HR and TA leaders can experience many benefits by implementing AI, but it’s critical to adopt a change management strategy before deploying the technology.
By Maggie Mancini
AI is reshaping talent acquisition by enabling recruiters to identify top candidates more efficiently and make data-driven decisions now and for the future. Pramod Patil, chief technology officer at Orion Talent, says AI-powered skills mapping, automated candidate outreach, and predictive analytics not only speeds up the hiring process but also enhances the overall quality of hires with recruiters making more informed decisions.
“AI-powered tools can automate candidate outreach, enabling recruiters to cast a wider net and attract diverse candidates,” Patil says. “This increased funnel size enhances the likelihood of finding the best-fit candidate for any role.”
In addition, applying natural language processing and skills ontologies allows AI to make connections between skills on resumes with job descriptions. This process results in a larger, better talent pipeline without wasting any of recruiters’ time.
And as organizational data accumulates over time (think candidate profiles, performance management, demographic trends), Patil says AI-driven predictive analytics can translate this data to get a better understanding of which candidates are most likely to succeed or the amount of time it will take to fill a role.
With all of these benefits at the ready to be uncovered, Patil advises a thoughtful, multifaceted strategy in order to reap the most reward. Here are some key considerations for HR and TA leaders.
- Adopt a change management strategy. Coupled with clear communication, change management helps build a shared vision for how AI will enhance—not replace—human talent.
- Invest in data infrastructure. “AI systems are only as good as the data they consume,” Patil says. “Ensuring data accuracy and integrity prevents misguided insights and suboptimal outcomes.”
- Evaluate existing processes. Ensure that the chosen processes can benefit from automation without compromising quality, Patil explains.
- Establish outcome metrics and guardrails. “Define what constitutes acceptable and unacceptable outcomes from AI interventions,” Patil says. By taking this step, leaders can manage risks and mitigate issues before they escalate.
- Train and upskill employees. Training not only alleviates job security concerns but also maximizes the technology’s potential by ensuring workers are comfortable and proficient in leveraging it, he adds.
- Provide support for continuous monitoring. “Implement robust monitoring practices to regularly review AI outputs, detect anomalies, and fine-tune based on real-world performance,” Patil says.
- Develop a risk management strategy. This strategy should include regular audits and performance reviews. Outcomes should be measured against defined ROI metrics to ensure the initiative delivers value and aligns with business goals.
