AI in People management practices: Future Prediction
Artificial intelligence has evolved as one of the essential technologies of the century. We explore how AI will impact the future of work as we step towards 2020.
AI, the “Killer app”, has permeated our lives, redefining the way we shop, drive, communicate, and manage our health. And as the applications of AI continue to breed, so do the new ideas and investments people make. Given a glimpse of AI’s prospective, leaders are eager to break away from conventional, and backward-looking HR analysis (i.e., descriptive analytics). They are now exploring the ways to attract and hire better talent, foresee attrition, unleash talent across the organization and deploy it expertly, deliver real-time feedback and recognition, build optimal teams for specific challenges, conduct real-time performance management, and provide acumens to managers in how to better engage, inspire and coach their team-members. For this, they need innovative analytics (AI) capabilities.
And many companies are now leveraging it heavily in HR; IBM, for instance, has redesigned its HR service delivery strategy to leverage knowledgeable agents to help employees and managers answer doubts and make decisions about roles, careers, salaries, and learning. United Health Group is building a graph database which uses AI to discover opportunities to improve productivity and service quality. McKesson is utilizing AI to understand employee relationships and improve diversity and team efficiency.
AI in TA and onboarding: Using predictive means such as programmatic recruitment advertising, algorithms both seek and attract qualified candidates. Once a person applies to a job advertisement, algorithms sort and screen them spontaneously using machine learning techniques. At Unilever, job aspirants that initial algorithms screen in are requested to play a series of online games constructed around standards of cognitive neuroscience. These engaging games use ML to generate and analyze abundant data from applicants’ behaviors, attributes, and job-related traits. Successful applicants then participate in a fully automated, AI-powered online interview that evaluates their emotions, truthfulness, and the content of their answers against the job requirements.
AI in Employee retention: Predictive retention analysis is one of the most mature, implemented, and simple solutions in the field of predictive workforce analytics. Algorithms used now by thousands of organizations, predict which employees are at risk of leaving the organization. In their day to day work and behaviors, employees give off many signals about their intentions.
Joberate, a predictive analytics platform that uses ML, looks at employee activities on publicly accessible social media channels, like LinkedIn and Twitter, to assess job seeking behavior patterns. If, for example, an employee has a public profile and updates their education, employment history, or joins a professional group—on multiple occasions over a certain period of time—Joberate’s software will gradually increase the employee’s “J-Score” and this J-Score not only measures job seeking activities, it tracks other actions that are associated to job seeking activities.
AI in learning and development: Organizations spend more than $350 billion on workplace training per year. The greatest opportunity for improvement in workplace learning most likely lies in creating highly engaging, hyper-customized instruction. AI assures a realistic solution to the problem of one-size-fits-all education. Today’s state of the art in AI for learning might be exemplified by Zoomi, a learning solutions provider that embeds AI in eLearning. Zoomi offers the first off-the-shelf toolset that enables L & D professionals to implement authentic AI in eLearning.
Zoomi’s platform provides personalization, adaptive learning, content curation, and automated, real-time assessment. Its algorithms assess various types of learning content (e.g., video, PowerPoint, paper-based, etc.), then break the content down and classify each word, phrase, and concept. As a learner reads through the material, every key stroke, every pause, every break, etc., is analysed in real time and predict the learner’s progress.
AI in PMS and Employee Engagement: Klick Health, a large Canadian healthcare consulting firm, has developed a passive data collection system and ML tool it calls “Genome.” Genome calculates the average time it takes to complete an array of tasks and alerts leaders when projects appear to be going off track. It also reminds project leaders of the pending and urgent things they need to work. Project managers know they’re being examined and are thought to perform better as a result.
AI in Rewards and recognition: Reward program owners should observe firms like Google, WL Gore, Tesla, and dozens of others that now utilize big data, predictive analytics, and machine learning techniques to monitor and analyze their talent constantly. It also helps them to make informed decisions about strategic initiatives designed to encourage performance, including those that incorporate rewards and deliver those rewards on a one-to-one basis.
Some IRR providers—individuals who design incentive and rewards programs for other companies and organizations—have taken measures in this direction with their clients already. One of the world’s largest incentive and motivation houses, for example, uses machine learning algorithms to forecast the rewards a loyalty program member is likely to redeem over the coming year. By examining past patterns, the AI suggests a redemption category to promote to each member.
As the focus on leading business value through HR practices becomes central in a tight economic environment, AI will help HR teams improve outcomes for HR processes by automating recurring routine tasks. AI is also helping HR teams predict and act upon information that was unavailable in the past. Like, AI can help HR teams identify employees who are most likely to quit and enable HR to proactively take retention measures.
For us as HR leaders, AI will revamp every process we touch. The way we source, assess, hire, train, develop, pay, and move people is all being guided by AI. In a post-AI world, the accomplishment of organizations will depend on their ability to prepare their workforce for disruption. They will need to adopt a mindset of continuous knowledge to develop the workforce of the future. In order to implement an organization-wide change, all stakeholders, not just CHROs and CLOs, will need to run this change.
Written by: Dr Ritika Srivastava (NamanHR)
