While HR is not known for pioneering nascent technologies, Artificial Intelligence (AI) represents a terrific opportunity. The consumerization of HR technologies has brought AI to the forefront of innovation in HR. From recruitment to employee experience, and talent management, AI has the potential to transform HR.
This issue of Succeeding with AI in HR will provide you with a basic understanding of what AI is, its applications in HR, and how it works. This issue is split into three parts: 1) AI basics, 2) the business case for AI in HR, and 3) five critical HR areas that AI is transforming.
PART 1: AI BASICS IN THE HR CONTEXT
What is Artificial Intelligence?
Artificial intelligence is a field of computer science that aims to solve cognitive problems commonly associated with human intelligence. In other words, AI enables machines to “think like humans,” and perform tasks such as learning, problem-solving, reasoning, and language processing. Today, AI is being driven by two fundamental technologies – machine learning and deep learning. (Read: The complete history of AI)
What is Machine Learning?
Machine learning is a branch of artificial intelligence that enables machines to learn from and make predictions based on data. The roots of machine learning are embedded in pattern recognition and the concept that algorithms can learn from recorded data without being programmed to do so. For instance, an algorithm that needs to identify cars will rely on images of other cars to teach itself what looks like a car. In this case, the end objective of the algorithm has been defined – which is to identify a car, but the steps to arrive at the objective is learned by the machine itself by training on data.
Key use cases of Machine Learning in the HR context:
- Anomaly detection: Identify items, events or observations which do not conform to an expected pattern or other items in a dataset.
- Background verification: Machine learning-powered predictive models can extract meaning and raise red flags based on structured and unstructured data points from applicants’ resumes.
- Employee Attrition: Find employees who are at high risk of attrition, enabling HR to proactively engage with them and retain them.
- Content personalization: Provide a more personalized employee experience by using predictive analytics to recommend career paths, professional development programs or optimize a career site based on prior applicant actions.
What is Deep Learning?
Deep learning is a branch of machine learning that trains a computer to learn from large amounts of data through neural network architecture. It is a more advanced form of machine learning that breaks down data into layers of abstraction. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using multiple neural network layers for processing (like neurons in the brain).
Considering the same car analogy from above, a deep learning algorithm can distinguish between a car and a truck by identifying the relationships between core elements of a car and a truck (car – 4 wheels, no loading bed; truck – 4 or more wheels, has a loading bed, etc.).
After sufficient training, deep learning algorithms can begin to make predictions or interpretations of very complex data.
Key use cases of Deep Learning in the HR Context:
- Image and video recognition: Deep learning algorithms outperform humans in object classification. Given videos and photos of thousands of applicants, deep learning systems can identify and classify candidates based on objective data.
- Speech recognition: While understanding human voice and myriad accents is difficult for most machines, deep learning algorithms can be designed to recognize and respond to human voice inputs. Virtual assistants use speech recognition algorithms to process human voice and respond accordingly.
- Chatbots: Natural language processing (NLP) trains chatbots and similar systems to understand human language, tone, and context. NLP will emerge as a crucial capability for AI systems as organizations continue to automate HR service delivery with chatbots.
- Recommendation engines: Digital learning experiences often involve personalized learning recommendations related to skill levels and professional interests. Using Big Data and Deep Learning, learning experience platforms can identify learning pathways that might interest individual employees.
PART 2: The Business Case for Artificial Intelligence in HR
Until recently, the primary benefits of HR technology were to improve efficiency and drive cost-savings by automating repetitive tasks. Today, smarter technologies are enabling HR teams to solve critical business challenges, drive exponential performance improvements and even impact larger business outcomes and profitability. AI is fueling HR’s transition from administrative to strategic to mission critical.
Sandy Michelet, Director of People Strategy at Sparkhound says, “AI will make HR more effective. End of story. It will not solve all problems but will allow HR to transfer their time currently spent on repetitive and administrative tasks to a more strategic approach. Many believe that AI will reduce human contact in HR but I believe it will do the exact opposite. Instead of an HR Generalist answering the same questions again and again, s/he can use that time discussing an employee’s development or internal process improvement.”
The top three business reasons for deploying AI in HR:
A recent survey by Deloitte revealed that while nearly 80 percent of executives rate employee experience as important, only 22 percent believe their organization excels at providing a differentiated employee experience. The next competitive frontier for businesses is employee experience and the future of HR will be centered on the employee experience and personalizing engagement. In a time when employees have smart assistants at home and recommendation engines for when they shop, they expect a personalized experience when they come to work.
Speaking about how consumer technologies are shaping employee expectations at work, Jennifer Stroud, HR Evangelist & Transformation Leader at ServiceNow, says, “We have seen the need for chatbots, AI and machine learning in the workplace to drive more productivity as well as modern, consumerized employee experiences. These consumer technology solutions are exactly what employees want in the workplace.”
A recent survey by ServiceNow revealed that 30 percent of employees want a “Google-like” option to easily get the help and information they need at work. “Voice technology in the workplace is also a key consideration. Our survey found that 16 percent of employees want Siri, but to help answer work questions. Beyond these considerations, an effective solution must be mobile, personal and provide the employee with options in terms of how they wish to engage and be supported,” adds Jennifer.
AI can be effectively woven into the entire employee lifecycle, starting with recruitment and onboarding, to HR service delivery and career pathing, to provide bespoke employee experiences.
Data-driven decision making
HR agility has emerged as a critical theme for organizations in the war for talent. And HR agility is determined by the pace at which HR leaders can make sound business decisions. Unfortunately, for many organizations, the data-to-decision workflow looks like the figure below:
While HR technology has made real-time data available to businesses, many still rely on manual methods to draw insights from data. This task is often left to end users or data analysts. This creates a bottleneck when trying to draw insights – causing delays. Decisions also continue to be made with outdated information.
AI helps HR teams extract insights from data and deliver recommendations in real time. Natural Language Generation (NLG) software has the ability to transform data into data-driven text automatically, which makes it a valuable asset for HR teams across industries. AI also has the potential to remove many of the common human biases and inconsistencies in a function as sensitive to such factors as Human Capital Management. Thus, decisions powered by AI have the potential to be faster at scale and more data-informed, as well as more consistent, and unbiased.
Intelligent automation combines AI with automation to enable machines to sense, understand, learn and act – either independently or with human assistance. Intelligent automation can perform not only manual tasks but also make intelligent decisions as a human would. Intelligent automation capabilities can enable machines to understand processes and their variations. Intelligent automation can be deployed across all repetitive manual processes to boost efficiency, productivity, and drive innovation.
AI in HR opens the door to limitless opportunities – imagine if an employee could learn new skills to match the pace of technological advances; they would be in complete control of their career! Now that’s an employee experience anyone would appreciate. As business performance becomes the focal point of all long-term HR strategies, AI has emerged as the most valuable ally for HR professionals.
PART 3: AI Is Transforming 5 Crucial Areas of HR
Increased investment in smarter HR technologies has led to some interesting innovations in the AI ‒ HR space. Here’s a quick overview of the most significant ways AI is transforming HR for the better:
- AI in Recruitment:AI has made candidate sourcing, screening, and matching easier for organizations. In addition to improving efficiency, AI is also helping HR leaders overcome human-bias in decision making.Tammy Cohen, Founder and Chief Visionary Office of InfoMart, believes, “Language within job descriptions steers diverse, qualified candidates away from a position. Gender-coded language, which is language that lends itself towards the ideas and stereotypes surrounding different genders, can subconsciously deter qualified talent. For example, a man is less likely to apply for a job ad that describes the ideal candidate as ‘feisty,’ while a woman is less likely to apply for a job ad that seeks a ‘domineering’ candidate. When writing job descriptions, the employer may not mean to make these mistakes; everyone, in some ways, uses gender-coded language. That’s where artificial intelligence comes into play.”
“AI systems are being used to create inclusive job descriptions and review them for gender-coded language. With several large corporations embracing this on a company-wide level, it stands to reason that AI-augmented job descriptions will become more common,” she says.
Here are some of the key use cases of AI in recruiting:
- Candidate sourcing
- Lead nurturing
- Candidate screening
- AI in HCM: AI is enabling organizations to meet increasing employee expectations by helping HR teams reimagine people and talent processes to build stronger teams, reduce employee turnover, and enhance the employee experience. A few major impact areas for AI in HCM include:
- Performance management
- Workforce planning
- People analytics
- Virtual assistants for self-service/HR service delivery
- Career pathing
- Leadership and coaching
- AI in Employee Engagement: AI has been a catalyst for how businesses interact with their employees. Key uses cases include:
- Intelligent surveys
- Real-time feedback platforms
- Rewards and recognition
- Personalized messaging and communication
- AI in Employee Benefits: AI and automation can ease the administration, implementation as well as management of employee benefits. Sparkhound deployed a chatbot to help demystify benefits for employees. Sandy says she’s already seeing positive outcomes from the deployment. She says, “We recently launched an HR BOT to answer the top 30 benefits questions we’ve received over the last four years. Part of the launch included a feedback button so we can continuously improve. Although it was a soft launch, I am already hearing positive comments about the ease of use and the immediate responsiveness. Right now, many employees (and HR people) believe that HR’s role is to provide human involvement every time the manager or employee needs assistance. I understand why….that has been the culture of companies since HR was called a ‘personnel department’. That was before everyone had a cell phone in their hand, shopped online and chatted with customer service.”
Key use cases of AI in benefits administration include:
- Benefits personalization
- Benefits automation
- Benefits communication
- Benefits compliance
- AI in Learning and Development (L&D): In the current talent landscape, where skills have a shorter shelf life than ever before, AI could prove to be the game changer. AI is enabling learning platforms to replicate the qualities of successful consumer content platforms like YouTube and Netflix to improve learning outcomes. Some of the key impact areas of AI in L&D include:
- Personalized learning pathways
- eLearning analytics
- Conversational interfaces
In the upcoming episodes of our “Succeeding with AI in HR” series, we’ll dive deeper into how AI is transforming each of these HR functions, how you can deploy these technologies and measure the ROI of your AI initiatives.