Mindset

From “learn, do, retire” to “learn, do, learn, do, rest, learn . . . repeat”: In his 1970 bestseller Future Shock, Alvin Toffler said, “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.”⁵ His observation becomes more relevant every day.

The great twentieth-century giants like General Motors and Ford grew by aggregating individual craftsmen in cottage industries into jobs in centralized factories. For decades, the careers that created income and development during employment and retirement comprised those jobs. Predictability and stability allowed schools to train talent before employment, organizations to add additional long-term skills, and careers to progress consistently through job families organized into functions like R&D, manufacturing, HR, finance, and sales. Authority and accountability predictably progressed from individual contributor to supervisor to executive. This linear progression worked with predictable and relatively stable economic growth, and supported organizations of global scale and hundreds of thousands of employees. Perhaps the most vivid icon of this stability was defined retirement pension benefits and medical plans, made possible by predictable growth and shorter life spans.

Of course, modern reality could not be more different. The volatile, uncertain, complex, and ambiguous modern world is amplified by the technology convergence and global transparency of the fourth industrial revolution (see chapter 3). This reality is often recognized in decisions about money, technology, innovation, customers, and markets. We see it in the shrinking half-life of skills and the relentless reinvention of jobs. These changes in work combine with societal trends such as increased life expectancy, ubiquitous virtual connectivity, social media, cyber threats, and income inequality.⁶ Table 6-1 summarizes some of these changes.

Past generations could rely on rewards from a pattern of “learn, do, retire.” That no longer holds. Jobs and professions have a shorter half-life, even as the duration of our work lives increases. The World Economic Forum estimates that 65 percent of children entering primary school will ultimately end up working in jobs that don’t yet exist and in professions that are very different and constantly changing.⁷ The new pattern will reflect a series of careers, built upon projects and shorter tours of duty in each organization.⁸ That requires a mindset more like “learn, do, learn, do, rest, learn . . . repeat.”

In chapter 5, we described how these work changes are enabling and requiring new organizational forms, supported by constantly reinvented jobs. However, the same work and organizational evolution also has significant implications for leaders. Leaders must adopt and encourage a mindset of constant retooling, supported by necessary changes in the very ideas of work, culture, and values. Leaders must pursue their careers this way and require, support, and encourage workers to adopt a mindset of continuous development. Instead of saying to workers, “This is what you will become,” leaders must say, “You start with these skills and capabilities, and our work is to collaborate to refine and enhance them.” For example, leaders can start by deconstructing competencies, skills, and capabilities, and distinguishing those with short-term value from enabling skills with longer-term value that underpin the ability to thrive in constant change. Enabling skills include things like critical thinking and global mindset. They are very different from technical skills that have received such significant social and policy attention, such as mathematics, computer coding, and so on. Leaders once expected workers to acquire technical skills before employing them. Increasingly, leaders select workers less on technical skills and more on enabling skills. Leaders must identify and nurture the ability to learn and apply a changing array of technical skills. Acquiring and changing your technical skills will be a routine, repetitive exercise that occurs as work and jobs are constantly reinvented and perpetually upgraded.

The Global Talent 2021 study by Oxford Economics, in collaboration with Willis Towers Watson and a number of other corporate and academic partners, showed that “agile thinking and relationship skills” were rated by leading executives as two of the most important future skills.⁹ This includes the ability to consider and prepare for multiple scenarios, manage paradoxes and balance opposing views, innovate, and co-create, and brainstorm. They were rated as far more important than specific technical skills.

This reinforces our descriptions of how work automation reinvents jobs. Manufacturing workers will have technical skills in machine tool repair and operating a precision drill. They will also have the skills of collaborating with colleagues, diagnosing problems, and understanding how work fits with broader processes and contributes to the total solution. As work is reinvented, automation may take on 60 percent of the tasks, such as operating the precision drill and removing and installing tools. Does this mean human workers will be replaced? Probably not, but their value is now in training the automation and collaborating with the automation to diagnose problems and invent solutions. At first, the human workers’ prior technical skills will support the tasks of training the automation. As reinvention evolves, the automation will have learned enough to operate on its own. The workers’ enabling skills in critical thinking and analytical ability will help them add value by analyzing and repairing the robot.

For leaders, this means creating a relationship and a work system where the human workers feel safe and motivated to report when they envision new automation applications that might replace some of their tasks. Leaders must show that, as automation progresses and jobs are reinvented, they can be trusted to help the human workers adapt or move to other organizations in a humane and exciting way.

Ability

From employment qualifications to work readiness. Employment qualifications are often defined using technical skills. Employers and policy makers lament the fact that companies can’t find workers with the specific skills for today’s work. Governments, employers, and educational institutions try to identify and provide those skills to reduce the gaps. However, as value shifts from technical to enabling skills, education, training and learning must retool accordingly.

Workers can increasingly acquire technical skills quickly and cheaply, outside of employment or educational institutions. Lynda.com, the world’s largest online learning resource, contains all the courses needed to qualify a C++ developer, who can acquire those skills in as little as fourteen hours of focused learning. Lynda offers learning pathways for many technical skills (Python, Java, iOS10, graphic design, 3D animation, network/infrastructure administration, etc.); Lynda is only one such source. Online learning sources also give workers a clear understanding of adjacencies. For example, graphic design and 3D animation are adjacent skills, because the work of graphic design can be upskilled to 3D animation work. On the other hand, although they share some common attributes, Python programming skill is not closely related to graphic design, because it takes many more hours and a longer pathway to go from Python to 3D animation, even though 3D animation may be done on software that uses Python programming.

Leaders must understand such relationships and offer workers the most effective learning path, even if that means leaving their organization. Learning is integrated with rewards by combining an online marketplace or talent platform like Upwork with the online learning solutions of Lynda. Now, a Java programmer on Upwork can earn $40 per hour, but can easily see that Android programmers earn $90 per hour. Lynda.com shows them that with four additional courses over fourteen hours, they can become certified Android programmers. When the price and alignment of such skill pathways is so apparent, leaders can and must shift their focus from searching for candidates who are fully prepared to search for those who are optimally close to being qualified and strategies to optimize the work and worker to achieve the most efficient match.

Automation plays into this in two ways: it accelerates the continual evolution toward new skills, and it provides the means to acquire and demonstrate those skills. As technical skills become increasingly easy to acquire and change, leaders will expect educational institutions and their organization’s long-term training and development to reinvent with a focus on enabling skills. Typically, such skills are codified only in proprietary corporate skill inventories or competency systems. So, they are not transparent and transportable. They seldom appear on a university diploma or certification, for example. Future organizations may need to reinvent that by supporting platforms that track and report enabling skills, as technical skills are now.

Leaders must enable relationships with workers that span multiple engagements of different kinds, punctuated by workers leaving to acquire new skills through education or work with other organizations. Leaders must learn to identify and track enabling capabilities, even among workers who may not currently possess the formal certifications or degrees in required technical areas. The best talent may well be a worker with an amazing ability to learn and see connections, but whose technical qualifications don’t yet match current needs.

Reward

From salaries for permanent jobs to flexible total rewards for deconstructed tasks and work arrangements. Typically, organizations value and reward work by combining tasks into a job and then surveying a market of rewards for comparable jobs in comparable organizations. Data provided by online sites such as salary.com and LinkedIn has increasingly modernized the process. Certainly, a worker’s rewards reflect personal attributes like experience and “hot skills” like Python programming, but typically the rewards are attached to a job. This is even true when workers are engaged through arrangements other than regular full-time employment, because the requisitions for contractors or freelancers are often based on jobs.

Yet, step one of our framework requires deconstructing jobs into work tasks and then constantly reconfiguring and reinventing new jobs. Step two estimates the ROIP of tasks. So, in the future world of perpetually reinvented jobs, future rewards must reflect the ROIP of tasks. Then, they must reflect the cost, risk, and productivity of human workers versus automation for those tasks, and the optimal combination of humans and automation.

How can work be valued in this new world? Traditional job-based market surveys are incomplete and inefficient because needed information is proprietary, resides in different places, and is opaque or difficult to get. So, the reward market that relies on pricing jobs moves slowly such that the pricing base is stable. A market for deconstructed tasks can be much more efficient, as talent platforms already show. If you want to engage an Android app developer, you can go to Upwork, Appirio, or other such platforms, and immediately find an array of developers and current pay rates, with the range reflecting their different past performance ratings, experience, and current expertise. The prices for the tasks of Android app development, and even the language to describe the work, all change quickly, as skills change, as parts of the work are automated, and as workers respond by changing their capabilities. Because the unit of analysis is the deconstructed work task and not the job, these markets support a higher volume and velocity of transactions. As workers and employers discover work tasks that they can substitute or augment with automation, or as automation creates entirely new versions of the work, a market freed from job descriptions can adapt more easily and quickly. This adaptation reflects more than merely how much a task is valued. The market can also differentiate rewards to reflect location, independence, continuity, reputation, and even how the work supports missions such as environmental sustainability, social justice, and so on.

How does this reward system change leadership? Leaders and workers will more constantly negotiate and renegotiate the nature and rewards of work, because work and jobs will be perpetually reinvented. Those reward negotiations will focus on tasks that are deconstructed and reconstructed to optimize automation as well as different human-worker arrangements. Both leaders and workers will increasingly have access to the same information, a far different situation than that in which only the leader knows organizational pay levels and only the workers know their true capabilities and alternatives.

The example of the reinvented job of an oil driller in chapter 1 illustrates the evolving rewards. Recall how the job is changing, shown in table 6-2.

Traditionally, leaders would value this work by comparing their organization to a market of other companies that employ workers in the job of “driller,” described in the left column of the table. The market data has little relevance to the reinvented job shown in the right column. How should a leader value the new job? Earlier chapters showed that the work optimization choices will increasingly be based on distinctive combinations of deconstruction, automation compatibility, ROIP, automation availability, and organization considerations. Because every organization is reinventing its jobs differently and rapidly, there will no longer be a sample of comparable jobs in other organizations. So, no one salary survey provider may be able to give market data for the new job. Of course, eventually, this reinvention process may produce some common and uniform work-automation combinations across organizations, but that will likely take too long. Leaders will need to deconstruct and reconstruct the work far faster than traditional salary surveys can reflect in their jobs.

An alternative way to establish the market value for the new job might be to estimate the proportion of the work for each task:

• Cockpit monitoring and response (25 percent of your time)
• Coordination with operations center (30 percent of your time)
• AI training and system innovations and upgrading (20 percent of your time)
• Change leadership and stakeholder engagement (25 percent of your time)

Then, you estimate a market value task by task. You might consult a talent platform that prices the component tasks of the new job. You might scan databases of community colleges for the salaries of certified graduates. You establish prices for the tasks, not the job, so you are not limited to organizations in your industry. You can tap data from a wide variety of industries, including aviation, mining, and transportation. You might end up with the following average prices on a per-hour basis:

• Cockpit monitoring: $45
• Coordination with operations centers: $30
• AI training and system innovation and upgrading: $60
• Change leadership and stakeholder engagement (based on freelancers who do change management)

As you do this, you realize that you could execute the human work tasks with combinations of freelancers, contractors, and regular employees. However, if you decide to reinvent these tasks into one job, and assume a two-thousand-hour work year, the estimated market value for your new job is $114,500.

How do you consider the enabling skills of your current employees in the driller job, such as learning agility, critical thinking, and global mindset, as well as organizational history and knowledge that is critical connective tissue to ensure the various activities are appropriately integrated? These skills are intangible because there is little market data. So, you might attach a 20 percent premium for these intangibles, bringing the market value of the new job to $137,000.

As talent marketplaces like Upwork grow in scope and scale, the quality of data will only improve over time, making such analysis increasingly more common and straightforward.

Deployment

From job architecture and movement between jobs to work architecture continuously matching capabilities to tasks. Traditionally, talent is deployed to jobs, using job architectures. A job like “software development engineer” exists in a job family like software development that contains multiple engineering jobs and levels. Each higher level has greater scope, impact, and responsibility. Several job families like software development and network design are grouped into job families like engineering. Organizations hire and develop workers through jobs and job families.

This can be costly and inefficient. Moreover, as we have seen, perpetually upgraded or agile work is reinvented too quickly for such fixed and job-based architectures to keep up. Deploying workers from one job to another is just too blunt-edged to capture the nuances of deconstruction, reinvention, and work automation. When the unit of analysis is the work tasks, more agile knowledge architecture can use data from multiple sources like LinkedIn and Upwork to match worker capabilities to work.

Recall the example of the oil driller. It may be possible to map the adjacency of those who did the old driller job to show them precisely what skills they need for the new job and how to get those skills. Some of the previous drillers can be deployed to the new job if they acquire the needed skills. Or, some of the former drillers may work part-time on some tasks in the new job, while also pursuing other work within the organization. Knowledge architecture, often powered by AI, can mine information on talent inside and outside the organization and match it to demands as they emerge. Workers can be borrowed for a project or set of tasks requiring skills and domain expertise for the duration the work demands.

As a leader, your relationship with the work and workers changes fundamentally. You oversee deployment options that cross the organization boundary, and your role is not so much to match workers in one job with their next job, but rather to optimize worker development to fit perpetual work evolution. You will more frequently look for workers who are close but not necessarily perfect matches. You will deploy workers to projects so they can develop targeted skills. You will use the language of jobs less often and the language of deconstructed tasks, automation, and workers’ capabilities and desires more often.

As job architectures enabled the organizations of the second industrial revolution, the deconstructed task and knowledge architecture form the basis for the more networked ecosystems of work that will likely characterize the fourth industrial revolution.

Development

From career ladders based on fixed jobs to reskilling pathways based on tasks and reinvented jobs. How do we then ensure the continued reskilling of humans? In the past, stable economic environments and technology allowed development to happen within specific professional domains and a single organization. Accountants built a base of technical skills and then took additional classes and a progression of jobs to expand them. They might have started a career doing financial reporting in a corporation and then shifted to working as an auditor with a large accounting firm, leveraging knowledge of US accounting laws and regulations. Then, they might have shifted into consulting or management accounting, building on their knowledge to add expertise in accounting principles outside the United States. This journey followed a predictable and stable path to build and enhance technical skills that were easily verified and tracked.

In the future, such technical skills will change more quickly, often being replaced or altered as the work combines with automation. Moreover, those changes will be difficult to predict far in advance. Enabling skills will last longer, but workers will need to develop technical and enabling skills and to adjust to career paths and learning pathways that change quickly.

As a leader, you will play a key role in whether the future of work and automation means the demise of learning and development within organizations, or the birth of a more precise, comprehensive, and boundless approach. This new approach will better account for the whole worker, rather than only those attributes that matter for a particular job or that are included in your proprietary organizational competency model. It’s possible to imagine future leaders as unconcerned with worker development, relying on the workers themselves to navigate a more transparent system of platforms and online career communities like LinkedIn. However, leaders have the opportunity to create unique worker engagement by guiding them through a connected and evolving array of development options, informed by more open work architecture. Leaders in future best places to work will likely become just this sort of skilled guide.

Work-automation optimization through deconstruction and job reinvention will provide leaders with greater insight into where and how automation will replace human labor for specific tasks. Also, reskilling will increasingly rely on enabling skills, not technical skills. The new reskilling pathways will map enabling skills through many different types of work that will span different professional domains, work arrangements, and organizations.

Accountants, in addition to their technical accounting skills, might also possess enabling skills: a global mindset, a strong process and method orientation, caution and risk aversion, and a learning orientation.

Reskilling pathways would track how these skills support the full development path and the new technical skills acquired along the way. Now, an accountant’s career might include managing an oil rig in Saudi Arabia, leading the actuarial function at a global insurer, or operating as an independent quality assessor for a major pharmaceutical company. These jobs seem very far from the typical career path. What do these different types of work have in common? They each require the following enabling skills for success:

• Oil rig manager. A global mindset is needed to manage a team of workers from around the world. The process and method orientation ensures the integrity of highly repetitive, process-based work. The enabling skill of caution leads to success when a small mistake can have devastating consequences.
• Actuarial leader. The global mindset is evident in leading a global function. The process and method orientation supports maintaining the integrity of determining reserves, evaluating claims, and so on. Caution and risk aversion enhance performance on risk diligence.
• Independent quality assessor. A global mindset is not used to supervise a global team, but rather for evaluating processes and products in many different countries. The process orientation now supports creating consistent, repeatable processes that can be audited and verified. The enabling skills of caution and risk aversion are in play for establishing appropriate risk tolerances for deviations from established standards.