Artificial Intelligence (AI) for HR is still in its early stages, but it can provide new efficiencies and greater effectiveness around the fundamental human process of engaging with, training and retaining the most critical of all resources—people.
If you’re an HR executive interested in how AI can benefit your company, you do not need to understand all of the precise details about the programs, technologies and applications that comprise AI. Instead you should think about AI from a business perspective by asking yourself these questions:
- What is the business value of the initiative?
- What processes is your organization trying to support?
- What needs to be in place to make it work?
Notice that I did not include “AI” in any of these questions. That is because these questions need to be answered for any technology that you are trying to bring to your organization. AI is not magic, and it needs to be understandable as a tool for humans just as any other technology needs to be.
How AI Can Help HR
One way to think about how AI can help is to consider the point of view of the employee lifecycle. This is analogous to how sales thinks of the customer lifecycle—since employees and prospective employees are in fact the customers of HR. Customer lifecycles can vary from industry to industry and even company to company within the same sector, so consider the lifecycle described below as just one example. Mapping the employee lifecycle will help to answer the first two questions. Later in the article, I’ll discuss what needs to be in place to make it work.
Generally speaking, most HR functions have to do at least some of the following in order to meet the needs of the business:
Sourcing: Prospective candidates need to learn about your opening and your organization. That means reaching the right audience and getting job descriptions and openings in front of the right people. AI makes this process more effective by presenting job openings to the right target candidates, resulting in more appropriate candidate applications. AI can programmatically buy advertising that will reach specific demographics, industry segments and interest profiles. AI tools can also adjust phrasing and language in advertisements to optimize engagement.
Screening: Candidate screening is labor intensive and tedious. Basic text analytics tools (a form of AI) use pattern-matching to identify needed skills and education and to screen out applicants who do not meet the basic requirements. A more nuanced approach is to use past successful hires to identify candidates by matching resumes against more subtle patterns. This approach depends on having a large number of resumes that represent the qualifications of ideal candidates along with historical information about the performance of people with those qualifications.
Matching: Determining the fit of candidates to jobs requires that they be assessed across multiple dimensions. Many flavors of assessment can address different criteria such as behavior style, communication approaches, propensity to take risks, learning style and ability, and technical and cognitive skills. The value of these types of assessments is in understanding how candidates will interact with supervisors, managers and coworkers and whether their soft and hard skills will match the needs and style of the role, position and team.
AI compares the candidate’s assessment with the results of past or currently successful people in the same job. For example, one would not put an introvert in an outbound sales position or place a creative problem solver who eschews formalities and rules in a position that requires rigor and adherence to process (for example, an auditor or accountant). There are dozens of vendors that can help with this type of matching; some have an industry or even a role specialization (such as assessing the abilities and style of a programmer) while others are more general.
Management: Just as a sales organization manages a pipeline of sales prospects and opportunities, the recruiting organization manages a talent pipeline. It’s important to retain the results of interviews and assessments as well as ongoing communications. A great candidate may be interested in the organization (and vice versa), but the timing may not be right. Candidates compare opportunities in part based on how they are treated during the process. A negative experience will impact not only whether they take a position but how they communicate their experience to peers, colleagues, and the broader market.
To improve the candidate experience, the HR staff must acknowledge receipt of the application and promptly communicate status updates. This includes answering routine questions and scheduling and managing interview and assessment logistics. However, large numbers of job seekers or the many interactions generated by hard-to-fill positions often makes this too costly to do manually. AI tools can help automate each of these tasks. Chatbots can provide routine answers to questions, but they should be tested thoroughly and include features that allow escalation to a human.
Onboarding, training and skill development: AI can help ensure greater job satisfaction and retention, even after recruitment tasks are complete and the candidate has been handed off to the hiring manager. The tools that assess and predict job skills and ultimate success can also identify knowledge and skill gaps. Customized e-learning programs can fill those gaps; many AI tools are available to support on-the-job training. (For example, call centers use AI simulations to train new call center representatives.) Helper bots can provide real time answers for new workers. AI-powered knowledge tools that better leverage organizational knowledge and expertise will increase job satisfaction and retention.
Three Broad Areas of Application for AI-Powered HR Processes
Recruiting, Screening and Hiring Training and Skill Development Day-to-Day Knowledge Work
- Applicant screening through résumé processes as well as skill and capabilities assessments, identification of knowledge gaps, reskilling opportunities, experience reapplication, intellectual proclivity and temperament alignment.
- Statistical models of educational, emotional, and psychological makeup aligned with job profiles and models of successful applicants.
- Workforce management using machine learning to predict customer support volumes based on factors like vacations, needed skills, seasonal variations, impact from the weather and changes in demand due to promotions.
Personalized eLearning that adapts to user thinking and learning styles.
- Knowledge remediation and training through customized, personalized, real-time eLearning that can rapidly retrain people for new job roles.
- Just-in-time training aids that provide context-appropriate information.
- Augmented reality overlay applications that provide a virtual reference for physical tasks.
- Performance reviews with data-driven validation to reduce subjective judgment and improve career success and personal development.
Collaboration spaces that support curation workflows and automated tagging.
- Semantic search to improve information access (including search-based applications that integrate structured and unstructured information sources, and question-answering systems that identify employee intents with machine learning.
- Helper, configuration and transaction bots to enable retrieval of unstructured information, guide users through complex product and equipment set up, and perform routine queries of structured data sources.
What Needs to Be in Place to Make AI Work
As I wrote above, “AI is not magic.” It needs to be understandable. Success requires clarity of the business process. If humans can’t figure it out, AI will not be able to add value. Begin by mapping your employee lifecycle. How do you find talent? How do you screen applicants? How do you assess them? Get consensus within your organization about the current lifecycle in as much detail as possible. Where are the bottlenecks? Where do things go off track? What is the candidate’s experience? How can it be improved? Don’t think of technology, just understand how things are being done and the impact on internal and external stakeholders.
It is critical to understand the problem you are trying to solve using AI. Don’t start with the tools and look for problems to solve. Begin with your business challenges and be as specific as you can in describing them. This is why the mapping of the employee lifecycle is so important. You can’t automate what you don’t understand, and you can’t automate a mess. If you begin looking at vendors, have them demo your scenarios and use cases, rather than theirs.
Another critical element is defining success. What part of the process will be improved and what are the baseline measures? Consider what is being measured today and make sure stakeholders understand and trust those measures as well as any promised improvements. If people don’t trust the baselines, they will not be convinced that using AI tools has created any improvements. Good quality data is critical. Identify the sources of data and be sure to test vendor solutions using your data and use cases.
One big red flag for HR is in the risk for potential bias in programs. Ask vendors to certify in writing how they developed their models and validated their data. Be aware of potential biases that could cause the AI to misinterpret candidates’ qualifications or inadvertently screen out results for a particular demographic or use case. If the data sources on which the vendor built their tool do not match the diversity or demographics of your targets, that can introduce unintended bias.
AI has many other potential applications in HR, from capturing the experiential knowledge of long-time employees to making information more readily available throughout the organization. AI-powered training and knowledge management is a very exciting field. Incorporating AI into the HR function can become a key competitive advantage for your business. The sooner you explore its potential benefits, the more quickly you will reap its rewards.
Originally posted on the HRPS blog.
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