Written by: Jessica Kim-Schmid and Roshni Raveendhran
c.2022 Harvard Business School Publishing Corp.
Artificial intelligence tools for talent management have the potential to help organizations find better job candidates faster, provide more impactful employee development and promote retention through effective employee engagement. But AI implementation comes with a unique set of challenges that warrant significant attention.
Leaders need to understand how and where AI might offer their company an edge, and how to anticipate and tackle core challenges in implementing AI for talent management.
Talent Management Pain Points and AI in Action
Talent management has three main phases: employee attraction, employee development and employee retention. AI can help address pain points in each of these areas.
Employee Attraction
Finding and hiring the right workers can be labor intensive, inefficient and subject to bias. AI can help by creating job postings that are appropriately advertised to prospective candidates, efficiently screening applicants to identify promising candidates and offering processes that attempt to check human biases. For example, the platform Pymetrics utilizes AI in candidate assessment tools that measure actual skill demonstration and reduces bias in the screening process as a result.
Employee Development
Offering workers ongoing learning and development opportunities is an essential aspect of talent management. A key pain point here is motivating employees and ensuring they have access to appropriate opportunities. AI can offer real-time solutions. For example, EdApp, an AI-based learning management system, provides employees with personalized learning recommendations based on performance and engagement analytics, allows human resources leaders to create micro-learning content within minutes and enables them to track learner progress and revise content based on analytical insights.
Employee Retention
Finally, there’s the question of how to ensure that the employees you hire and develop stick around. A critical aspect of this is employee engagement. A recent Gallup survey shows that only 21% of the global workforce feels engaged at work.
Various AI tools can help capture employee engagement metrics accurately in real-time and create employee-focused solutions for promoting well-being. One example is Microsoft Viva + Glint, an employee experience platform that combines sentiment analysis with actual collaboration data to gauge employee engagement and well-being.
Where AI Tool Can Go Wrong – And How to Mitigate The Risk
To leverage AI’s full potential for talent management, leaders need to consider what AI adoption and implementation challenges they may run into. Below, we describe key challenges as well as research-based mitigation strategies for each.
Low Trust In AI-Driven Decisions
Research shows that people often mistrust AI for three main reasons: they don’t understand how AI works, it takes decision control out of their hands and they perceive algorithmic decisions as impersonal and reductionistic.
Mitigation strategies include:
Fostering Algorithmic Literacy: One way to reduce algorithm aversion is to help users learn how to interact with AI tools. Talent management leaders who use AI tools for making decisions should receive statistical training, for instance, that can enable them to feel confident about interpreting algorithmic recommendations.
Offering Opportunities For Decision Control: Research suggests that when people have some control over the ultimate decision, they are less averse to algorithmic decisions. People are also more willing to trust AI-driven decisions in more objective domains. Therefore, deciding which types of talent management decisions should be informed by AI, as well as determining how HR professionals can co-create solutions by working with AI-driven recommendations, will be critical for enhancing trust in AI.
AI Bias And Ethical Implications
While AI can reduce bias in decision-making, AI is not entirely bias-free. AI systems are typically trained using existing data sets, which may reflect historical biases. Given AI’s vulnerability to bias, applications of AI in talent management could produce outcomes that violate organizational ethical codes and values, ultimately hurting employee engagement, morale and productivity.
Mitigation strategies include:
Creating Internal Processes for Identifying & Addressing Bias In AI: To mitigate bias in AI technologies, it is important to create internal processes based on how the organization defines fairness in algorithmic outcomes and to set standards for how transparent and explainable AI decisions within the organization need to be. Leaders should be cautious about setting fairness criteria that do not account for equity, particularly for vulnerable populations. To address this, leaders can consider including variables such as gender and race in algorithms and set different criteria for different groups to address preexisting biases.
Building Diverse Teams to Design AI Systems: Research indicates that more diverse engineering teams create less biased AI. By fostering diversity throughout AI design and implementation processes within their talent management function, organizations can draw on diverse perspectives to minimize AI bias.
Erosion of Employee Privacy
Organizations have deployed AI technologies to track employees in real-time. If implemented poorly, these tools can severely erode employee privacy and lead to increased employee stress, faster burnout, deteriorated mental health and a decreased sense of agency.
Mitigation strategies include:
Being Transparent about the Purpose & Use of Tracking Technology: Gartner Research reveals that the percentage of employees who are comfortable with certain forms of employer tracking has increased over the past decade. The increase in acceptance is much higher when employers explain the reasoning for tracking, growing from 30% to 50% when organizational leaders transparently discussed why these tools were being used.
Making Tracking Informational, Not Evaluative: Recent research has discovered that employees are more accepting of tracking when it is conducted solely by AI without any human involvement. Technological tracking allows employees to get informational feedback about their own behavior without fear of negative evaluations. When tracking tools are deployed primarily for monitoring, they erode privacy and reduce intrinsic motivation. Therefore, the key consideration for leaders should be whether tracking can enhance informational outcomes for employees without causing evaluation concerns.
Potential for Legal Risk
National and international laws governing employers’ and employees’ AI-related rights and responsibilities are constantly evolving.
Mitigation strategies include:
Understanding Current Legal Frameworks Regulating AI Use: It is important for leaders to stay informed of changing regulations, especially when operating businesses in multiple locations.
Establishing A Proactive Risk Management Program: Organizations should create risk management practices, such as designing AI systems with appropriate controls at various stages of the model development process.
AI tools can make managing talent easier and fairer, but the implementation process comes with challenges — and if leaders want to get the most out of these tools, they need to remember that.
