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Artificial intelligence (AI) is perhaps the hottest topic in business, but what are its possibilities for HR?

HR leaders can adopt AI to significantly enhance and personalize the employee experience. Use AI to tailor both HR programs and outreach to individual needs and preferences, supporting the engagement and retention of key talent. This personalization can apply across all functions of HR, from triggering automatic nudges to combat implicit bias, to providing networks for new hires, to suggesting rewards and compensation packages. 

But while the potential of AI is enticing, HR leaders must also closely consider its inherent risks of bias and ethical challenges. Understanding what AI cannot do is as important as appreciating what it can. Artificial Intelligence for HR: Separating the Potential from the Hype gives HR and other business leaders a basic, nontechnical foundation in AI and a deeper understanding of what AI is and isn’t. With this knowledge, they can recognize AI hype in the marketplace and realistically assess where AI might make a difference in HR’s capabilities. With a grasp of the fundamentals of AI, HR will become a more valuable partner in thinking through the optimal mix of people and technology to deliver business results. 

Explore this series of practice-focused reports to explore the current capabilities, suggested safeguards, and potential applications of AI across multiple HR functions: talent acquisition; onboarding; total rewards; executive compensation; coaching; diversity, equity & inclusion; career management and talent mobility; learning & development; and HR technology & operations.


Core uses for AI in HR

  • Increase the efficiency of recruitment processes.
  • Improve employment branding.
  • Bolster recruitment marketing.
  • Improve the hiring experience for both candidates and hiring managers.
  • Optimize sourcing.
  • Enhance the quality of the candidate pool.
  • Track when new hires have completed required training.
  • Point new hires to coworkers they should connect with, based on their respective skills, experience, and jobs.
  • Recommend relevant development opportunities.
  • Personalize onboarding experiences.
  • Decrease the regrettable attrition of new joiners.
  • Enhance overall onboarding and work experience.
  • Improve the work productivity of onboarding professionals by taking over administrative, repetitive tasks.
  • Provide real-time, personalized total rewards communication.
  • Conduct frequent sentiment analysis.
  • Leverage AI to set up sales quotas to improve work productivity.
  • Optimize earning by making better informed decisions.
  • Construct sales territories that are efficient and equitable.
  • Validate payment accuracy.
  • Simplify job evaluations.
  • Efficiently benchmark compensation.
  • Alleviate pay equity.
  • Make customized health-benefit recommendations to individual employees, based on their personal profiles, family circumstances, past usage, and other factors.
  • Mine qualitative data, such as online discussions and open-ended polling questions, to track employee sentiment and identify organizational issues that could affect attrition.
  • Select performance measures that will indicate management’s success in delivering annual and long-term business results.
  • Broaden access to coaching.
  • Support coaching conversations.
  • Augment the management of coaching initiatives across the organization.
  • Identify recommended coaching topics that would accelerate time-to-productivity for new hires in a specific role.
  • Realize scalable impact by analyzing massive amounts of data on job roles, career paths, and employee development opportunities, providing recommendations for the highest likelihood of employee success.
  • Listen in on online sales presentations and provide sellers with real-time, pop-up suggestions to improve their pitches.
  • Suggest compensation packages based on performance data, external market trends, and comparable internal data, to ensure managers award equal pay for equal work.
  • Nudge managers to avoid referencing stereotypes or using biased language in performance reviews.
  • Select candidates from large pools of applicants based on objective criteria, mitigating the impact of decision-making bias.
  • Match employees’ skills and experiences to future role openings to widen the pool of potential candidates and internal mobility.
  • Accelerate and scale up internal career moves and tailored leaning plans.
  • Identify talent gaps, uncover training needs, and embrace agile working.
  • Create customized career paths, complete with required skills and learning to reach desired roles.
  • Ensure all qualified candidates are considered for internal or geographic mobility.
  • Provide greater transparency for what options exist for employees.
  • Enhance the delivery of curated, seamlessly integrated learning content to meet the needs and expectations of the learner.
  • Guide learners in real-time to the information they need to perform their job.
  • Create insights from millions of data points to make informed, strategic decisions about the effectiveness of learning across the organization.
  • Analyze job-related data to predict how jobs may change over time.
  • Analyze real-time data from multiple sources and then add or subtract resources from projects, manage deadlines, schedule meetings, take notes, and do basic follow-up.
  • Enhance employee self-service for questions on benefits, payroll, or transitions.
  • Define skills and build employee profiles used in talent management practices.


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