At New Era Insurance, Ellen, a seasoned project manager, was in a quandary. Jen, a key team member, decided to leave midway through a pivotal project. The departure threatened to derail their timelines and disappoint the stakeholders. However, equipped with the new capabilities of their generative AI-driven HR system, Ellen was optimistic about handling the turnover and facilitating a quick return to normalcy.
When Jen submitted her resignation, Ellen logged into the company’s enterprise AI platform, code-named “HR-GPT.” She entered the job role and project specifics. Within minutes, HR-GPT pulled up a list of potential candidates from both internal databases and external job platforms. The AI system screened résumés, matching candidates with the required skills and expertise for the project.
Ellen received notifications of top candidates, complete with AI-generated interview schedules based on Ellen’s availability, that of the candidates, and available conference rooms. HR-GPT even prepared a set of interview questions tailored to assess the candidates’ skills in relation to the project’s needs. This saved Ellen hours of preparation.
After the interviews, Ellen felt a bit torn between two candidates. She turned to HR-GPT again, which provided an analysis comparing the candidates based on their responses, past job performances, and fit with the company culture. This made Ellen’s decision more straightforward.
The brave new world of hiring described in this book about how Gen AI will change the game for project managers is already upon us. It is how the best qualified candidates are summarily screened out by AI. Resumes can be written for humans with experience, the ability to connect dots between things, spot signals good and bad based on their lived experience in the world as a human being. A good recruiter has a very strong BS-meter and can screen out questionable candidate even on a short phone call. The scenario here fails on all counts when compared to what capable human recruiter can do. Yes, the process is efficient and you are hire and fire at unprecedented scale without obviously impacting the rhythm of the business. The book proceeds to describe exactly that utopia next:
Fastforward: once the new employee, Max, was selected, HR-GPT moved into onboarding mode. Max received a series of personalized tutorials about the project. These tutorials, generated by the AI, came from documentation, past team discussions, and actual code snippets to help him understand the project’s current state. Such onboarding can take a significant amount of time away from the project team.
On Max’s first day, he didn’t wander around looking for supplies or access permissions. HR-GPT had already set up his workstation, granted him access to necessary files, and even scheduled a virtual meet-and-greet with the team.
Ellen watched with satisfaction as Max quickly integrated into the team, armed with insights and knowledge that typically took weeks to accumulate. The project not only stayed on track, but also thrived with fresh energy.
Starting from the match-making step all the way to the enablement of the new-hire relies on data being complete, accurate, timely and just perfect in all other ways possible. I found the on-boarding phase particularly intriguing having been in situations myself where I had to come up speed by end of the first day and placing people in that position because of delivery timelines. The process is a bit messy and organic - the problems that the new hire will need to roll up their sleeves and solve do not arise from well-defined or well-controlled sources. If they have any experience at all they know if the solution is fairly obvious to them and has not been implemented chances are it is impossible to implement - people are not stupid or crazy.
So you give the person a lay of the land, a sense of the power-dynamics, the idiosyncrasies of people and teams involved and the multifarious sources of technical debt - some of which cannot be paid-off in the foreseeable future. There is no sense in paying someone good money for their experience, if these were not infact the problems that they needed to solve within 24 hours of having their access situated. It's all dandy to dream up utopias with the aim of writing a book that needs to sell but real life as practitioners know does not work that way.
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