AI in Human Capital Management (HCM): The What, Why and How

AI in HCM can be defined as the application of smart technologies such as artificial intelligence, machine learning, natural language processing (NLP), and deep learning to automate routine HR tasks, deliver personalized experiences, and gain actionable insights from HR data.

Welcome to the third episode of Succeeding with AI in HR. This edition of AI in human capital management (HCM) will provide you with an understanding of the key applications of artificial intelligence in talent management, employee self-service, recruiting, analytics, and benefits administration. You will also learn about the key features, deployment models, and questions to ask before investing in an AI solution. 

Table of Contents

• Section I: What is AI in Human Capital Management
• Section II: How AI Improves HCM
  (i) 6 Components of the Employee Lifecycle
  (ii) 5 Essential Use-Cases of AI in HCM
• Section III:  Investing in an AI-powered HCM Solution
   

Human capital management (HCM)Opens a new window is undergoing a profound shift. Much like the industrial revolution that transformed manufacturing, technologies like artificial intelligence (AI), machine learning (ML), and deep learning are transforming the way we live, work and communicate. These systems that constantly consume information to improve the accuracy and efficacy of human tasks hold great potential when paired with human innovation.

As the focal area of HR shifts from administrative tasks to improving workforce productivity, AI in talent management can help organizations streamline processes, improve functional efficiency, and make more informed people decisions that directly impact the bottom line. So, let’s look at some of the basic concepts in AI in HCM and explore the key use cases.

Section I: What is AI in Human Capital Management (HCM)?

AI in HCM can be defined as the application of smart technologies such as artificial intelligence, machine learning, natural language processing (NLP), and deep learning to automate routine HR tasks, deliver personalized experiences, and gain actionable insights from HR data.

Like most smart technologies, the foundation of AI in HCM is also largely based on Big Data. Organizations have used human resource information systems (HRIS)Opens a new window and human resource management systems (HRMS) since the late ‘80s. These systems laid the foundation of the modern cloud-based HCM solutionOpens a new window that functions as an end-to-end software to manage the entire employee lifecycleOpens a new window . In addition to providing a seamless user experience and tons of new talent management features, HCM solutions also consolidated vast amounts of disparate HR data points from across the organization into a single, centralized solution.

Now, with the integration of smart technologiesOpens a new window into HCM solutions, all of this data can be put to use to understand how employees interact with their work, their workplace, and their colleagues to predict future business performance from a human capital perspective.    

Speaking about the potential of AI in HCM, Scott Morgan, Director of Information Technology & Administration at Infor, says, “HCM of the future will be transparent and limitless to the user. No rule or integration or user experience will be too complex or compound, to deliver under a seamless cloud service. Native capabilities for HCM, in this context, will no longer be a distinction because functions and capabilities will be synchronized across the many platforms leveraged to deliver the expected outcomes of HCM e.g., labor planning, execution, and analysis.

“As such, Infor views artificial intelligence (AI) as the bigger of these big ideas, or the umbrella discipline, that would also include machine learning and the neural networks needed for chatbot services. As a collection of smart tools, AI enables more data to be considered when a transaction is initiated by the user, or when a scheduled event is triggered. It also searches for connections, patterns, and correlations between these data elements. As a jumping-off point, the transformation that we see occurring between the user and an HCM system is two-fold, 1) less intervention and 2) better outcomes. Less (human) intervention is required because the HCM system is considering more than linear inputs, and the contextual machine learning algorithms progressively and iteratively will improve the accuracy of its outcomes.”

For HR leaders, it is better business outcomes that is of primary interest. So, let’s dive into how AI in HCM can help HR teams deliver improved business outcomes.

Section II: How AI Improves HCM

(i) 6 Components of the Employee Lifecycle  

Fig. 1. How AI Impacts Human Capital Management

The entire employee lifecycle can be broken down into six major components:

• Talent Attraction: It begins even before a recruiter approaches a candidate with a profile. Talent attractionOpens a new window typically represents employer branding: the perception a potential candidate has about an employer. This perception could be shaped by word of mouth, general business performance (bullish stock performance or innovative product lineups), employee stories, and media coverage. 
• Recruitment: This begins with candidate sourcing and ends with an offer being made to the shortlisted candidate. As a huge chunk of recruitment becomes automated, the people focus moves from operational tasks to improving the candidate experience, boosting engagement, and selling the job to the candidate.
• Onboarding: Employee onboardingOpens a new window is perhaps the most critical driver of long-term engagement. Effective onboarding ensures that your new hires seamlessly integrate into the larger workforce and add real value to their work. Onboarding determines how productive your new hires will be.
• Talent Development: Once your new hires are onboarded, how do you keep them engaged and become better at their jobs? Talent developmentOpens a new window includes everything from on-the-job training, to performance management, and succession planning. Data from performance reviews and weekly sign-ins provide valuable insights into employee competencies and skills which can then be mapped to their succession plan.
• Retention: It would be incorrect to look at employee retentionOpens a new window in isolation from the other parts of the employee lifecycle. However, there are certain factors that impact retention more than others. For instance, employee benefits, stock options, and rewards are more likely to impact retention than, say, learning and development. With changing workforce demographics, retentionOpens a new window will become more crucial as HR leaders look to justify the ROI of their HR strategy.
• Separation: This is the last stage of the employee lifecycle and presents a terrific opportunity for HR teams to collect valuable data in the form of feedback and exit interviews. Also, past employees are perhaps one of the most important influencers when it comes to your employer brand. Employee stories greatly determine how your organization is perceived by candidates.

 

With AI in the loop, the employee lifecycle becomes easier to manage and influence. Let’s look at how AI is improving human capital management (HCM).

(ii) 5 Essential Use-Cases of AI in HCM   

1. Talent Acquisition: AI, when used in talent acquisition Opens a new window automates a large chunk of processes that relied on human recruiters. AI-driven talent acquisition also makes hiring more transparent, data-driven, and objective. From candidate sourcing to recruitment marketing, to interviewing and onboarding, AI has a significant impact on recruiter productivity and the candidate experience.
2. HR Service Delivery: AI-powered chatbots are changing how we interact with businesses in our consumer lives. The same principle holds good when it comes to interacting with your employer. AI-powered chatbotsOpens a new window provide real-time, 24×7, support to employees. This effectively eliminates the need for HR professionals to attend to request/questions that employees ask. Additionally, the scalability of AI ensures that HR services can be delivered on mobile devices. Scott believes, good AI can effectively eliminate the need for HR intervention in employee self-service. He says, “When we layer chatbot services over the sophistication of AI and machine learning, the science then becomes approachable. Chatbots humanize the inhuman by elevating the synchronization of HCM data, most often, across multiple different business systems. In doing so, HCM becomes transparent through voice and text recall(s) that will not require direct intervention – a limitless utility that while critical and necessary, will cease to exist in the eyes of the user.”
3. Improved Personalization: Remember, how we identified data as the foundation for all intelligent technologies in part one of this series?Opens a new window Well, HCM solutions are best positioned to leverage the wealth of employee data spread across their multiple modules. And with more data comes more personalization. Think of personalized nudges or recommendations you receive when you watch a movie on Netflix. Now, apply the same level of personalization to your technological interactions at work. Imagine if you just completed a course on your organization’s eLearning platform. Next, you begin receiving tailored recommendations based on your previous course choice. Doesn’t this make learning more engaging and fun?
   Personalization based on people dataOpens a new window can be used across every step of the employee lifecycle from recruitment to separation. AI helps HCM solutions detect employee behavior patterns and create relevant, individualized interactions that improve the employee experience.
4. Performance Management: Performance managementOpens a new window is unarguably the one process that relies heavily, if not completely, on data. And what better way to analyze all this data than apply machine learning or AI to make more data-informed talent decisions? The idea behind AI-driven performance management is to highlight employee strengths and weaknesses in the most objective way. AI also helps eliminate any bias from the performance evaluation process. The long-term impacts of AI-driven performance management include higher retention, improved workforce productivity, better engagement, and lastly, improved business performance.
5. Business Intelligence and Analytics: Perhaps one of the most important use-cases of AI in HCM is AI’s ability to monitor and analyze reams and reams of employee data in real-time and provide HR leaders with actionable insights into key talent areas. AI also helps transform HR data to reveal future trends, predict behavior, propensities and future talent needs. Better people analytics and business intelligenceOpens a new window allow effective and faster implementation of large-scale organizational changes.

As we’ve seen in the points listed above, the underlying concept of AI in HCM is to maximize the value delivered to both the employee and the organization from their interactions with technology, work and, each other.

Section III: Investing in an AI-powered HCM Solution

So, AI HCM solutions can certainly help your business thrive in the digital age. But what considerations do you need to make before investing in an AI-powered HCM solution? Well, evaluation criteria for any AI-powered HCM solution are very similar to the purchase parameters of any HCM solutionOpens a new window . However, the data requirements for an AI HCM solution may vary slightly depending on where employee data resides at your organization and how you extract it. So, here are a few essentials for deploying an AI-powered HCM solution:

• Identify pain-points and outline how your new solution will help you overcome those challenges
• Perform a data-audit to ensure data integrity
• Document your KPIs and expected outcomes
• Create a change-management strategy to onboard all stakeholders

 

Scott’s advice to HR leaders looking to invest in AI HCM solutions is to be prepared to completely adopt it. He says, “Don’t invest in it, if you and/or your organization is not fully prepared to adopt it. More specifically, implementing an AI strategy for HCM is easy. All it takes is time and money. However, nurturing the business systems and data elements that allow AI to aggregate and exploit better outcomes i.e., do what it was intended to do, that’s a completely different matter. In the context of HCM, organizations should have consensus on the fixed labor elements such as budgets, task standards, averaged cost, workload factors and production drivers, all of which are collapsed under AI optimization. Know where the source of this data exists, and how hard or easy it is to extract it. AI does not function in isolation of other systems, rather it requires and demands them all – each and every one of them – to achieve maximum effectiveness. Whether in structure or unstructured format, enterprise and contextual AI – like those developed by Infor – will then be able to apply the appropriate mathematical approach, without human intervention, to achieve smarter outcomes for staffing, scheduling and production decisions. But we’re not done in terms of other considerations for HR and Operations stakeholders. There’s one more – trust. Organizations must break the habit of instinct-first decisions and rely on the infinite data considerations that AI is performing.”

Before you deploy an AI HCM solution, it is recommended that you create a change management strategy to bring your HR team and employees up to speed. In addition to change management, it is also helpful at this point to identify and document your KPIs and expected business outcomes that will allow you to quantify your success with the new AI-powered HCM solution.

Final Thoughts: Does AI Make Sense for Your HCM

Employee-centric organizations cannot afford to ignore the massive impact AI has on HCM. AI in HCM empowers organizations to influence employee engagement and their experience every step of the way. By tapping into rich data sources and performing advanced analysis, AI can help you build future-proof talent strategies and create better workplaces. As employee-centricity becomes a competitive advantage for organizations in the future of work, AI can become your most valuable ally in the war for talent.

Are you using AI for talent management? Let us know on Facebook, LinkedIn or Twitter. We’re always listening!