 calls a day from doctors, hospitals, and patients, were experiencing high turnover and absentee levels, and low productivity due to the inexperience of customer service reps. Callers were getting frustrated by their inability to get their questions answered or get the results of lab tests promptly. As a result, the company was losing customers to competitors who were providing better service.

Three years ago, the company launched an effort to address these problems by rethinking and redefining the work of call center representatives. Front line workers were organized into small pods of 10 to 15 people who were encouraged to work together to address customers’ problems. Pod members met weekly to discuss their work experiences and identify problems that needed attention. Quest also committed to harnessing technology to improve call center performance. The company undertook an effort to identify routine tasks performed by customer service reps that could be automated, but it involved the reps themselves in the process and made a commitment that no one would be laid off because of automation. A key result of the process was a major upgrade of the company’s website that made it possible for customers to get many of their questions answered online.

As the day-to-day demands on reps lessened, they were able to spend more time on finding new ways to provide value. They invested their time in developing a deeper understanding of the company’s products and how Quest could better serve its customers. For example, the company was able to focus on the management of chronic care and on being more proactive in preventing health problems. These changes resulted in a 17 percent decrease in overall call volume, even as the total number of customers increased. And annual worker turnover at the call centers fell from 34 percent to 17 percent. The project was so successful that Quest is now expanding it to other parts of the company.

The most important lesson from this project, Hagel suggested, is that it focused not at how to use technology to automate operations or eliminate jobs but rather on strategies to make existing workers more productive and provide customers with greater value. Quest discovered that there was actually a hunger among its workers to work in ways that are more satisfying and meaningful.

The project also illustrates the value of engaging front line workers, who often have the best view of customer needs, in the process of deciding how to deploy technology in operations. And this story demonstrates that the focus of attention should be on small groups rather than on individuals. Workers are able to learn more and improve faster when they are organized into teams of five to 15 people who can share learnings and develop a strong sense of trust in each other (from Tom Malone’s perspective, each pod could be seen as a supermind, and the result of the project was to increase the collective intelligence of each one.) Finally, the job redesign was successful because the company was able to identify a set of “metrics that matter” to guide its effort and provide meaningful feedback on what worked.

Strategies for the New Workplace: The Importance of Meaning
One of the central assumptions of the Aspen Roundtable has been that providing opportunities for meaningful work is good both for employers and employees. But, in addition to stories about the desire for meaning in work, is there any hard evidence that this is actually the case? And, if so, does it matter?

A study done by BetterUp in 2017 attempted to answer these questions. The study “Meaning and Purpose at Work” was based on a survey of 2,285 professional workers in the U.S. and explored how important meaningful work was to them, how meaningful they judged their current jobs to be, and what factors contributed to making a job meaningful.

Alexi Robichaux of BetterUp, presented the key findings from the survey that provide strong support for the importance of meaning to workers:

  • Employees whose work feels meaningful work longer hours and are absent less. They also are less likely to leave their jobs and are more likely to receive raises and promotions.
  • On average, workers say that their jobs are about half as meaningful as they would like them to be. Just one in 20 respondents said that their current job “is the most meaningful work they could imagine having.”
  • Meaning and social support at work are closely related.
  • More than nine out of ten workers would be willing to trade a percentage of their lifetime earnings for greater meaning at work.

The survey also documented the fact that in addition to making workers happier, providing meaningful work yields tangible benefits for employers. For every 10,000 workers who have meaningful jobs, a company can expect to enjoy $82 million in annual productivity gains, 19,500 fewer days of paid leave per year, and savings of $55 million in reduced annual manager turnover costs.

Providing meaningful work seems to offer many benefits to both employees and employers. But what, exactly, makes a job meaningful? The survey identified three key dimensions of work that are vital to a sense of meaning: providing for personal and professional growth, a shared sense of purpose with fellow employees, and an opportunity to be in service to others. Other important factors that contribute to meaning are a sense of balance between personal and corporate priorities, a chance to be inspired by work, and having a corporate culture that supports honesty (see Figure 2).

FIGURE 2: Contributors to Meaning at Work

Source: BetterUp, Meaning and Purpose at Work. See:
  1. Allow for flexibility at worke—e.g., give employees the ability to set their own schedules or to work remotely (at least one-third of employees prefer working remotely, at least part-time).
  2. Provide opportunities for self-care—time to exercise, rest, or find quiet time away from disturbances. Particularly valuable is time for “self-reflection;” those who do this most regularly are more likely to be promoted or get a pay raise.
  3. Strive for alignment between individual and corporate values—for example, managers can help foster a sense of meaning by tying the goals of a specific project to those of the larger organization.
  4. Guard against “toxicity” in the workplace—by strongly combatting bullying, discrimination or harassment of any kind. Meaningful work and a positive, supportive culture build on each other.

i Thomas W. Malone, Superminds: The Surprising Power of Computers and People Thinking Together (New York: Little, Brown and Company, 2018).
ii Thomas W. Malone, op. cit., pages 55-57.
iii Ann M. Pendleton-Jullian and John Seely Brown, Design Unbound: Designing for Emergence in a White Water World ( Cambridge, MA: The MIT Press, 2018).
Title Goes Here
Close [X]