PDSA + AI for Career Development
I coach young professionals (YP) in project settings. The initial goals they used to bring to the coaching centered around project management, agile or waterfall. No longer. Today, the two more common goals are these:
Leverage AI Appropriately
Get a (Better) Job
YP’s tend to organize these in a straight forward way: learn AI and leverage it “appropriately” in search for a new job or as part of a career pivot.
In my experience, most of them use AI already. However, for some, AI tends to open rabbit holes, one after another, and lead, often, to an intersection of dead ends.
Other YP’s do become able to leverage AI powerfully in the job search. Most of these seem to do so informed by their own project experience, especially the practice of Plan-Do-Study-Act (PDSA). This is the basic Deming cycle for continuous quality improvement – of products and processes – with “quality” understood, basically, as “fitness to purpose”.
I see YP’s with project experience adapting this cycle to the purpose of their job search. In doing so, they clarify and continuously improve the key construct of their own “purpose”. This also enables them to continuously improve:
the quality of job openings they surface,
the quality of their choice of job openings to pursue,
the quality of their pursuit
the quality of positions they ultimately secure.
How does PDSA yield these outcomes?
To Plan, YP’s define career and job objective(s), criteria job openings must satisfy, and sources of such openings
To Do, they search, via identified sources, for current job openings that meet their criteria
To Study, they review openings yielded by the search. They also learn more about their current job objective and search criteria, about the relevant job market and organizations, and about their career purpose
In the Act Phase, they act on each selected, current opening, applying, adapting, or abandoning it.
How does this improvement become “continuous”? The Study phase of iteration “n” looks not only at the current iteration “n”, but also ahead. It informs the design of iteration n + 1, especially via revisions to the Plan phase of n + 1.
It is in the context of such a PDSA practice that I see YP’s “leverage AI appropriately” for job search and career development. Specifically, it is the Plan and Do phases where they do the heavy and appropriate leveraging. In the Plan phase, the YP engineers (and reengineers) an AI prompt, as a “job opening aggregator” in order to surface, extract, and aggregate job openings that fit the YP’s purpose. YP’s also automate the labor intensive “Do” phase by having their AI aggregator go out, search, and aggregate current openings.
Some YP’s try to leverage AI also in the Study and Act phases. In my experience however, what seems to accelerate the learning – and to shorten the time to landing a position, is the quality of YP practice in the “human-in-the-loop” phases of “Study”, “Act”, and “Plan/Design”.
In my experience to date, I have seen it take as long as six months for a YP to land a new or better position by practices like this one. That said, I have never seen any such a practice fail to have positive outcomes for the YP from the very start. What are these? They are: 1) the YP’s understanding of themselves and of “fit” to a given opening, evolves in clarity and depth, as does 2) the YP’s understanding of the pertinent job market, leading to 3) the YP getting a new job with a great fit and career potential, and, all along, 4) the YP learning AI – hands-on and continuously.