Equipping the Sustainability Insurgency.04-21

Equipping the Sustainability Insurgency.

An environmentalist and an MBA team up to develop a network of sustainability champions.
Sustainability World Wildlife Foundation.

Image Credit : Shyam's Imagination Library


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As this series has documented, Sustainability Insurgents are professional insiders driven by a higher purpose. While loyally serving their employers, they also pursue a higher ideal: aligning their organizations with a global vision of a peaceful, prosperous, and sustainable world. This article explores how two insurgents, working for dramatically different organizations, partnered to save the planet — and in doing so spread the sustainability insurgency to thousands of individuals and dozens of organizations.

The unlikely duo is Frances Edmonds, an environmental scientist, and Adrienne Lo, an MBA. They hail from the conservation non-profit WWF and the tech leader HP Canada. But, in a twist increasingly common to the insurgency, the environmentalist works for the business, and the MBA works for the non-profit.

What brought this odd couple together? A shared belief that saving the planet depends on engaging and rallying as many people as possible to action. Their target audience: Nascent Sustainability Insurgents, whom they call “champions,” in companies around the globe. Champions are employees who see opportunities for positive change, but aren’t tasked by their company’s official CSR function to act.

Their model of change is peer-to-peer engagement, which they see as the most effective way to support champions and encourage them to take actions that can foster new sustainable norms at work.

Edmonds and Lo knew from experience that champions operate on a volunteer basis, stepping up to the challenge largely because it makes their job more meaningful and personally fulfilling. This intrinsic motivation is vital because insurgents swim against the organizational current. They get told “no” often.

It’s not that the companies don’t want to improve their sustainability performance: No executive gets up in the morning and asks, “How can I destroy the planet today?” But, according to Lo, established corporate processes, systems, and policies are difficult to change — and often, no single person or department has the power to act alone. Corralling and convincing all the needed people requires unique skills and organizational knowledge that are not readily available, nor taught in traditional management programs.

This is where our duo steps in. They wanted a way to provide the know-how and tools champions need to advance the sustainability insurgency in their organizations. But more important, they knew that a supportive peer network of like-minded insurgents would empower champions and give them the courage to persist in the face of inevitable resistance.

The result of their vision is a web-enabled system launched by WWF Canada called Living Planet @ Work, a free, one-stop-shop that provides insurgents with the resources, inspiration, and support they need to become sustainability champions in their company. The website also works to overcome the dismal rate of charitable giving to environmental causes, which is less than 5% in Canada, by encouraging users to support important WWF campaigns like Arctic conservation (see the video).

Living Planet @ Work’s online tools include how-to guides for reducing the workplace environmental footprint and advice on engaging colleagues around sustainability. But beyond information, the site fosters an engaged, peer-to-peer network where members support each other in navigating the organizational obstacle course. As Frances puts it, “There is nothing more motivating than knowing that you’re not alone.”

Since Living Planet @ Work launched 5 years ago, more than 1,200 champions have joined. The website documents examples of insurgents making their offices more energy efficient, reducing the landfill waste, organizing e-waste drives, and more. While these tangible outcomes are important, it is the growing organizational capacity to act on sustainability that ultimately counts. With each successful initiative, champions demonstrate to themselves, their peers, and company executives how corporate social responsibility can have an impact on important managerial issues like employee engagement, operational efficiency, and corporate purpose.

Living Planet @ Work website captures the vision this way: “We know that meaningful change comes from within an organization, and the more help you can get to build support across teams and employees, the better.” This is where change begins, but the end game of these bottom-up victories must be an executive embrace of sustainability and a commitment to embedding corporate responsibility into every function of the organization. Luckily, with champions like these populating the company, executives will find an established network ready and willing to support a coherent and compelling sustainability strategy.

How social tools can reshape the organization 04-20

How social tools can reshape the organization.

Not all social technologies bring equal benefits. Survey respondents say the most valuable tools make it easier for employees to collaborate—and could even transform the way organizations work

While social technologies have become ubiquitous in business, not all tools—or the benefits companies  see from their use—are created equal. Indeed, results from McKinsey’s latest survey on social tools suggest that a new generation of tools is enabling employees to collaborate in improved and innovative ways.1 Respondents say improved internal communication is the feature of social tools that has most benefited their businesses. They also expect that, in the coming years, enabling better communication will be  one of the ways these tools could bring about fundamental changes at their organizations.

The results also suggest that social tools play a critical role in how technology overall can encourage organizational change. We asked executives about their companies’ use of social tools, digital technologies, and big data in 18 different business processes; the clear consensus is that using social begets better use  of these other technologies. When organizations digitize a process’s work flow (which happens most often

with customer-facing processes), respondents say that using social tools in that same process has  enabled their companies’ overall digital efforts. What’s more, some executives report greater benefits— decreased costs and increased productivity, for example—if they digitize and use social tools in a  given process. Several benefits are greater still if the company uses data collected from social interactions among employees and with customers.

A new generation of social tools Executives report that the business use of social tools is nearly universal. Ninety-three percent  of respondents say their companies use at least one social technology, continuing an upward trend  (82 percent said so in the previous two surveys).2 And most respondents say employees at their  companies use at least one tool on mobile devices. In addition, 74 percent say social tools are at least somewhat integrated into employees’ work—up from 67 percent in the past two years.

Although social technologies are more and more commonplace, the results suggest that not all tools are created equal. Specifically, those that can enable collaboration among employees are the most valued— increasingly important, since 80 percent of executives, up from 69 percent in 2014, say their companies use these tools for internal purposes.

When asked about the most beneficial features of the social tools their companies use, respondents most often cite real-time interactions, the ability to collaborate with specific groups, and cross-platform availability (Exhibit 1). What’s more, respondents believe the same three features will most improve how people work at their organizations in the future.


View Exhibits 1to 6

Just How Smart Are Smart Machines? 04-20

Just How Smart Are Smart Machines? 


The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities.

If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build?

To help, we have developed a simple framework that plots cognitive technologies along two dimensions. (See “What Today’s Cognitive Technologies Can — and Can’t — Do.”) First, it recognizes that these tools differ according to how autonomously they can apply their intelligence. On the low end, they simply respond to human queries and instructions; at the (still theoretical) high end, they formulate their own objectives. Second, it reflects the type of tasks smart machines are being used to perform, moving from conventional numerical analysis to performance of digital and physical tasks in the real world. The breadth of inputs and data types in real-world tasks makes them more complex for machines to accomplish.

By putting those two dimensions together, we create a matrix into which we can place all of the multitudinous technologies known as “smart machines.” More important, this helps to clarify today’s limits to machine intelligence and the challenges technology innovators are working to overcome next. Depending on the type of task a manager is targeting for redesigned performance, this framework reveals the various extents to which it might be performed autonomously and by what kinds of machines.

What Today’s Cognitive Technologies Can — and Can’t — Do

Mapping cognitive technologies by how autotnomously they work and the tasks they perform shows the current state of smart machines — and anticipates how future technologies might unfold.

Four Levels of Intelligence

Clearly, the level of intelligence of smart machines is increasing. The general trend is toward greater autonomy in decision making — from machines that require a highly structured data and decision context to those capable of deciphering a more complex context.

Support for Humans

For decades, the prevailing assumption has been that cognitive technologies would provide insight to human decision makers — what used to be known as “decision support.” Even with IBM Corp.’s Watson and many of today’s other cognitive systems, most people assume that the machine will offer a recommended decision or course of action but that a human will make the final decision.

Repetitive Task Automation

It is a relatively small step to go from having machines support humans to having the machines make decisions, particularly in structured contexts. Automated decision making has been gaining ground in recent years in several domains, such as insurance underwriting and financial trading; it typically relies on a fixed set of rules or algorithms, so performance doesn’t improve without human intervention. Typically, people monitor system performance and fine-tune the algorithms.

Context Awareness and Learning

Sophisticated cognitive technologies today have some degree of real-time contextual awareness. As data flow more continuously and voluminously, we need technologies that can help us make sense of the data in real time — detecting anomalies, noticing patterns, and anticipating what will happen next. Relevant information might include location, time, and/or a user’s identity, which might be used to make recommendations (for example, the best route to work based on the time of day, current traffic levels, and the driver’s preference for highways versus back roads).

One of the hallmarks of today’s cognitive computing is its ability to learn and improve performance. Much of the learning takes place through continuous analysis of real-time data, user feedback, and new content from text-based articles. In settings where results are measurable, learning-oriented systems will ultimately deliver benefits in the form of better stock trading decisions, more accurate driving time predictions, and more precise medical diagnoses.

Self-Awareness

So far, machines with self-awareness and the ability to form independent objectives reside only in the realm of fiction. With substantial self-awareness, computers may eventually gain the ability to work beyond human levels of intelligence across multiple contexts, but even the most optimistic experts say that general intelligence in machines is three to four decades away.

Four Cognitive Task Types

A straightforward way to sort out tasks performed by machines is according to whether they process only numbers, text, or images — the building blocks of cognition — or whether they know enough to take informed actions in the digital or physical world.

Analyzing Numbers

The root of all cognitive technologies is computing machines’ superior performance at analyzing numbers in structured formats (typically, rows and columns). Classically, this numerical analysis was applied purely in support of human decision makers. People continued to perform the front-end cognitive tasks of creating hypotheses and framing problems, as well as the back-end interpretation of the numbers’ implications for decisions. Even as analysts added more visual analytics displays and more predictive analytics in the past decade, people still did the interpretation.

Today, companies are increasingly embedding analytics into operational systems and processes to make repetitive automated decisions, which enables dramatic increases in both speed and scale. And whereas it used to take a human analyst to develop embedded models, “machine learning” methods can produce models in an automated or semiautomated fashion.

Analyzing Words and Images

A key aspect of human cognition is the ability to read words and images and to determine their meaning and significance. But today, a wide variety of technological tools, such as machine learning, natural language processing, neural networks, and deep learning, can classify, interpret, and generate words. Some of them can also analyze and identify images.

The earliest intelligent applications involving words and images involved text, image, and speech recognition to allow humans to communicate with computers. Today, of course, smartphones “understand” human speech and text and can recognize images. These capabilities are hardly perfect, but they are widely used in many applications.

When words and images are analyzed on a large scale, this comprises a different category of capability. One such application involves translating large volumes of text across languages. Another is to answer questions as a human would. A third is to make sense of language in a way that can either summarize it or generate new passages.

IBM Watson was the first tool capable of ingesting, analyzing, and “understanding” text well enough to respond to detailed questions. However, it doesn’t deal with structured numerical data, nor can it understand relationships between variables or make predictions. It’s also not well suited for applying rules or analyzing options on decision trees. However, IBM is rapidly adding new capabilities included in our matrix, including image analysis.

There are other examples of word and image systems. Most were developed for particular applications and are slowly being modified to handle other types of cognitive situations. Digital Reasoning Systems Inc., for example, a company based in Franklin, Tennessee, that developed cognitive computing software for national security purposes, has begun to market intelligent software that analyzes employee communications in financial institutions to determine the likelihood of fraud.

Another company, IPsoft Inc., based in New York City, processes spoken words with an intelligent customer agent programmed to interpret what customers want and, when possible, do it for them.
IPsoft, Digital Reasoning, and the original Watson all use similar components, including the ability to classify parts of speech, to identify key entities and facts in text, to show the relationships among entities and facts in a graphical diagram, and to relate entities and relationships with objectives. This category of application is best suited for situations with much more — and more rapidly changing — codified textual information than any human could possibly absorb and retain.

Image identification and classification are hardly new. “Machine vision” based on geometric pattern matching technology has been used for decades to locate parts in production lines and read bar codes. Today, many companies want to perform more sensitive vision tasks such as facial recognition, classification of photos on the Internet, or assessment of auto collision damage. Such tasks are based on machine learning and neural network analysis that can match particular patterns of pixels to recognizable images.

The most capable machine learning systems have the ability to “learn” — their decisions get better with more data, and they “remember” previously ingested information. For example, as Watson is introduced to new information, its reservoir of information expands. Other systems in this category get better at their cognitive task by having more data for training purposes. But as Mike Rhodin, senior vice president of business development for IBM Watson, noted, “Watson doesn’t have the ability to think on its own,” and neither does any other intelligent system thus far created.

Performing Digital Tasks

One of the more pragmatic roles for cognitive technology in recent years has been to automate administrative tasks and decisions. In order to make automation possible, two technical capabilities are necessary. First, you need to be able to express the decision logic in terms of “business rules.” Second, you need technologies that can move a case or task through the series of steps required to complete it. Over the past couple of decades, automated decision-making tools have been used to support a wide variety of administrative tasks, from insurance policy approvals to information technology operations to high-speed trading.

Lately, companies have begun using “robotic process automation,” which uses work flow and business rules technology to interface with multiple information systems as if it were a human user. Robotic process technology has become popular in banking (for back-office customer service tasks, such as replacing a lost ATM card), insurance (for processing claims and payments), information technology (IT) (for monitoring system error messages and fixing simple problems), and supply chain management (for processing invoices and responding to routine requests from customers and suppliers).

The benefits of process automation can add up quickly. An April 2015 case study at Telefónica O2, the second-largest mobile carrier in the United Kingdom, found that the company had automated over 160 process areas using software “robots.” The overall three-year return on investment was between 650% and 800%.

Performing Physical Tasks

Physical task automation is, of course, the realm of robots. Though people love to call every form of automation technology a robot, one of Merriam-Webster’s definitions of robot is “a machine that can do the work of a person and that works automatically or is controlled by a computer.”

In 2014, companies installed about 225,000 industrial robots globally, more than one-third of them in the automotive industry. However, robots often fall well short of expectations. In 2011, the founder of Foxconn Technology Co., Ltd., a Taiwan-based multinational electronics contract manufacturing company, said he would install one million robots within three years, replacing one million workers. However, the company found that employing only robots to build smartphones was easier said than done. To assemble new iPhone models in 2015, Foxconn planned to hire more than 100,000 new workers and install about 10,000 new robots.

Historically, robots that replaced humans required a high level of programming to do repetitive tasks. For safety reasons, they had to be segregated from human workers. However, a new type of robots — often called “collaborative robots” — can work safely alongside humans. They can be programmed simply by having a human move their arms.

Robots have varying degrees of autonomy. Some, such as remotely piloted drone aircraft and robotic surgical instruments and mining equipment, are designed to be manipulated by humans. Others become at least semiautonomous once programmed but have limited ability to respond to unexpected conditions. As robots get more intelligence, better machine vision, and increased ability to make decisions, they will integrate other types of cognitive technologies while also having the ability to transform the physical environment. IBM Watson software, for example, has been installed in several different types of robots.

The Great Convergence

Slowly but surely, the worlds of artificially intelligent software and robots seem to be converging, and the boundaries between different cognitive technologies are blurring. In the future, robots will be able to learn and sense context, robotic process automation and other digital task tools will improve, and smart software will be able to analyze more intricate combinations of numbers, text, and images.
We anticipate that companies will develop cognitive solutions using the building blocks of application program interfaces (APIs). One API might handle language processing, another numerical machine learning, and a third question-and-answer dialogue. While these elements would interact with each other, determining which APIs are required will demand a sophisticated understanding of cognitive solution architectures.

This modular approach is the direction in which key vendors are moving. IBM, for example, has disaggregated Watson into a set of services — a “cognitive platform,” if you will — available by subscription in the cloud. Watson’s original question-and- answer services have been expanded to include more than 30 other types, including “personality insights” to gauge human behavior, “visual recognition” for image identification, and so forth. Other vendors of cognitive technologies, such as Cognitive Scale Inc., based in Austin, Texas, are also integrating multiple cognitive capabilities into a “cognitive cloud.”

Despite the growing capabilities of cognitive technologies, most organizations that are exploring them are starting with small projects to explore the technology in a specific domain. But others have much bigger ambitions. For example, Memorial Sloan Kettering Cancer Center, in New York City, and the University of Texas MD Anderson Cancer Center, in Houston, Texas, are taking a “moon shot” approach, marshaling cognitive tools like Watson to develop better diagnostic and treatment approaches for cancer.

 Continued on page 2

Designing a Cognitive Architecture

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The organizational cost of insufficient sleep 04-19

The organizational cost of insufficient sleep


Sleep-awareness programs can produce better leaders.
Thou hast no figures nor no fantasies,
Which busy care draws in the brains of men;
Therefore thou sleep’st so sound.

—William Shakespeare, Julius Caesar


In the passage above, the playwright’s tragic antihero Brutus enviously reflects on the timeless truth that people without worries and anxieties (in this case, his servant Lucius) generally enjoy the most peaceful and uninterrupted rest.

Some senior business people skillfully and consciously manage their sleep, emerging refreshed and alert after crossing multiple time zones or working late into the night. Yet we all know caffeinated and careworn executives who, after hours of wakeful slumber, struggle to recall simple facts, seem disengaged and uninspired, lack patience with others, and can’t think through problems or reach clear-cut decisions.

Sleep (mis)management, at one level, is obviously an individual issue, part of a larger energy-management challenge that also includes other forms of mental relaxation, such as mindfulness and meditation, as well as nutrition and physical activity. But in an increasingly hyperconnected world, in which many companies now expect their employees to be on call and to answer emails 24/7, this is also an important organizational topic that requires specific and urgent attention.

Research has shown that sleep-deprived brains lose the ability to make accurate judgments. That, in turn, can lead to irrational and unjustified claims such as “I do not need sleep” or “I’m doing fine with a couple of hours of sleep.” Our own recent survey of executives (see sidebar “Highlights from our survey of 196 business leaders”) demonstrates how many of them remain in denial on this point. Yet our respondents contradicted themselves by suggesting that companies should do more to help teach leaders the importance of sleep.

On this point, they are right. Many companies do not do enough to promote healthy sleep, which can have serious consequences. As we will demonstrate, sleep deficiencies impair the performance of corporate executives, notably by undermining important forms of leadership behavior, and can thereby hurt financial performance. This article will demonstrate and explore the link between sleep and leadership behavior before discussing solutions that can improve both individual well-being and organizational efficiency and effectiveness.

The link to organizational leadership

The last part of our brain to evolve was the neocortex, responsible for functions such as sensory perception, motor commands, and language. The frontal part of the neocortex, the prefrontal cortex, directs what psychologists call executive functioning, including all the higher-order cognitive processes, such as problem solving, reasoning, organizing, inhibition, planning, and executing plans. These help us get things done.

It’s long been known that all leadership behavior relies on at least one (and often more than one) of these executive functions and therefore, in particular, on the prefrontal cortex. Neuroscientists know that although other brain areas can cope relatively well with too little sleep, the prefrontal cortex cannot.¹ 1. Namni Goel et al., “Neurocognitive consequences of sleep deprivation,” Seminars in Neurology, 2009, Volume 29, Number 4, pp. 320–39; Ilse M. Verweij et al., “Sleep deprivation leads to a loss of functional connectivity in frontal regions,” BMC Neuroscience, 2014, Volume 15, Number 88, biomedcentral.com. Although basic visual and motor skills deteriorate when people are deprived of sleep, they do not do so nearly to the same extent as higher-order mental skills.

Previous McKinsey research has highlighted a strong correlation between leadership performance and organizational health,² 2. We define organizational health as the ability to align around a clear vision, strategy, and culture; to execute with excellence; and to renew a company over time by responding to market trends. itself a strong predictor of a healthy bottom line. In a separate study of 81 organizations and 189,000 people around the world, we have found that four types of leadership behavior are most commonly associated with high-quality executive teams: the ability to operate with a strong orientation to results, to solve problems effectively, to seek out different perspectives, and to support others.³ 3. See Claudio Feser, Fernanda Mayol, and Ramesh Srinivasan, “Decoding leadership: What really matters,” McKinsey Quarterly, January 2015. What’s striking, in all four cases, is the proven link between sleep and effective leadership (exhibit).

Operating with a strong orientation to results

To do this well, it’s important to keep your eye on the ball and avoid distractions, while at the same time seeing the bigger picture—that is, whether your company is heading in the right direction. Scientists have found that sleep deprivation impairs this ability to focus attention selectively. Research shows that after roughly 17 to 19 hours of wakefulness (let’s say at 11 PM or 1 AM for someone who got up at 6 AM), individual performance on a range of tasks is equivalent to that of a person with a blood-alcohol level of 0.05 percent. That’s the legal drinking limit in many countries. After roughly 20 hours of wakefulness (2 AM), this same person’s performance equals that of someone with a blood-alcohol level of 0.1 percent, which meets the legal definition of drunkenness in the United States.

Solving problems effectively

Sleep is beneficial for a host of cognitive functions—insight, pattern recognition, and the ability to come up with innovative and creative ideas—that help us solve problems effectively. One study has shown that a good night’s sleep leads to new insights: participants who enjoyed one were twice as likely as those who didn’t to discover a hidden shortcut in a task. Likewise, an afternoon nap has been found to aid creative problem solving: subjects who took a nap after struggling on a video-game problem were almost twice as likely to solve it as subjects who had remained awake. Other research has established that creative thinking is especially likely to take place during dream sleep, enhancing the integration of unassociated information and promoting creative solutions.

Seeking different perspectives

A wealth of scientific studies have also highlighted the impact of sleep on all three stages of the learning process—before learning, to encode new information; after learning, in the consolidation stage, when the brain forms new connections; and before remembering, to retrieve information from memory. An important consideration for leaders seeking different perspectives is the ability to weigh the relative significance of different inputs accurately, to avoid tunnel vision, and to reduce cognitive bias. Sleep has been shown to improve decision making for tasks that mimic real life, such as complex cognitive–emotional ones which integrate emotional responses by involving financial rewards and punishments. Science supports the commonly heard advice that rather than making an important decision or sending a sensitive email late at night, you should sleep on it.
5. Matthew P. Walker and Els van der Helm, “Overnight therapy? The role of sleep in emotional brain processing,” Psychological Bulletin, 2009, Volume 135, Number 5, pp. 731–48, apa.org/pubs.

Supporting others

To help other people, you must first understand them—for example, by interpreting the emotions on their faces or their tone of voice. In a sleep-deprived state, your brain is more likely to misinterpret these cues and to overreact to emotional events,⁶ 6. Els van der Helm, Ninad Gujar, and Matthew P. Walker, “Sleep deprivation impairs the accurate recognition of human emotions,” Sleep, 2010, Volume 33, Number 3, pp. 335–42, journalsleep.org; Els van der Helm et al., “REM sleep depotentiates amygdala activity to previous emotional experiences,” Current Biology, 2011, Volume 21, Number 23, pp. 2029–32. and you tend to express your feelings in a more negative manner and tone of voice.⁷ 7. Eleanor L. McGlinchey et al., “The effect of sleep deprivation on vocal expression of emotion in adolescents and adults,” Sleep, 2011, Volume 34, Number 9, pp. 1233–41, journalsleep.org. Recent studies have shown that people who have not had enough sleep are less likely to fully trust someone else, and another experiment has demonstrated that employees feel less engaged with their work when their leaders have had a bad night of sleep
. William H. Macey and Benjamin Schneider, “The meaning of employee engagement,” Industrial and Organizational Psychology, 2008, Volume 1, Number 1, pp. 3–30, my.siop.org/journal; and Steven A. Stumpf, Walter G.Tymon Jr., and Nick van Dam, “Felt and behavioral engagement in workgroups of professionals,” Journal of Vocational Behavior, 2013, Volume 83, Number 3, pp. 255–64, journals.elsevier.com/journal- of-vocational-behavior.

What organizations can do

How can organizations improve the quality and efficiency of sleep to ensure that their leaders attain—or recapture—the highest performance levels? At McKinsey, we’ve been working on this issue with our own colleagues, as well as with business leaders, over the past year. We offer this menu of possible solutions for companies to consider. As we are the first to admit, our own people do not always practice what we preach. In any case, certain types of organizations cannot implement these ideas without an accompanying change in the underlying culture.

Training programs

Interestingly, 70 percent of the leaders in our survey said that sleep management should be taught in organizations, just as time management and communication skills are now. Ideally, such programs should be part of a unified learning program that includes a number of components, such as online assessments, in-person workshops, and a performance-support app offering reminders, short inspirational videos or animations, additional assessments, and opportunities to connect with online communities. (For a selection of healthy sleep habits, see sidebar “Sleep tips.”)


Companies should embed sleep training in a broader approach to well-being that takes in other topics, notably exercise, nutrition, mindfulness, and energy management. Yet it can be daunting for leaders to go about changing a lot of behavior at once, so it’s important to allow time for new habits to stick.

Company policies

Before introducing new policies, businesses should start a conversation among their leaders to determine which ideas will best suit the organization, particularly bearing in mind the fact that working cultures differ.

Travel. Companies should encourage flexibility—for example, by allowing employees, if possible, to take an earlier plane (rather than an overnight “red eye” flight) to get a good night’s sleep before an important meeting.

Team working. Companies must increasingly be responsive 24/7, but this doesn’t mean that specific people should bear the brunt of the burden single-handedly. IT help desks in many global organizations have shown the way—shifting location every eight hours. Likewise, other groups should work to alleviate the pressure by creating “tag teams” of employees who seamlessly hand over the reins to other teams, in different time zones, at the end of their shifts. Phone calls and home-based videoconferences do run the risk of extending the workday but, used judiciously, can cut unnecessary travel-to-work time. Leaders should set an example by being mindful of local times (and the time preferences of the people involved) when scheduling global calls. Simply knowing the participants’ preferences can help reinforce a sleep-friendly culture.

Emails. A number of companies have imposed blackout times on work emails. A large European car business, for example, programs the smartphones of its nonmanagement employees to switch off work emails automatically between 6 p.m. and 7 a.m. In many companies, particularly knowledge-based ones, this would be disruptive and counterproductive—but provided there are overrides, such a policy can send a clear signal of management’s intent.

Work-time limits. Some companies known for a “long-hours culture” have been implementing rules to curb working very late at night. One major financial-services business, for example, specifically required its summer interns to leave the office before midnight each day to ensure that they were not subjected to “all-nighters.” This organization’s full-time employees have been told to stay out of the office from 9 p.m. Friday to 9 a.m. Sunday.

Mandatory work-free vacations. A US software company gives employees a $7,500 bonus if they follow two rules: (1) They have to actually go on vacation or they don’t get the money. (2) They must disconnect, and hence cannot work, on vacation.

‘Predictable time off’ (PTO). Leslie Perlow, a professor at Harvard Business School, introduced a good way to catch up on lost sleep: a planned night off, with no email, no work, and no smartphone. A large global consulting firm found that productivity went up when it tested this approach, which is now the basis for a company-wide program.

Napping rooms or pods. The image of a sleeping manager is easy to mischaracterize. Research has shown that a short nap of 10 to 30 minutes improves alertness and performance for up to two and a half hours.⁹ 9. Amber Brooks and Leon Lack, “A brief afternoon nap following nocturnal sleep restriction: Which nap duration is most recuperative?,” Sleep, 2006, Volume 29, Number 6, pp. 831–40, journalsleep.org. Over half of the leaders in our survey wanted their businesses to imitate the large technology companies and telcos that have already successfully adopted sleep pods and nap rooms.
Smart technology. Companies should consider supplying (or at least informing their employees about) some of the gadgets and tools designed to improve sleep management. Examples include the f.lux application, which limits blue light on computers and iPhones, thereby boosting reduced levels of the sleep hormone melatonin. Other apps on the market provide individualized jetlag-minimizing schedules.

Organizations of the future

Much attention has been focused on the importance of sleep for top-performing athletes, musicians, and even politicians. Expert violinists, for example, have cited practice and sleep as two of the most important drivers of performance. (One study shows that the top performers consistently take a nap and get over half an hour more sleep than their less well-regarded peers do.) Former US president Bill Clinton once admitted, “Every important mistake I’ve made in my life I made when I was tired.” Business people have often lagged behind others in both their willingness to acknowledge the issue and their readiness to act on it.

A recent Harvard Medical School study surveyed senior leaders and found that 96 percent reported experiencing at least some degree of burnout. One-third described their condition as extreme.¹⁰ 10. Leslie Kwoh, “When the CEO burns out,” Wall Street Journal, May 7, 2013, wsj.com. It’s time for organizations to find ways of countering the employee churn, lost productivity, and increased healthcare costs resulting from insufficient sleep. If it is true that some millennials care less about high salaries and more about work–life integration, the next generation of employees will demand solutions even more strongly.


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Deliberately Developmental How workplaces can build success by unlocking the potential of every employee. -04-17

  

Deliberately Developmental

How workplaces can build success by unlocking the potential of every employee.

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Many workplaces claim to be committed to fostering the personal growth of their employees. But few are deliberately organized to put employee growth at the very center of their mission. After all, when was the last time you worked somewhere that asked you to talk several times a day about your progress on personal-improvement goals, to undergo a formal review in front of colleagues (who discuss your performance with total candor), or to recognize that longtime leaders may be demoted by their peers at any time?

It may sound shocking. But imagine if, at the same time, your colleagues were excited to celebrate your personal accomplishments, your supervisor was eager to make your development one of her top priorities, your organization's leaders prioritized trust and held themselves to the same standards as others, and that everyone in your workplace was fully committed to the success of both the company and each other?

Organizations that engage in practices like these — called “deliberately developmental” in a new book exploring the topic — have embraced the “simple but radical” conviction that they will thrive only if and when every one of their employees does.

And as the authors of An Everyone Culture found, these companies appear to have charted a new and distinctive path to success, one that lays out a new definition of what it means to be at work. In the process, they broaden the notion of a “learning organization,” making the case that any workplace — including, but not limited to, schools — can be a site of deep learning and personal growth.

The benefits for the three deliberately developmental organizations (DDOs) the book profiles are staggering: increased profitability, improved employee retention, better communication, reductions in employee downtime, less interdepartmental strife, and faster solutions to tough problems, such as how to anticipate crises, create valuable leadership, and realize future possibilities — to name a few.
These companies offer a model for the rest of us — for organizations and schools seeking to grow, for leaders wanting to make a cultural change, and even for individual employees who may feel stuck, say Lisa Laskow Lahey and Robert Kegan, who wrote An Everyone Culture with fellow Harvard Graduate School of Education faculty Matthew L. Miller and Deborah Helsing and with organizational consultant Andy Fleming.

It turns out, says Lahey in a recent EdCast interview, that “when you support people’s development, it allows the organization to better and better deliver on its mission.” When employees overcome their barriers to success — facing their vulnerabilities and offering support in kind — organizations clear hurdles, too.

Five Qualities that Make a DDO

In their analysis of these DDOs, the authors demonstrate to other organizations how they, too, can maximize their outcomes by helping employees overcome personal challenges and amplify their potential.

A key finding: Development does not happen on its own. Engrained practices — or specific routines that emphasize growth in every aspect of the workday — are key to ensuring that employees will keep growing.

What types of practices encourage growth?

1. Practices that shed light on internal struggles. The DDOs the researchers studied all invited employees to share challenges and goals that, typically, might be considered irrelevant to a professional environment. By helping workers overcome internal or personal struggles, the organizations helped them address longstanding limitations in the way they approached their work.

2. Practices that connect professional and personal work. This same focus on surmounting personal challenges was not a separate part of the work day, but integrated throughout. For instance, rather than host an annual session on how to receive feedback, the organizations made feedback and coaching a fundamental part of every meeting.

3. Practices that shift the focus from outcomes to the processes generating those outcomes. All three DDOs were less concerned with correcting an unhelpful behavior than they were with changing the mindset that created that behavior. This perspective helped emphasize the importance of achieving long-term goals above short-term ones.

Practices that give employees a common language. Each organization wound up with its own “language” used to describe its many practices and norms. While perhaps confusing to outsiders, these languages both built community within each workplace and helped further integrate the developmental practices into the workday. Practices that exist every day, at every level. Personal development was always an essential goal — for entry-level new hires and for the most senior leaders alike, in meetings and on individual assignments, every day of the week.

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Airport Authority of India Executive Recruitement 04-13

Airport Authority of India Executive Recruitment

AAI Junior Executive Recruitment 2016:

Apply for 220 posts

AAI Jr Executive Recruitment 2016:The application process will commence from April 18 and the last date to send the fully completed form is on May 17, 2016

               

AAI Jr Executive Recruitment 2016: The CTC would be around Rs 6.9 lakh.

The Airports Authority of India (AAI) has invited candidates for the post of junior executive in various streams. The selected candidates will fill up around 220 vacancies.

The application process will commence from April 18 and the last date to send the fully completed form is on May 17, 2016.

The Airports Authority of India (AAI) ATC is under the Ministry of Civil Aviation.

Vacancy details

Jr Executive (Civil): 50 posts

Jr Executive (Electrical): 50 posts

Jr Executive (Information Technology): 20 posts

Jr Executive (Airport Operations): 100 posts

Salary: Rs 16,400 to Rs 40,500. The CTC would be around Rs 6.9 lakh

View: PHOTOS: Top government jobs to apply in March-April 2016

Educational qualifications:

The candidates should have a minimum 60% marks in degree. The candidates who have appeared for the final year examination can also apply.

Junior Executive (Civil): BE/ B.Tech in Civil engineering

Junior Executive (Electrical): B.E/ B.Tech in Electrical engineering

Junior Executive (IT): B.E/ B.Tech in Computer science/ Computer engineering/ IT

Junior Executive (Airport Operations): Graduate in science and MBA (regular) or Bachelors degree in engineering. Valid driving license is essential for JE (Airport Operations).


Age limit: The candidates who had applied should have a maximum age as on 31.5.2016

General: 27 years


OBC: 30 years


SC/ST: 32 years



View the detailed advertisement

From Data to Decision, Moving past the Hype (About Research)

About the Research

About the Research

To understand the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review conducted its sixth annual survey of business executives, managers, and analytics professionals from organizations located around the world. The survey, conducted in the summer of 2015 in partnership with SAS, captured insights from 2,192 respondents across the world, from a wide variety of industries and from organizations of all sizes. The sample was drawn from a number of sources, including MIT alumni, MIT Sloan Management Review subscribers, and other interested parties.

In addition to these survey results, we interviewed subject matter experts from a number of industries and disciplines to understand the practical issues facing organizations today in their use of analytics. Our interviewees’ insights contributed to a richer understanding of the data. We also drew upon a number of case studies to illustrate how organizations are using business analytics as a strategic asset.
In this report, the term “analytics” refers to the use of data and related business insights developed through applied analytical disciplines (for example, statistical, contextual, quantitative, predictive, cognitive, and other models) to drive fact-based planning, decisions, execution, management, measurement, and learning.

About the Authors:

Sam Ransbotham is an associate professor in the Information Systems Department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Review’s Data & Analytics Big Idea initiative.

David Kiron is the executive editor of MIT Sloan Management Review.
Pamela Kirk Prentice is the chief research officer at SAS Institute Inc.

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Contributors

Michael Fitzgerald, contributing editor, MIT Sloan Management Review
Nina Kruschwitz, senior project manager, MIT Sloan Management Review

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Acknowledgments

Steve Allan, head of analytics, Silicon Valley Bank

Gahl Berkooz, former head of data and governance, Ford Motor Company
Bin Lin, co-founder and president, Xiaomi

Joseph Bruhin, chief information officer, Constellation Brands

Jason Cooper, vice president and chief analytics officer, Horizon Blue Cross Blue Shield of New Jersey

Angela Galeziowski, vice president of sales strategic insights & planning, InterContinental Hotels Group

J. Michael Gaziano, MD, MPH, director of science, Massachusetts Veterans Epidemiology Research and Information Center, and a principal investigator for the Million Veteran Program
Greg Jones, vice president of enterprise data & analytics, Equifax

Dev Koushik, vice president of global revenue optimization, InterContinental Hotels Group
Adam Leary, lead data scientist and senior director of the data team, CBS Interactive
David Pahl, director of analytics, The Northwestern Mutual Life Insurance Company
Saiju Pyarajan, scientific director, GenISIS (Million Veteran Program)
Hugh Scandrett, vice president of engineering, EnerNOC

David Schmitt, director of global digital operations and analytics, InterContinental Hotels Group
Larry Seligman, vice president of global sales and marketing strategic analytics, InterContinental Hotels Group

Jim Sprigg, director of database marketing and analytics, InterContinental Hotels Group
James Taylor, CEO and principal consultant, Decision Management Solutions

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To cite this report, please use: S. Ransbotham, D. Kiron, and P.K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review, March 2016

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Tags: Big Data, Competitive Strategy, Long-term Strategy

References (12)

1. See, for example, “The 11 Best Data Quotes,” July 8, 2012, https://blog.datamarket.com; A. McAfee and E. Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review 90 (October 2012): 60-68; and B. Marr, “Big Data-as-a-Service Is Next Big Thing,” April 27, 2015, www.forbes.com.

2. Several surveys conducted by other organizations point to dissatisfaction with results from big data technology, but general managers, who comprise the bulk of our respondents, remain enthusiastic about the potential of analytics. For a different point of view about satisfaction trends with data technology, see Q. Turner, “Data Digest: Big Data Disappointment, Stranded Data Scientists, and Locking Down Mobile Devices,” October 26, 2015, https://tdwi.org; and D. Henschen, “Big Data Meets Trough of Disillusionment: Gartner,” November 18, 2013, www.informationweek.com.

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