Why do so many companies know more about their customers than their own employees?
This is the central question posed by my colleague James Root in his new book, The Archetype Effect. It is indeed an intriguing mystery. Business today segments customers in all sorts of ways to construct value propositions highly tailored to their needs. Personalized marketing—down to the individual consumer—is now quite commonplace.
Not so with employees.
Most HR systems are built on the assumption that everyone goes to work for the same reasons. In reality, it’s a very different story.
Deaveraging employees
The book highlights six distinct archetypes of workers, each with unique motivations:
- Givers are driven by helping others and thrive in collaborative environments.
- Operators value stability and teamwork, preferring clear instructions and minimal risk.
- Explorers seek variety, creativity, and new experiences, prizing flexibility and innovation.
- Artisans, motivated by mastery and pride in their work, are autonomous and focused on quality.
- Strivers, ambitious and career-oriented, are motivated by recognition and advancement.
- Pioneers are visionary and entrepreneurial, motivated by creating and often by leading new ventures.
These archetypes challenge long-standing management assumptions. The conventional wisdom in many organizations is that people need to be motivated by a vision. In fact, many, including most operators, don’t particularly care; they find meaning outside of work. Similarly, it’s often assumed that employees naturally want to advance to the next level. In fact, many, including most artisans, are not particularly interested. Just like with customers, we need to “deaverage” employee motivations.
Earlier this month I had lunch with a group of graduate students just starting their summer internship. They all had some degree of work experience already and, through this internship, were trying out the world of management consulting. The conversation focused on their career aspirations, why they were interested in this industry, and what they wanted to get out of the summer.
As they told their stories, Root’s insights reverberated in my head. Each intern had quite different motivations. One was clearly a striver, interested in climbing the corporate ladder as quickly as possible. Another seemed more of an explorer, wanting exposure to global opportunities and asking questions about mobility and cross-border assignments. The lunch left me convinced that if we treated them all the same, we would not tap into their true motivations or full potential.
The business imperative
Nearly 10 years ago, Michael Mankins and Eric Garton wrote Time, Talent, Energy about the power of employees who are not only satisfied but truly engaged and even inspired. Their research showed that engaged employees are 44% more productive than satisfied employees, and those who feel inspired at work are nearly 125% more productive.
If we think about human capital the way we do financial capital, the returns on building a more inspired workforce become clear.
Management must do a better job understanding and motivating people. Of the approximately 3.5 billion people who go to work each day, nearly 1.2 billion feel “not engaged” and more than 500 million are “actively disengaged,” according to research in The Archetype Effect.
Surely there is a better way to maximize time, talent, and energy.
How AI can help build a more effective talent strategy
We live in a time of rapid change, in which workers often seem less committed to their firms and firms less committed to their workers. Young people struggle to get started in their careers, not knowing where or how to begin. Older workers are among the more motivated, but many firms don’t fully embrace them.
Artificial intelligence, already reshaping so many jobs, could help to close this disconnect. A new generation of digital, AI-assisted HR management tools might be able to match people to jobs more effectively, improving our ability to design tailored career journeys instead of having everyone climb one monolithic ladder. As we feed models more data about who we are at work—skills, achievements, qualifications, prior roles, trainings, motivations—they in turn will feed us more insights into performance and potential career pathways.
It’s possible for gen AI to make HR more human, not less. A recent analysis by John Hazan and Susan Gunn suggests that a typical company can save, on average, up to 20% in HR labor time through AI automation and augmentation. Think how that 20% could be more productively redirected toward identifying and addressing different employee motivations for work. This could elevate HR’s role in the organization, from transactional operator to strategic adviser.
Many companies are already collecting quantitative and qualitative data about their teams and experimenting with greater personalization for workers. This can include flexible options for where and when work can be done; flexible benefits such as financial planning services, additional personal leave, and wellness programs; flexible career paths with more sophisticated learning and development programs; internal mobility options; and the opportunity to spend work time on projects outside one’s job description, something that started at companies like Google but is beginning to be extended to blue-collar workers as well.
My lunch with the summer interns sparked deeper engagement with our leadership team on ways to inspire the diverse people in our organization. Armed with quantitative data as well as insights from supplemental interviews and focus groups, we now better understand the different motivations of different workers and are tailoring action plans accordingly.
We need a diverse set of skills and backgrounds, and it only stands to reason that everyone won’t be looking for the same things. The key to motivating them, as The Archetype Effect eloquently argues, is to deaverage workers as we have consumers. The business case for an engaged—and inspired—workforce is clear. The question is whether leaders will act.