Job Analysis 2.0:
Mapping the Pace of Change

Job analyses typically generate “snapshots” of a profession at a single point in time—yet work demands are constantly changing. We’ve pioneered the use of new methods, including AI-based approaches, that are helping address this challenge.

February 19, 2020 – Ironically, the ancient Greek philosopher Heraclitus may have captured the essence of 21st century work best with his pithy quote that “Change is the only constant.” From technological innovations and automation to the gig economy and shifting labor trends, myriad forces are collectively transforming the way work is performed and the knowledge, skills, and abilities (KSAs) workers need to be successful. From within this dynamic environment, a critical challenge emerges: How do organizations map the requirements of jobs that may be constantly evolving?

Despite their rigor, typical job analytic (JA) methods may provide an incomplete answer to this question. Though they clearly generate a wealth of useful information, the picture that most JAs paint reflects “one moment in time”—and even in relatively stable industries, that picture can quickly become outdated. Recognizing this dilemma, talent management professionals often ask another critical question: How often should JA information be updated to keep up with the “pace of change”?

There have been some valiant attempts to answer this question, with a recommendation emerging from them to update JA information every five to seven years. This may not be an optimal strategy in all cases, though, because the pace of change can vary quite dramatically across jobs—and even within a job’s various duties. Recognizing this nuance and complexity, we have employed several innovative methods to map the pace of occupational change that fall largely within two domains: 1) Tracking emergent job changes through AI-based methods, and 2) Forecasting how changes may differentially impact certain aspects of a job.

The type of qualitative, text-based information contained in position descriptions and vacancy announcements can truly be a gold mine when tracking the evolution of occupational requirements, particularly because that is where such changes first appear. However, the unstructured (not to mention typically quite voluminous) nature of such information has severely limited its usefulness to date.

Our staff has harnessed the power of AI to profile jobs, using text from job descriptions and associated task statements as input. For example, we recently applied AI-based techniques to profile military jobs and identify jobs with similar requirements, where data from subject matter experts (SMEs) was not available or incomplete. Our approach opens the door to many additional applications of these techniques, including:

• Systematically and continuously tracking shifts in the importance of job requirements.
• Automatically refreshing profiles as job requirements change.
• Identifying new or emerging job requirements.
• Using profiles to assess whether vacancy announcements effectively communicate requirements that are consistent with the type of occupation being advertised.

These AI-based methods go beyond offering large scale, streamlined estimation of KSA and competency ratings—they truly create the potential for new automated applications of fundamental JA data and concepts.

It seems very reasonable to assume that the pace of change may not impact each element of a job equally—some may be relatively immune to the forces that often drive change (e.g., the emergence of new technologies or shifts in workforce demographics), while others are almost constantly evolving. Despite this, most JA methods are not particularly effective at teasing out such differences.

We recently conducted a study to examine the pace of change in the architecture profession that directly identified which elements of the profession may be experiencing the most change, and why. First, we developed a series of “forecast statements” reflecting trends impacting specific architecture practice areas (i.e., Project Planning and Design, Construction and Evaluation). Next, SMEs estimated the target years when such forecasts are likely to come true, as well as rating the future importance of critical tasks and KSAs. This approach revealed that while the fundamentals around what architects do may not be rapidly changing, their delivery methods and the tools they use certainly are—and there is a trend towards increasing specialization across the profession. Our approach also helped illuminate:

• When future changes are likely to occur.
• Which tasks and knowledge domains are becoming more important.
• How to design and implement “pulse check” surveys to monitor potential changes in domains that are the most likely to experience them.

JAs reflect a tremendous amount of time and resources to conduct. Ideally, they should generate more than a static picture of a job or profession and be able to capture at least some of the dynamism inherent in the work itself. The methods described here move us closer to that goal.