Enterprise operation and performance require the right talent. Talent acquisition functions best when there is a positive public reputation, good and accurate external descriptions of work (external), and detailed internal work descriptions (position and jobs) exist. AI has a place in aiding both attraction and Acquisition.
At a high level, work environment reputation comes from PR management of public websites (like Glassdoor), review websites (such as for products or services), general social media perceptions (the category of ‘hedge fund’ is positive and negative depending on what the reference is about), and particular interests for workers. Reputation assessment, evaluation, and management can employ AI agents to gather social media postings and metadata, use natural language understanding to evaluate data in a context and generate a synopsis.
Public use of titles (“Chief Accountant” has different connotations and status than “Chief Beancounter” or “Chief Financial Officer”) in social media (LinkedIn, Meta, others) and documents. Individuals picking their titles (“Chief Tweet”) might have whimsical or very serious reasons. In certain industries (movie-making, scientific studies, academics) titles are really serious signals of talent attractions. Gathering up large databases and metadata can be processed by language models to determine the perquisite capabilities and job functions correlated with titles).
The talent acquisition process has employed matching algorithms for decades. Unfortunately, for recruiters and talent acquisition, rarely does the ‘perfect’ candidate actually exist for a specific position. Much time effort, and rule-based processing, go into building a list of qualified candidates suitable for a specific position. The same style of rule-based systems provides screens for applicants by eliminating from consideration those not meeting stated prerequisites or other mandatory preferences.
These applications of rules and AI functions have led to some observed issues being resolved. Here are some challenges of using AI in the attraction and acquisition process:
A) Reputation challenges - Postings online (like Glassdoor and others) are made by opinionated people and may have little to do with actual candidates' or employees' experience. The mismatch between reputation online and espoused opinions can be large.
B) AI produces a perfect search description (that no one fits) - The pool and qualities of candidates presented after rule-based screening are narrow and include candidates with likely false credentials. (A master’s degree at 16? Possible, not likely.) Determining authenticity and authoritative data can be difficult when candidates are motivated to lie.)
C) AI deals with a chaotic hiring environment - The sequence of steps from application to final hire can be driven by an AI-style application. Moving people along this sequence is often difficult and doesn’t follow rigid schedules. Workflows and steps are disrupted as uploads get lost, applicants in different time zones don’t necessarily respond in a ‘timely’ fashion, and follow-up questions aren’t answered in an expected fashion.
We’ll be covering other topics regarding HR aided by AI. You can find this and other posts at www.ekalore.com/bad-project-blog
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