As adjunct faculty, I get a front-row seat to the AI revolution in education and the workplace. What I've observed is both exciting and concerning, a paradox that we must navigate carefully as think about our future work.
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Image Credit: Wanda Tuerlinckx , Humanoids
Who is pacing this race?
Employees have been encouraged to ‘automate their roles’ to demonstrate self-direction and continuous learning. In the past, an employee's skills, motivation, and business interests determined the pace of change. Soon, the pace may be beyond their control, risking job loss before they can adapt to consider the next set of problems. If they can’t find problems faster than the pace of automation, they are not adequately prepared for transition.
Read MoreFundamentals of workplace automation
[ From McKinsey ]
As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated—at least in the short term.
The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM’s Watson, Rethink Robotics’ Baxter, DeepMind, or Google’s driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines’ ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.
What will be the impact of automation efforts like these, multiplied many times across different sectors of the economy? Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life? Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?
Earlier this year, we launched research to explore these questions and investigate the potential that automation technologies hold for jobs, organizations, and the future of work.3 Our results to date suggest, first and foremost, that a focus on occupations is misleading. Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined, much like the bank teller’s job was redefined with the advent of ATMs.
More specifically, our research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent about $2 trillion in annual wages. Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated.
The organizational and leadership implications are enormous: leaders from the C-suite to the front line will need to redefine jobs and processes so that their organizations can take advantage of the automation potential that is distributed across them. And the opportunities extend far beyond labor savings. When we modeled the potential of automation to transform business processes across several industries, we found that the benefits (ranging from increased output to higher quality and improved reliability, as well as the potential to perform some tasks at superhuman levels) typically are between three and ten times the cost. The magnitude of those benefits suggests that the ability to staff, manage, and lead increasingly automated organizations will become an important competitive differentiator.
Christine Haskell, PHD has built her practice on credible, published research and data. In the Research Series, you’ll find highlights, shareable statistics, and links to the full source material.
The Future of Jobs report 2018
[ From the Centre for the New Economy and Society ]
As technological breakthroughs rapidly shift the frontier between the work tasks performed by humans and those performed by machines and algorithms, global labour markets are undergoing major transformations. These transformations, if managed wisely, could lead to a new age of good work, good jobs and improved quality of life for all, but if managed poorly, pose the risk of widening skills gaps, greater inequality and broader polarization.
As the Fourth Industrial Revolution unfolds, companies are seeking to harness new and emerging technologies to reach higher levels of efficiency of production and consumption, expand into new markets, and compete on new products for a global consumer base composed increasingly of digital natives. Yet in order to harness the transformative potential of the Fourth Industrial Revolution, business leaders across all industries and regions will increasingly be called upon to formulate a comprehensive workforce strategy ready to meet the challenges of this new era of accelerating change and innovation.
This report finds that as workforce transformations accelerate, the window of opportunity for proactive management of this change is closing fast and business, government and workers must proactively plan and implement a new vision for the global labor market. The report’s key findings include:
• Drivers of change: Four specific technological advances—ubiquitous high-speed mobile internet; artificial intelligence; widespread adoption of big data analytics; and cloud technology—are set to dominate the 2018–2022 period as drivers positively affecting business growth. They are flanked by a range of socio-economic trends driving business opportunities in tandem with the spread of new technologies, such as national economic growth trajectories; expansion of education and the middle classes, in particular in developing economies; and the move towards a greener global economy through advances in new energy technologies.
• Accelerated technology adoption: By 2022, according to the stated investment intentions of companies surveyed for this report, 85% of respondents are likely or very likely to have expanded their adoption of user and entity big data analytics. Similarly, large proportions of companies are likely or very likely to have expanded their adoption of technologies such as the internet of things and app- and webenabled markets, and to make extensive use of cloud computing. Machine learning and augmented and virtual reality are poised to likewise receive considerable business investment.
• Trends in robotization: While estimated use cases for humanoid robots appear to remain somewhat more limited over the 2018–2022 period under consideration in this report, collectively, a broader range of recent robotics technologies at or near commercialization— including stationary robots, non-humanoid land robots and fully automated aerial drones, in addition to machine learning algorithms and artificial intelligence— are attracting significant business interest in adoption. Robot adoption rates diverge significantly across sectors, with 37% to 23% of companies planning this investment, depending on industry. Companies across all sectors are most likely to adopt the use of stationary robots, in contrast to humanoid, aerial or underwater robots, however leaders in the Oil & Gas industry report the same level of demand for stationary and aerial and underwater robots, while employers in the Financial Services industry are most likely to signal the planned adoption of humanoid robots in the period up to 2022.
• Changing geography of production, distribution and value chains: By 2022, 59% of employers surveyed for this report expect that they will have significantly modified how they produce and distribute by changing the composition of their value chain and nearly half expect to have modified their geographical base of operations. When determining job location decisions, companies overwhelmingly prioritize the availability of skilled local talent as their foremost consideration, with 74% of respondents providing this factor as their key consideration. In contrast, 64% of companies cite labour costs as their main concern. A range of additional relevant factors—such as the flexibility of local labour laws, industry agglomeration effects or proximity of raw materials—were considered of lower importance.
• Changing employment types: Nearly 50% of companies expect that automation will lead to some reduction in their full-time workforce by 2022, based on the job profiles of their employee base today. However, 38% of businesses surveyed expect to extend their workforce to new productivity-enhancing roles, and more than a quarter expect automation to lead to the creation of new roles in their enterprise. In addition, businesses are set to expand their use of contractors doing task-specialized work, with many respondents highlighting their intention to engage workers in a more flexible manner, utilizing remote staffing beyond physical offices and decentralization of operations.
• A new human-machine frontier within existing tasks: Companies expect a significant shift on the frontier between humans and machines when it comes to existing work tasks between 2018 and 2022. In 2018, an average of 71% of total task hours across the 12 industries covered in the report are performed by humans, compared to 29% by machines. By 2022 this average is expected to have shifted to 58% task hours performed by humans and 42% by machines. In 2018, in terms of total working hours, no work task was yet estimated to be predominantly performed by a machine or an algorithm. By 2022, this picture is projected to have somewhat changed, with machines and algorithms on average increasing their contribution to specific tasks by 57%. For example, by 2022, 62% of organization’s information and data processing and information search and transmission tasks will be performed by machines compared to 46% today. Even those work tasks that have thus far remained overwhelmingly human—communicating and interacting (23%); coordinating, developing, managing and advising (20%); as well as reasoning and decisionmaking (18%)—will begin to be automated (30%, 29%, and 27% respectively). Relative to their starting point today, the expansion of machines’ share of work task performance is particularly marked in the reasoning and decision-making, administering, and looking for and receiving job-related information tasks.
• A net positive outlook for jobs: However this finding is tempered by optimistic estimates around emerging tasks and growing jobs which are expected to offset declining jobs. Across all industries, by 2022, growth in emerging professions is set to increase their share of employment from 16% to 27% (11% growth) of the total employee base of company respondents, whereas the employment share of declining roles is set to decrease from currently 31% to 21% (10% decline). About half of today’s core jobs—making up the bulk of employment across industries—will remain stable in the period up to 2022. Within the set of companies surveyed, representing over 15 million workers in total, current estimates would suggest a decline of 0.98 million jobs and a gain of 1.74 million jobs. Extrapolating these trends across those employed by large firms in the global (nonagricultural) workforce, we generate a range of estimates for job churn in the period up to 2022. One set of estimates indicates that 75 million jobs may be displaced by a shift in the division of labour between humans and machines, while 133 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms. While these estimates and the assumptions behind them should be treated with caution, not least because they represent a subset of employment globally, they are useful in highlighting the types of adaptation strategies that must be put in place to facilitate the transition of the workforce to the new world of work. They represent two parallel and interconnected fronts of change in workforce transformations: 1) large-scale decline in some roles as tasks within these roles become automated or redundant, and 2) large-scale growth in new products and services—and associated new tasks and jobs— generated by the adoption of new technologies and other socio-economic developments such as the rise of middle classes in emerging economies and demographic shifts.
• Emerging in-demand roles: Among the range of established roles that are set to experience increasing demand in the period up to 2022 are Data Analysts and Scientists, Software and Applications Developers, and Ecommerce and Social Media Specialists, roles that are significantly based on and enhanced by the use of technology. Also expected to grow are roles that leverage distinctively ‘human' skills, such as Customer Service Workers, Sales and Marketing Professionals, Training and Development, People and Culture, and Organizational Development Specialists as well as Innovation Managers. Moreover, our analysis finds extensive evidence of accelerating demand for a variety of wholly new specialist roles related to understanding and leveraging the latest emerging technologies: AI and Machine Learning Specialists, Big Data Specialists, Process Automation Experts, Information Security Analysts, User Experience and Human-Machine Interaction Designers, Robotics Engineers, and Blockchain Specialists.
• Growing skills instability: Given the wave of new technologies and trends disrupting business models and the changing division of labour between workers and machines transforming current job profiles, the vast majority of employers surveyed for this report expect that, by 2022, the skills required to perform most jobs will have shifted significantly. Global average skills stability—the proportion of core skills required to perform a job that will remain the same—is expected to be about 58%, meaning an average shift of 42% in required workforce skills over the 2018–2022 period.
• A reskilling imperative: By 2022, no less than 54% of all employees will require significant re- and upskilling. Of these, about 35% are expected to require additional training of up to six months, 9% will require reskilling lasting six to 12 months, while 10% will require additional skills training of more than a year. Skills continuing to grow in prominence by 2022 include analytical thinking and innovation as well as active learning and learning strategies. Sharply increasing importance of skills such as technology design and programming highlights the growing demand for various forms of technology competency identified by employers surveyed for this report. Proficiency in new technologies is only one part of the 2022 skills equation, however, as ‘human’ skills such as creativity, originality and initiative, critical thinking, persuasion and negotiation will likewise retain or increase their value, as will attention to detail, resilience, flexibility and complex problem-solving. Emotional intelligence, leadership and social influence as well as service orientation also see an outsized increase in demand relative to their current prominence.
• Current strategies for addressing skills gaps: Companies highlight three future strategies to manage the skills gaps widened by the adoption of new technologies. They expect to hire wholly new permanent staff already possessing skills relevant to new technologies; seek to automate the work tasks concerned completely; and retrain existing employees. The likelihood of hiring new permanent staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption. However, nearly a quarter of companies are undecided or unlikely to pursue the retraining of existing employees, and two-thirds expect workers to adapt and pick up skills in the course of their changing jobs. Between one-half and two-thirds are likely to turn to external contractors, temporary staff and freelancers to address their skills gaps.
• Insufficient reskilling and upskilling: Employers indicate that they are set to prioritize and focus their re- and upskilling efforts on employees currently performing high-value roles as a way of strengthening their enterprise’s strategic capacity, with 54% and 53% of companies, respectively, stating they intend to target employees in key roles and in frontline roles which will be using relevant new technologies. In addition, 41% of employers are set to focus their reskilling provision on high-performing employees while a much smaller proportion of 33% stated that they would prioritize at-risk employees in roles expected to be most affected by technological disruption. In other words, those most in need of reskilling and upskilling are least likely to receive such training.
There are complex feedback loops between new technology, jobs and skills. New technologies can drive business growth, job creation and demand for specialist skills but they can also displace entire roles when certain tasks become obsolete or automated. Skills gaps—both among workers and among the leadership of organizations—can speed up the trends towards automation in some cases but can also pose barriers to the adoption of new technologies and therefore impede business growth.
The findings of this report suggest the need for a comprehensive ‘augmentation strategy’, an approach where businesses look to utilize the automation of some job tasks to complement and enhance their human workforces’ comparative strengths and ultimately to enable and empower employees to extend to their full potential. Rather than narrowly focusing on automation-based labour cost savings, an augmentation strategy takes into account the broader horizon of value-creating activities that can be accomplished by human workers, often in complement to technology, when they are freed of the need to perform routinized, repetitive tasks and better able to use their distinctively human talents.
However, to unlock this positive vision, workers will need to have the appropriate skills enabling them to thrive in the workplace of the future and the ability to continue to retrain throughout their lives. Crafting a sound in-company lifelong learning system, investing in human capital and collaborating with other stakeholders on workforce strategy should thus be key business imperatives, critical to companies’ medium to long-term growth, as well as an important contribution to society and social stability. A mindset of agile learning will also be needed on the part of workers as they shift from the routines and limits of today’s jobs to new, previously unimagined futures. Finally, policy-makers, regulators and educators will need to play a fundamental role in helping those who are displaced repurpose their skills or retrain to acquire new skills and to invest heavily in the development of new agile learners in future workforces by tackling improvements to education and training systems, as well as updating labour policy to match the realities of the Fourth Industrial Revolution.
Christine Haskell, PHD has built her practice on credible, published research and data. In the Research Series, you’ll find highlights, shareable statistics, and links to the full source material.
My Path With Numbers, Nerves & the Future of Work
After 20+ years in tech, the last ten laying front-end process and back-end infrastructure enabling a data-driven culture at MSFT, I had a few observations that made me question the work I was doing. The more available and accurate the data became, the more dependent on data people became. The result? leaders made decisions that doubled down on investing in known quantities like Office and Windows, missing opportunities to dominate and lead in several growing areas of technology. That’s not just my opinion. Those are facts.
There was little awareness, attention or focus paid to subjective skills like having good judgment, sound evaluation skills, or what psychology refers to as “other ways of knowing.” At that time, qualitative data was never as highly regarded as qualitative data.
Cultures that claim to be data driven often let other skills go under-utilized. They become preoccupied with the lure of predictability—the holy grail of business management. They seek data for the smallest of decisions. A study from MIT’s Human Dynamics Laboratory claims to have identified the elusive group dynamics that characterize high-performing teams. Looking at two separate call centers, researchers found that patterns of communication explained why performance varied so widely among seemingly identical teams in that bank’s call center. The best predictors of productivity were a team’s energy and engagement outside formal meetings. Drawing on that insight, they advised the center’s manager to revise the employees’ coffee break schedule so that everyone on a team took a break at the same time. That would allow people more time to socialize with their teammates, away from their workstations. Leaders are starting to rely on spreadsheets and gadgets to give them a ‘God’s-eye view of human behavior.’
Did we really need to invest in expensive, predictive analytics to tell us that those blessed with the energy, creativity, and shared commitment far surpass other teams? Not only was this dynamic uninspiring to me, I felt we were moving in the wrong direction. That began my journey away from what felt like technology for technology’s sake, and toward questions of self-awareness, critical thinking, and ethical responsibility.
Initially, this led me to study applied behavioral science, sustainability, and leadership, and psychology at the graduate level. A common thread through all the literature, for me, was: values. Our lived values are the foundation of our decision making and ultimately dictate the kind of lives we lead, how happy we allow ourselves to be and become, and achieve.
My research taught me:
Values, whether we are aware of them or not, guide our decisions.Our lives are punctuated by experiences, decisions, or influences. How we respond to those events directs the course of our lives, and in particular, when we find ourselves at a significant choice point, our upbringing can have an enduring influence on the work we choose and our larger career decisions. What we subconsciously learn from our parents plays an important role in how we think about and manage those career decisions.
We are on a path toward the fullest expression of ourselves, whether we know it or not. If we are not aware, honest, or clear about our values (i.e., how we got them and what they are), it is reflected in all of our decisions–and subsequently, our work. Rather than thinking aspirationally of our values, our decisions under pressure are the most honest reflection of our values and ourselves. We are deeply shaped by values and how well we live them.
Both people and organizations lose their way by losing touch with their core values. As individuals, we experience dead ends. Sometimes this is in the form of unfinished projects. More extreme versions of this state result in some form of midlife crisis. I prefer to call this a midlife crossroad because not all “crises” are negative–some can be incredibly fulfilling. However, the path toward closing the gap of who you thought you were and the beliefs you relied on, and who you are now and the beliefs you hold now, can be both painful and incredibly enlightening.
Self-awareness, deliberate practice, and experimentation are the path forward. Practicing your values in a consistent way brings meaning to your work and life and enables you to be congruent. Lived andpracticed, our values guide the expression of our work.
Most people (~70%) are unengaged by their work, yet they are seeking more skills. Two-fifths say their senior leaders prioritize employee engagement, and just 28% said their managers are highly skilled at fostering engaged individuals and teams.
Engagement and ability to fail are linked. Failure and the concept of failing fast have become chic to talk about again. Yet, too often, we ask people to sign up to fail at something they don’t care about. If you agree to fail at something, and I do believe an agreement is required to avoid dysfunction and abuse, you must care about something enough and know why you care about it.
And last, that learning is truly ongoing. We learn a lot, especially when we are interested in the subject. But to retain and integrate requires ongoing practice. An example of my (almost) daily practice re-explores #NotesFromMyYogaJournal.
When I left my job in 2014 to complete my doctorate in Industrial Psychology, people thought I was crazy. I was leaving a stable job and steady paycheck, for what?
Work I care about. Work I experiment with and fail in order to learn. Work where I can take responsibility for my own learning, development, and advancement.
Today, I am working harder now than I ever have in my life–and I’ve worked hard my whole career. I’m learning in a way I was never able to do in my former career, and that is not because I left the corporation. It is because I learned how to create my own safety and take responsibility for my own learning with hyperfocus–two skills we are not taught in schools and do not learn in workplace training. Making these skills available to people has become the focus of my current work.
To both earn and manage the kind of responsibility technology like artificial intelligence will put upon us, we need to start training our minds to reduce the inner chatter. We need to find our Craft, manage feelings v why we continue to develop a feel for our work and engage in the kinds of bold experiments that will solve the problems of tomorrow. We need to learn to contemplate, comprehend, and respond more and react less. We need to not only solve problems, but we also need to find them.
The future of work depends on the state of the human mind, specifically our awareness, ability to reason and contend with values, ethics, and great paradoxes.
Christine Haskell, PhD is a writer and consultant helping leaders increase their attachment to their work to lead with greater effectiveness.
UPDATE: this post has been updated to reflect the latest engagement trends, which still hold at 70% un-engaged.
Robotic surgery
[ From the Mayo Clinic ]
Robot-assisted heart surgery
Robotic surgery, or robot-assisted surgery, allows doctors to perform many types of complex procedures with more precision, flexibility and control than is possible with conventional techniques. Robotic surgery is usually associated with minimally invasive surgery — procedures performed through tiny incisions. It is also sometimes used in certain traditional open surgical procedures.
About robotic surgery
Robotic surgery with the da Vinci Surgical System was approved by the Food and Drug Administration in 2000. The technique has been rapidly adopted by hospitals in the United States and Europe for use in the treatment of a wide range of conditions.
The most widely used clinical robotic surgical system includes a camera arm and mechanical arms with surgical instruments attached to them. The surgeon controls the arms while seated at a computer console near the operating table. The console gives the surgeon a high-definition, magnified, 3-D view of the surgical site. The surgeon leads other team members who assist during the operation.
Christine Haskell, PHD has built her practice on credible, published research and data. In the Research Series, you’ll find highlights, shareable statistics, and links to the full source material.
Explaining Is Harder Than Achieving
More difficult than achieving expertise in something is describing it, especially to a novice or someone outside their medium. When asked directly about their expertise, virtually none of the craftsmen referred to themselves as an expert. While all had a master designation, they all felt they were still learning, still developing an edge to their skills. They also described the challenges of their medium by using other disciplines: “Clay drying is different than paint drying.” An interesting analogy, but to a novice in both disciplines, the implications of dry clay versus dry paint are lost. They talk about having a feel for their material, understanding the pace of the work, and showing up deliberately in their practice. Across the board, the actual work was described as “meditative.”
What does a novice do in the face of this level of ambiguity? The death of the industrial age has exposed a level of uncertainty we are not used to. We reach for playbooks, follow recipes, seek templates that we know will work—we do what is familiar, tried, and proven. We become conservative in our experiments. We start to think conventionally. This is the very reaction I am working toward changing with this book.
The work we do matters. How we go about it matters.
The degree to which we can dance with uncertainty is in direct proportion to the ingenuity of our problem-solving capability. The more we risk what we know, the more we learn. The better our skills get, the more we can engage uncertainty (in ourselves, in others, and in our environment), the greater our capacity for solving problems. Increasing our capacity for uncertainty increases our tolerance for dealing with the unknown. The more problems we directly interact with, the greater the change we are capable of making in the world.
Skills are about meeting a standard. Mastery is the ability to create your own standard. In order to achieve mastery, we need to be improvisational with our skills, like a craftsman. It’s our choice, then, to find what level of uncertainty we can effectively manage when we learn something new. My wish is that these vignettes provide a way forward for people interested in developing their life craft, in whatever work they do.
So how do I know about craftsmanship? Through detailed interviews, observation, and hands-on learning. I wanted to understand what made their approach to work unique in a way that didn’t repeat what I was trying to avoid: a top ten list, a template, or a playbook. Too often we are looking to emulate that a handful of leaders or companies that enjoyed a unique set of circumstances, at a particular time in their development. In business, we sometimes believe that if organizations do those same five things, or produce the same kinds of products, we too can get where they are. The organizations we admire, it turns out, do enjoy a prime state but seldom stays there for long.[i] Virtually every company featured in Good To Great peaked at the time of that book’s initial publication.[ii] Greatness in business is generally measured in stock market return or market share. We might imagine that we’d like to be in their shoes, but most leaders in those positions have such a high-risk profile, their focus is on playing not to lose.
For this effort, I sought people performing at their peak with a different definition of success. They regard the struggle to learn as part of the privilege of their craft, of working hard to make a difference and do work that is worthwhile to them. From these individuals, I gathered detailed descriptions of their work, I observed them, with some I even practiced alongside, and I recorded my understanding of their efforts in action. The findings in this book are the result of that effort.
This process formed my ideas about the essential qualities of craftsmanship. I learned how craftsmen regard and engage problems, the relationship between process and outcomes, and the emergence of craft from a foundation of inner knowledge that generally goes unspoken and unseen. This project also informed me about the potential of our innate ingenuity.
In analyzing how craftsmen approach their work and how that work reflects them as individuals, I wanted to know how the eager and awkward efforts of an apprentice can transform into the flowing precision of a master. I wanted to understand an approach to living and working that’s possible across all fields, and a way of learning that helps people wrestle with the ungainliness of the beginner. I wanted to understand a way of engaging a business that helps people grapple with the uncertainty, change, external expectations, uniqueness, and complexity of organizational growth—while solving societal problems. I wanted to understand how people across a variety of sectors—from engineers, members of a band, to leaders of organizations—can effectively apply their uniqueness to a problem at hand, using business as their medium for creative self-expression.
This approach to business—call it craftsmanship.
Interviewing master craftsmen and business leaders about how they learn; I wanted to understand what motivates them; what inspires them; how they orient themselves toward their work; where their attention wonders when they are at work; how they go about their work; how they confront challenges; and, how they push against the constraints of their mediums. We discussed how they manage the tension of remaining open to the surprises (that enable creativity), while demonstrating control (expertise) in their craft. This research resulted in a navigation system for business and leadership discussed later in this book that anyone can apply toward their own development if they look beyond scale as a primary goal and think of their work as a medium for creativity and not a template to fit into.
Once we sat down, I asked them questions. A lot of questions. What do you value most? What made you get this going to the degree that you have? What was the biggest decision you ever made? What was your biggest mistake? How do you ignore the competition and maintain focus on your personal vision? How did you find your purpose? What has influenced you most in your life? What is success? How do you know you’ve achieved success? When do you feel pressure to conform? What do you do about that? —to name a few.
[i] prime Mintzberg, H. (1984). Power and organization life cycles. Academy of Management review, 9(2), pp.207–224. and Adizes, I. (1979). Organizational passages—diagnosing and treating lifecycle problems of organizations. Organizational dynamics, 8(1), pp.3–25.
[ii] good to great failed http://www.economist.com/node/13980976AND http://y0ungmoney.blogspot.com/2015/04/good-to-great-is-flawed-book.html
This post is part of a series #LookToCraftsmen set for publication in 2019.