AI Energizing HCM for Better Employee Experience

Over the last two years, investments in digital technologies such as artificial intelligence (AI) by nearly every major provider of HCM systems and tools have transformed the HR technology landscape. Many of the investments have gone into developing distinctive product capabilities, particularly capabilities that rely on machine learning technology.

Investments have also taken the form of strategic acquisitions of smaller companies with AI expertise and complementary early-stage products. Ultimate Software acquired Kanjoya in September 2016 and is using its AI technology to help customers better understand employees’ attitudes toward their work experience. Workday bought SkipFlag in January 2018 to be able to use deep learning to help customers make sense of mountains of workforce-related data. Then in July of this year Workday announced the acquisition of Stories.bi, a company specializing in augmented analytics that uses machine learning to analyze data and deliver insights framed as a story. Also earlier this year, SAP bought French startup Recast.AI to establish a beachhead in conversational user experiences and speed up development of its Leonardo machine learning. And Oracle added DataScience.com to the fold in May 2018 with plans to use its powerful data science and machine learning platform.

These purchases by vendors with large HCM war chests is an effort to add new and transformative product capabilities rather than to build out or bolster a suite of HCM modules. These acquisitions also are part of a race to generate the most AI buzz in the HCM software market, since AI and the quality of the employee experience have pulled away from the field as themes that attract and excite HR buyers today.  

Among the most important new HCM product capabilities associated with the current spate of industry acquisitions are:

• personalizing the employee experience in onboarding, learning, coaching and even compensation.
• predicting job fit, retention or compliance risks or biased decision-making and recommending best actions.
• improving line managers’ people management skills via prescriptive guidance on, for example, personalized coaching approaches informed by each employee’s personal drivers and proclivities as well as potential.
• analyzing employee sentiments, typically in aggregate and often using unstructured email content and other free-form commentary.
• AI-infused chatbots that can detect a person’s intentions and, if sophisticated enough, show empathy and collaborate with humans or even other chatbots, making interactions more efficient and human-like.

HCM industry developers and data scientists will continue to improve the ablity of their systems to learn from the HCM transactional and training data they process. Consequently, over the next 18 to 24 months I expect to see an array of breakthrough capabililties that address longstanding issues and use case gaps in HR technology. These might include guidance on when to hire, train, transfer or promote, or technology-enabled change management support, such as capabilities to assess organizational readiness, identify potential obstacles and individuals opposed to change, and personalize case-for-change messages and coaching.

Catalyzed by emerging technologies, the systems relied on by HR managers and executives are changing, and in many instances changing rapidly and radically. This promise of technology to make their day-to-day jobs easier comes with an associated challenge, of course: When will the optimal moment be to seek budget for an upgrade? We will of course learn more as we move forward on and as I’m able to assess the improvements and use by organizations. Stay tuned!