Appropriate AI 1: Job Opening Aggregator (JOA)
I post on coaching young professionals in project settings. Recently, I noted that the initial goals that they tend to have these days are no longer centered around project management as they had been in the past. Instead, they want to:
Leverage AI Appropriately
Get a (Better) Job
Improve Relations with Boss (e.g. initiate, repair, enhance, exploit)
Facilitate Joint Design (with external stakeholders)
I am happy to report that young professionals (YP) with initial goals like these do find ways to “leverage AI appropriately”. Many ways, in fact. I will be sharing some here, tagged as “Appropriate AI”.
These days, many YP’s come to coaching in order to get a (better) job. Every one whom I have coached recently sees AI as a partner. They leverage it in their job searches in many different ways. One way that I see it being used – often and effectively – is in the specific role of a Job Opening Aggregator (JOA). I’ll introduce that role here as an example of “Appropriate AI”.
This JOA role is to discover job openings that have a great fit to the YP and are current. While YP search strategies and career development practices vary widely, many use a JOA, recursively, in the general pattern set out here.
Design AI prompt as JOA in order to surface, extract, and aggregate job openings that fit the YP.
Have JOA search for, extract, and aggregate openings per design
Study aggregated job openings for patterns that may usefully inform action on a) current iteration and b) design of next iteration (e.g., redesign of JOA)
Act on each selected, current opening: apply, adapt, or abandon
The action of phase 4 hopefully leads to progress in the job search, such as an invitation to interview. Whether or not this occurs, each successive iteration of a practice like this incorporates lessons learned from preceding iterations and leverages them. In the “Study” phase, YP discover patterns from which they learn more about themselves, the job market, their current JOA, and their current practice. Such learning can inform powerful revisions to the JOA prompt going forward. Examples are revisions to sources of job openings to search, to criteria for selecting openings that fit the YP, and to the format in which selected openings are provided to the YP for study.
A couple of YP’s have recently come to my coaching having already built a JOA as part of a strong career development practice. However, most YP’s searching for a job come to the coaching engagement frustrated. They have been relying long term on a single platform (e.g. LInkedIn) or on a single service (e.g. expert resume writing). At the same time, they have been talking a lot with a lot of AI partners, but these talks keep turning into rabbit holes. Fortunately, in the coaching, I see these same YP’s go on to build a robust career development practices, ones that incorporate services, platforms, and appropriate AI.
Their first step in building a robust practice that includes “appropriate AI” is very often engineering an AI prompt as a JOA. This YP’s are able to do with two basic resources: with a single strand of conversation with, say, a Claude or ChatGPT chatbot, and with a basic prompt formula, such as RTF (Role, Task, Function). The Role portion of the JOA prompt can be informed by the pattern of practice the YP aims to build, such as the four phase pattern above. The Task part instructs the JOA to a) look at specified sources of job openings (e.g. LinkedIn and job boards of ideal employers) and b) select those that fit the YP criteria. The Function part instructs the JOA as to how selected openings should be provided for study. Of course, based on what YP’s learn in the Study phase of any given iteration, going forward, they can modify role, task, and function or this prompt formula itself.
A single iteration of a practice including such a JOA – by YP’s at any level of technical skill – can produce wonderful things. From the start, they can aggregate job openings that are current and seem to have a great fit. That said, by any technical configuration of a JOA, it takes time and work to complete a single iteration of this sort of practice. YP’s tend to do so in timeframes ranging from about a week to about a month. What seems to accelerate the learning – and to shorten the time to landing a position – is not the technical power of the JOA. Rather, is the quality of the YP’s practice in the “human-in-the-loop phases of “Study”, “Act”, and “Design”. (More on these in upcoming posts, especially since YP’s have found ways to leverage AI in these phases as well.)
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 practice fail to have positive outcomes for the YP. 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.
More than ever, career development these days requires both a “journey in”, to the center of one’s being, as well as a “journey out”, to job market turbulence. To navigate these concurrently, it takes a practice, a systemic practice that includes “appropriate AI”, a practice which can start and grow by a JOA.