Ben is VP Products at Saba where he has overall responsibility for the Saba Cloud offering and Saba’s Talent Management and Social/Collaboration Suites. Ben has a passion for products that help people learn and work and joined Saba to deliver on that vision. In his early years with Saba, Ben served as a Business Consultant & Engagement Manager and led Saba launch efforts for key accounts including IBM, Intel, EMC, TelMex, T. Rowe Price and others. Ben later moved from Consulting to Product Management where he set a product strategy to move Saba through several transitions: From transactional to engaging. From learning to intelligent talent management and from enterprise to cloud. Recent initiatives have included making Saba Meeting a native component of the the Saba Cloud platform, launching new products for Recruiting, Succession and Compensation and innovations such as The Intelligent Mentor (TIM: machine learning + big data = predictive analytics), pQ Score (employee engagement measure) and Dynamic Network Analysis (DNA).
MK: What are some ways artificial intelligence (AI) and machine learning (ML) will affect future HR best practices?
BW: The advancements we’re seeing today with AI being applied to HR technology are about making people’s lives easier. For HR professionals, it’s an exciting time because automation and machine learning can help them deliver more value to their employees and their managers. For example, because these algorithms can synthesize vast amounts of data about employees across the company, they can then make personalized recommendations such as new learning to support areas of interest and improving performance, or new connections to make within the organization – all with the aim of making employees more productive every day. It can also give HR and other business leaders improved understanding of employee sentiment in their organization and arm HR with the data they need to make better business decisions around talent programs.
MK: Artificial intelligence (AI) and machine learning (ML) are disrupting best practices not only in HR, but across the entire enterprise. CHROs must expand the scope of the discussion beyond HR. What are the most important considerations for the coordination of AI and ML across an organization?
BW: The nature of people’s work is changing in all areas of the business and that’s the lens through which HR needs to look at the application of AI. This is particularly true when it comes to how technology augments the work of current employees or replaces some of the work of current employees. HR should ask: What is the nature of the work that needs to be done? Where does it make sense for our people to do it? If the work is undifferentiated and requires heavy processing capabilities, can it be done or augmented by automating it? In addition, HR can look ahead at what skills – IT, business analysis or otherwise – are going to be required in the future for roles that must program, work with or alongside AI enabled functions.
MK: Why should HR be excited about the future of artificial intelligence (AI), machine learning, chatbot assistants and the like, and where are some areas where automation will make HR more effective?
BW: There’s a lot to be excited about as HR technology continues to showcase capabilities that come from the consumer realm. For example, conversational voice interfaces give employees a consumer-like interaction experience with their talent platform. Everyone at work is busy, and often feels strapped when it comes to investing time in things that distract from their day-to-day work. People are used to a consumer-like interaction with apps and virtual assistants on their personal devices, so I think the future of HR technology will do the same, making it easier for everyone to fit modern performance and learning into the daily flow of work to really augment human performance. By creating new ways to interact with HR technology and fitting into the flow of work, things like voice enabled or text enabled chat interfaces the technology becomes like another trusted advisor, and a support tool to conduct tasks that occur frequently but may by complex and unnatural to do in the regular flow of work.
MK: How can organizations leverage data and workforce analytics to meet the needs of business models that are becoming more global and diverse in nature?
BW: As organizations continue to become more global, key workforce data gets more complicated to access and understand. Mergers and acquisitions, contract employees, and contingent and part-time workers all make even a simple headcount calculation complex and time-consuming. A strong workforce analytics model is critical to aligning talent with organizational objectives to remain competitive. This requires understanding of the current status of the workforce to understand gaps between present state and desired future state. Before embarking on the workforce analytics journey, HR should partner with business leaders to determine what critical questions need to be answered – and it cannot be everything – and then identify the sources of data within the organization where those answers should exist. It’s an important exercise to start. Once you understand what those questions are and where the data sources reside, HR can work with their partners in the business to determine the best approach to understanding the data.
MK: Predictive analytics are increasingly being used to decrease employee turnover and increase employee performance. Where do you see predictive analytics having the most impact in talent management today — and in the future?
BW: There’s great potential to use predictive analytics to address areas like engagement or retention and even potential performance. However, too many focus on metrics that are too late to do anything about. Most organizations only run engagement surveys annually or once every other year. That means there’s a gap in the information they collect because employee perception and sentiment shifts all the time. And engagement isn’t something a product can solve – it’s an outcome. It’s the ultimate report card on your talent management strategy . By taking the pulse of employee engagement more frequently, you have more aggregate data and you’ll be able to leverage predictive algorithms to discover patterns and identify potential hotspots before they become a problem. A strong approach to measuring employee sentiment combined with all talent program insight such as performance, learning, tenure, career mobility, means you can use predictive analysis to understand potential turnover and the triggers for that type of turnover.
MK: What is your vision for the future of workforce data and talent analytics and how will HR technology fit into this vision?
BW: Our talent systems are collecting an abundance of data about our people. How they’re performing against goals, their learning patterns, their sentiment and feelings about the organization, their connections within the organization – to name a few. The application of this data, using machine learning to enrich the employee experience, is where HR can unlock tremendous value in their HR technology in the future. For example, being able to access and apply learning when it’s needed simply by asking the system questions, or connecting with the right people in the organization to get work done, or jotting down important goals or items as they come up. The same approach can give HR leaders real-time actionable insight into the talent programs that drive business outcomes without having to be a data scientist. Imagine an HR leader being able to ask questions and get real time answers to which departments are most engaged, which are falling behind in learning requirements, or which individuals are retention risks, simply by talking to the system. That’s the power of the future of technology like this.