Current State
Finding the right talent for the right job has never been as challenging as it is today. Unemployment in the U.S. is hovering around 4% or less at the time of this writing. That translates into fewer workers available for open jobs. There has always been concern about using machine learning and/or artificial intelligence in talent acquisition (TA) technology because of the risk it may cause undesired effects. The profession has relied heavily on technology to gain efficiencies, but what has resulted is a kind of black hole into which qualified candidates sometimes fall. New ways of working are leading companies to create jobs that never existed before or requiring candidates to have new skills that have never been needed before. Every change to a hiring requirement puts even more pressure on an already narrow talent pool.
Complications
The emergence of Generative AI gives organizations a host of new tools to hire great talent, but brings the risks of bias, data security and unethical practices. TA teams must maintain their vigilance to ensure their talent pools remain diverse and inclusive and are ready to comply with unfolding regulations on the use of AI and machine learning in hiring decisions. While emerging innovations offer promise, realizing their potential requires holistic workforce strategies. With diligent planning and a focus on human intelligence, businesses can navigate talent acquisition’s complexities to build a capable, future-ready workforce.
Consequences
As the entry point to the employee lifecycle, employers simply can’t afford to get TA wrong on any level. The risks of inaction span financial, cultural, regulatory and competitive dimensions. As skills needs escalate, outdated hiring practices will leave roles unfilled for longer, costing money in the form of increased contract labor costs or loss of productivity. Lagging recruitment technology will allow rivals to outmaneuver and out-scale. In today’s demanding talent market, organizations that fail to meet change with courage cement their eventual decline
Critical Question
- Which processes can benefit from AI, ML, and RPA capabilities?
Brandon Hall Group™ POV
Audit Talent Acquisition Technology for Alignment with Organizational Goals
Talent acquisition technology often is used to improve usage rates, time-to-fill, and cost-per-hire. These are all important metrics, but it is also necessary to align TA goals with organizational values and goals. Sometimes simply hiring as many people as fast as possible will hurt the candidate experience, which will cause the organization to miss its overall long-term goals.
Verify that Automation is Working as Intended
TA has long been the leading edge in the application of automation, machine learning and AI technology. In that push to automate and adopt, many process decisions were left to technology and haven’t necessarily been validated by TA professionals. With even more automation and AI capabilities being introduced across the spectrum of work, now is the time for TA leaders and their teams to validate that processes and automations are working as intended (not just as designed). Regular review of decision filters and “rejected” candidates is key to ensuring algorithms are delivering the best candidates without bias. Leverage the efficiency that comes from additional technical capability, but pay attention to how it is working and be diligent in evaluating the overall effectiveness so changes can be made sooner rather than later to minimize any potential damage
Automate All TA Processes, Not Just the Early Stages
The current use of AI and ML-backed technology is in the early stages of the TA processes – sourcing and screening. This leaves post-hire TA processes sadly in need of more automation because the onboarding processes are just as ripe for improvement through the use of more modern systems. There is a great opportunity to leverage AI and ML for post-hire processes. Organizations should expand the use of AL and ML technologies into the post-hire process.
Candidate Experience is Primary
Talent Acquisition has always been the place where automation has found an early home. AI is no different, expanding the efficiency to be gained during the hiring process. Ensuring the candidate’s experience is easy, informative and encouraging even if candidates don’t interact directly with a human until midway or further through the process is key.
Treating every candidate like a potential employee, no matter how far they progress on any single job opening creates a sense of desire to belong for the candidate. Long-term relationships with candidates will be the competitive advantage of the future.
Take Steps to Eliminate AI-Driven Discrimination
Using automated and AI-driven selection processes, hiring for remote-first work environments and the decentralization of work will put increased pressure on employment regulations/laws. Most employment law is geographically based. How do you ensure fair and equitable practice when geography is no longer the primary consideration in hiring? AI-driven discrimination in hiring will require deeper scrutiny. There is already a New York City ordinance around the use of AI algorithms in hiring. The first fines for discrimination were issued in late summer 2023 under existing discrimination laws. New AI regulation will only increase the level of scrutiny.
