Here are a few things we know about the future of work:
- 85 percent of jobs that will exist in 2030 haven’t been invented yet.
- By 2025, almost 100 million new roles will emerge that require people “more adopted to the new division of labor among humans, machines, and algorithms,” according to the World Economic Forum.
- While the news these days is focused on layoffs, talent shortages that increasingly hobble global business won’t be cured by hiring freezes.
- Thanks in great part to artificial intelligence, industry-by-industry workforce realignment that used to take years to execute after lengthy research is now done in one business quarter.
- The fundamental understanding and definition of “job” is evolving from a single perceived function to a stack of fast-change skills.
All of which means there’s a tremendous number of things we don’t know about the future of work.
Change has become so rapid that wherever people labor – office factory, home, coffee shop, farm, and field – nobody is even sure exactly what work is anymore. Work and staffing strategies go obsolete as fast as they’re devised, which may account for the recent whiplash hypercycle of over-hiring and over-firing.
Fortunately for business leaders and workers – both groups longing for the reassurance of solid certainty – increasingly capable artificial intelligence has the capacity to analyze and understand all the changes that (re)define and clarify the fundamental nature of work.
AI’s capacity to understand what a job truly entails and how that definition is changing augments human ingenuity to suggest better ways to design and build teams, structure workforces, divide tasks properly between humans and machines, and develop engaging, motivating worker career mobility that will future-proof companies and their employees.
What is a job, anyway?
The disruption and confusion of the pandemic, global digital acceleration across almost all industries, and the emergence of multiple flavors of hybrid work have driven business and HR leaders back to fundamental questions: What does “work” really look like? What is a “job,” anyway?
AI can help develop actionable answers. One example is AI’s ability to create a real-time dashboard of the future as it emerges by monitoring all jobs posted and discussed on jobs boards, corporate websites, and LinkedIn, showing where organizations are hiring for different talents and skill sets, analyzing how organizational structures are evolving, and predicting where multiple trends will lead.
Increasingly, AI is helping companies decide “build, buy, or bot” – which skills should be hired, which taught, and which assigned to robotic automation?
But even for AI insatiable data-maw, there’s a problem of plenty – so many questions, a universe of variables. Where to begin?
Even the mightiest AI needs a framework to help us understand first the actual nature of work and then the optimal future of work.
For example, media-star ChatGPT, the fastest-growing application in history, has limited value without a framework. It can list jobs within functions and offer a rough description of those jobs, but without a framework of meaning, it can’t explain how those jobs relate to one another, and how those job relationships in turn can best fuel organizational success.
The framework AI needs to deliver on its potential is the success profile.
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Success profile – the atomic structure of work
The success profile is work’s atomic structure, the smallest unit in the universe of work. Just as all objects are composed of atoms arranged in certain ways, all organizational designs, job architectures, career ladders, and career paths are composed of success profiles arranged in certain ways.
A success profile is a common language of a job’s necessary skills, responsibilities, and relationships to other jobs that allows people and our AI augmenters track trends and changes across organizational functions.
AI-powered success profiles aren’t static job/skills descriptions. They’re living, three-dimensional, dynamic resources updated in real time by internal and external talent and business market data and customized for each user’s specific purpose. The richer and more comprehensive the data, the stronger the success profile. The stronger the success profile, the more useful AI’s insights.
Success profiles are limber tools. An HR professional may want to look at success profiles one way, a business leader/team-builder another, and employees yet another (what skills are critical to me for success in current and future roles?).
AI can simplify what seems like overwhelming complexity
Using success profiles as frameworks, AI is a powerful tool in (re)defining the universe of work, clarifying what seems like hopeless complexity and delivering actionable solutions.
KF Digital, for example, used AI to (re)define the universe of tech work. We began by identifying the departments in all organizations where tech work is done. Then we asked, “Within each function, what are the sub-functions?” Next came detailed portraits of career ladders – practitioners, individual contributors, managers, subject matter experts, leaders, etc.
Drilling further down, we asked, “What are the job families? Where are they different? Where are they similar? What kinds of people are working inside those families, and what are their functions?”
For example, within data science there’s data engineering, machine learning, and quality assurance. For each of those families there are individual contributors who work with scrum teams, team leads, a team manager for both projects and team-members’ careers, and various subject matter experts.
The success profile for each of these jobs defines responsibilities, duties on the job, and the critical behavioral, technical, and leadership competencies needed.
Sounds imposing, doesn’t it? Layer upon layer, vertically and horizontally. We used AI within the framework of success profiles to examine 40 million data points across the 60,000 tech jobs listed in all available sources and were able to reduce 60,000 jobs to just 350 distinct roles.
This vastly streamlined structure enables leaders to manage strategically today and create future career opportunities for their teams.
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AI is creating a universal taxonomy of work
AI is creating a universal taxonomy of work. Just as the success profile is the common building block of work, an AI-defined universal taxonomy will be a shared, fundamental principle that differs across industries, geographies, and organizations. It will, in the end, be the Periodic Table of the Elements of Work.
The accelerating capabilities of AI promise multiple benefits for companies and their employees that I plan to discuss in future posts – among them trend-spotting that mutually benefits companies and workers, enabling optimized workforce planning, accelerating DE&I initiatives, and future-proofing careers.
As it should be for any powerfully disruptive development, the role of AI in HR is the focus of vigorous and often contentious debate. My view, developed through experience, is that a technology that creates greater efficiency, speedier innovation, and a clearer vision of the future for organizations, as well as flexibility, upskilling, and career-long mobility for workers, is rich with promise.
Read More: Deel’s ‘State of Global Hiring Report’ Shows That Global Hiring Is Still Growing, Despite Higher Rate of Layoffs and Lower Starting Salaries