AI in Talent Management: 6 Ways for HR Pros to Harness the Power of Machine Learning
Artificial intelligence comes with plenty of promises. The promise of eliminating boring, repetitive work. The promise of generating ideas. And the promise of speeding up your decision-making. But these promises are just scratching the surface of how AI can support your talent initiatives.
Think about it—the HR landscape is vast and intricate. Talent management? Even more so. You've got data coming in from all directions – resumes, performance evaluations, employee feedback, engagement surveys. It's a goldmine waiting to be tapped.
But who's got the time? You do, with AI on your side.
When people teams leverage AI in talent management, the technology enables us to become a strategic partner for our respective businesses. That doesn't mean handing over critical HR decisions to the bots but instead using AI's intelligence to inspire our next moves in shaping the employee experience.
This guide covers:
- The benefits of using AI in talent management.
- How people teams worldwide already use it.
- When to overrule the algorithms with some human thinking.
🕵️♀️ 5 Reasons why talent management needs AI
Talent management is a delicate process encompassing the entire employee lifecycle, including:
- performance management.
Each of these stages must run seamlessly for your talent to be satisfied and successful, and HR professionals are already leaning on AI to assist:
- 92% of HR leaders state they're moving ahead with AI in at least one major area (Eightfold AI)
- 79% of employers use AI or automation to recruit and select top candidates (SHRM)
- 80% of Chief Human Resource Officers agreed that generative AI has the potential to revolutionize talent management practices. (Bain & Company)
Here's how AI is already taking talent management by storm:
1. Data-driven decision making
Traditional HR practices have gotten so far by using gut instincts and word of mouth to make key operational decisions. But data-driven HR is the modern approach.
When people teams collect and process data from multiple sources, they gain insights that would take a human weeks or months to uncover. Data is accurate and empowering, enabling HR leaders to make decisions more confidently, leading to better outcomes for the company and its employees.
You probably already have plenty of data to comb through—pulse surveys, turnover figures, L&D program participation, engagement surveys, etc. Here, AI can analyze your existing data sets and find trends to support your decision-making.
Alternatively, if you need AI to generate new data points, you can run experiments and simulations to gain the insights you need. AI does this by exploring multiple alternatives to find the best possible outcome for your company.
2. Predictive analysis
AI excels at using data to predict future outcomes, a skill that not all HR specialists possess.
For example, you might use it to:
- Predict the probability of a candidate accepting an offer.
- Forecast employee performance once they're on the job.
- Anticipate worker retention rates.
By using data to support workforce planning, HR leaders can model the potential impacts of different decisions, for example:
- How would an increase in promotion opportunities affect employee retention?
- What would happen if we changed L&D programs to focus on specific skills?
- How would implementing AI-powered performance management impact employee engagement?
Predictive analytics in HR allows us to proactively address any issues before they become serious problems. In this way, AI helps us stay ahead of the competition by quickly adapting to changing market conditions and talent needs.
3. Administrative task automation
For all the doubt and scaremongering about artificial intelligence, there's also giddiness about the technology's potential.
Microsoft's 2023 Work Trend Index highlights that 70% of employees are excited that AI will reduce their workloads.
By automating routine tasks such as resume screening, scheduling interviews, or data entry, AI frees up HR teams to focus on more strategic initiatives. Automation is a time saver for HR and a way to eliminate human error and bias in administrative tasks.
4. L&D personalization
Effective employee development should always focus on individual targets and career aspirations. But with hundreds or thousands of employees, manually creating tailored programs for each worker can feel like climbing Mount Everest. AI technologies can build individual profiles for every employee using data from performance reviews, skill assessments, and achievements.
Your AI tools then generate personalized training plans, learning suggestions, and development goals for each employee at the click of a button.
5. Skill gap analysis
People teams often lack a clear view of their workers' skill sets. If you're struggling to understand where to concentrate your upskilling and reskilling initiatives, AI can automatically analyze employee profiles and compare them to current job requirements. This skills gap analysis enables businesses to identify critical gaps that may require immediate attention and create targeted training programs or source external candidates with specialized skills.
➡️ Learn how to revamp your people operations with these essential AI tools for HR.
🤖 How is AI used in talent management: 6 Major use cases
AI excels at providing context to HR data, so it's easier for leaders to make informed decisions on all aspects of their talent management strategy. From recruitment and performance management to employee engagement, here's how AI enhances the entire employee lifecycle.
1. Talent acquisition
Traditional recruitment practices have been riddled with bias. Customs like resume screening and panel interviews are highly susceptible to human error, leading to poor recruiting decisions and a DEI nightmare, as hiring managers unwittingly invite more of the same to join their ranks.
Today's AI-powered recruitment systems eliminate bias using:
- Predictive analytics to determine the best-fit candidates
- Virtual AI-driven interview platforms
- Intelligent shortlisting for potential candidates
The technology uses predefined selection criteria during screening, ensuring a level playing field for all applicants. In this way, AI bridges geographical divides, cultural differences, and time zones, giving you access to a larger and more diverse pool of candidates.
Onboarding is a fragile stage of the talent journey as new joiners get to know their colleagues, job requirements, and the company culture. AI can help by automating much of the admin in preparing for day one and the coming weeks. Pre-boarding chatbots, for example, can answer common questions and guide new hires through the initial round of form filling.
➡️ For more details, check out our comprehensive guide to using AI in employee boarding.
3. Talent development
Using AI in learning and development ensures that employees continually acquire the skills and knowledge they need to excel in their roles. Machine learning plays a pivotal role in optimizing this process, making it more personalized, efficient, and effective by:
- Conducting individual training needs assessments.
- Customizing training materials based on specific employee development areas.
- Delivering competency-based coaching.
➡️ Learn how an AI-based learning management system (LMS) is ideal for unleashing the potential of every learner in your organization.
4. Performance management
Once your hiring teams have recruited the right people into the right seats and have set them up for success with their learning and development plans, the next step is to manage and measure their employee performance.
AI supports this process by:
- Analyzing a continuous stream of employee data to identify trends and patterns in performance.
- Giving growth recommendations based on feedback.
- Summarizing feedback from multiple sources and highlighting the most impactful development areas.
- Offering predictive insights to help managers make better decisions about promotions, compensation, and career development opportunities.
➡️ For more information, check out our comprehensive guide to AI in performance management.
5. Talent engagement and retention
AI contributes to employee engagement and retention efforts by automating tasks like employee surveys, pulse checks, and exit interviews. With the help of natural language processing (NLP), AI can analyze unstructured data from these interactions to:
- Understand engagement trends and proactively address concerns.
- Gauge employee sentiment.
- Analyze patterns to predict potential employee turnover.
- Measure employee engagement.
➡️ Learn more about how to leverage AI in your employee engagement initiatives. Or, if reducing attrition is your goal, check out our guide to using AI for employee retention.
6. Succession planning
Identifying and developing future leaders ensures you invest in your business today to prepare for tomorrow. Although some talent acquisition teams may need to find external hires to fill leadership positions, it's more cost-effective to commit to succession planning within your internal org chart.
AI's data-driven insights support HR teams in identifying high-potential employees who are an excellent match for leadership roles. The technology analyzes employee performance data and career progression to spot those demonstrating excellent examples of leadership skills.
🤝 3 Reasons why human-AI collaboration in talent management is critical
The benefits of using AI-powered tools to support talent management are undeniable.
However, as powerful as AI is, it's not a magician. It needs the correct data, the right questions, and the proper human interpretation. That's where you, the HR professional, step in. AI models aren't intended to replace your people function, but they're a fantastic aid.
So, here's why we always recommend leaving humans in charge of AI-backed decisions:
1. Addressing ethical concerns
While AI sounds like a great tool for weeding out bias from the recruitment process, it's not that simple. AI systems are built using training data sets. If you use them during screening, this historical data will define success in specific job roles.
For example, suppose high performers are typically white women aged between 30 and 45. In that case, your AI screening tool will continue to look for candidates in this demographic. Your tool might do this quicker and more efficiently than you previously handled the task. However, the output will still be biased and unethical.
As pointed out in a Harvard Business Review paper on Building Ethical AI for Talent Management,
"If the training set, the data, or both are biased, and algorithms are not sufficiently audited, AI will only exacerbate the problem of bias in hiring and homogeneity in organizations."
"Integrating AI into HR practices offers efficiencies and data-driven insights, but it does pose the risk of dehumanizing the very essence of Human Resources. It is about balancing AI and the irreplaceable "human touch" that can maintain empathy, understanding, and cultural cohesion.
For instance, we recently promoted an AI recruitment selection tool. It could detect, among other things, whether a candidate was overweight and automatically exclude them from jobs where fitness was important. This is a frightening prospect, as it could be a predetermined criterion for AI, highlighting why people should be involved in assessing human factors that a machine callously disregards."
2. Handling sensitive employee issues
With free, open-source AI tools like ChatGPT available to the general public, it's easy for HR pros to dive in and use the technology without thinking about the consequences.
One is the significantly inadvisable step of sharing sensitive data with a robot, which even ChatGPT advises against.
There are data protection implications to inputting certain types of employee data in an AI tool. Depending on your location, you may need to adhere to General Data Protection Regulations (GDPR) in Europe or various US state privacy laws such as the California Privacy Rights Act (CPRA.) To ensure confidentiality, use compliant human-based practices to protect your employees and their personal data.
3. Creating synergy within your talent management strategy
Effective talent management comprises various pillars, and human insight is invaluable to ensure these pillars work seamlessly together. Human resource professionals can connect the dots between different stages of the employee lifecycle by using:
- Talent acquisition insights to inform the design of onboarding processes and better integrate new employees into the organization.
- Performance management data to guide talent development initiatives and identify areas where training and coaching are most needed.
- Engagement data can provide input into succession planning, as engaged employees are more likely to take on leadership roles.
By using AI for specific functions within each pillar, HR professionals have more time and resources to connect these functions cohesively across the talent management spectrum. However, human professionals have the contextual knowledge to ensure that the strategies employed in each pillar align with the organization's overarching goals and values.
"If you do not understand your people, and you do not understand the job or the work, the matching algorithm is not a silver bullet. People think AI is this thing that's going to overnight, make everything amazing. AI needs information; it needs information in order to match people to work."
➡️ Implement next-gen talent management with Zavvy AI
In a world where agility and adaptability are key, talent management is undergoing a transformative shift. Integrating Artificial Intelligence (AI) in Human Resources is no longer a distant dream—it's a tangible reality.
Zavvy AI stands at the forefront of this revolution, redefining the talent management landscape. By infusing AI into the core of talent development, Zavvy AI doesn't just enhance processes—it revolutionizes them.
Here are some features that will supercharge your talent management processes in combination with the skills of your exceptional people teams.
- 🤖 Zavvy AI is a 360-degree growth system that allows you to run development programs 10x faster than your traditional, manual approach.
- 🧑🎓 AI training software allows you to create entire training courses, including microcourses, workshops, self-paced courses, and more, in minutes.
- 🌱 AI growth plans bridge the gap between individual employee goals and tailored training resources.
- 🧭 Career frameworks are the foundation of your talent development, which you can create in just a few clicks to define competency libraries, job leveling, and consistent job descriptions.
- 📊 AI feedback provides structured, growth-oriented reviews that link to development plans and training courses.
📅 Ready to hit the future running with our next-gen talent management software? Book a free Zavvy demo today.
How is AI used in recruitment?
Recruiters can use artificial intelligence for various standard recruitment tasks, including sourcing candidates, screening resumes, and conducting interviews. The technology may save time and resources by automating repetitive tasks and providing data-driven insights for predictive hiring decisions.
What is the future of AI in talent acquisition?
As AI advances, its role in talent acquisition will only expand as recruiters rely on it even more heavily. We expect a greater focus on removing bias from training data sets and access to trending information. For example, ChatGPT can now access real-time information from the internet, where previously, it could view data up to September 2021. This is a significant change, meaning recruiters can now scour current open vacancies, up-to-date salary information, and the latest benefits and trends to entice candidates.
➡️ Check out our broader discussion about the future of AI in Human Resources.