Augmenting the Workforce: Tools and Technologies to Know

From text analysis that uncovers cultural issues to technologies that “understand” interactions with help desks, the hope is that cognitive tools can elevate the performance of HR, and, ultimately, improve the employee experience.

Whether your organization has explored open-source platforms or used proprietary artificial intelligence (AI) in the workplace, the IMPACT 2018 session “Augmenting the Workforce: Tools and Technologies to Know,” offered context about the capabilities of technology-driven automation in HR organizations today.

“There has been so much hype around AI in the workplace,” said Christa Degnan Manning, Vice President, Solution Provider Research Leader, Bersin, Deloitte Consulting LLP. “We’re here today to help demystify and debunk the hype.”

“Chatbots and virtual assistants are among the prevalent AI tools in use at high-performing organizations,” Manning said. She noted that the major categories of AI technologies encompass ways that companies can perceive, predict, and process data (see Figure 1).

Figure 1: Five Types of AI Technologies

“These technologies are already having an impact on the workplace, and the use of these technologies is correlated with better business and talent outcomes,” Manning said. She shared examples from the three primary ways that AI technologies are currently being used in HR.

  • The first is for automation, taking away repetitive, mundane tasks that no one wants to do. She shared an example of a talent acquisition system that uses robotic process automation (RPA) to confirm that nurses applying for jobs have up-to-date licenses, freeing up recruiters to do more advanced work.
  • The second is for insights, as in the example of a company that uses text analysis to understand cultural disconnects. AI technology assesses the public-facing websites that describe the company and its values, and compares the text to that in performance management systems. The goal is to see where there’s a disconnect between how the company is defining its employer brand and the values by which it assesses employees.
  • Finally, companies are using AI for engagement. For instance, one company is using natural language processing (NLP) to recommend and refine actions—by taking the language in continuous performance feedback and using it as the basis for recommended actions that managers can take to address employee disengagement and dissatisfaction. Managers then feed data back into the system about what has worked and what hasn’t, allowing it to continually improve.

Manning concluded with the potential benefits and risks of AI adoption (see Figure 2), noting, “These systems are only as intelligent as the data and feedback that you give them.” One attendee in the audience agreed, commenting, “The thing that is worrying me is lack of good data. The accuracy and cleanliness of the data we have are a big issue.”

Figure 2: The Risks and Rewards of AI

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