Prior to the 1982 breakup of the “Ma Bell” monopoly and the subsequent 1986-87 strike that affected 130,000 American telecom workers and 100,000 British telecom workers, my father worked for Ohio Bell (Barger, 1984). After the FCC, the Justice Department, and federal courts broke up the Bell system, allowing long-distant competition from AT&T, Ameritech, MCI, Sprint, and others dad split off to work for Ameritech (Barger, 1984). He retired in 2000 after 30 years of service and was only 50 years old at the time. If you asked my father, he would admit that 50 is too young for retirement, and he would tell you that he had at least another 15 good years in him. So, why retire from Ameritech, an emerging giant in IT technology and telecommunication, at such a young age? The impact of technology and the aversion to be micromanaged by foremen who were sitting behind computers monitoring GPS devices attached to the rooves of telephone vans caused a lot of the guys my dad’s age to retire young.
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Throughout his career, dad saw audio recordings replace telephone operators, experienced the decline and eventual extinction of pay telephones, and witnesses the emergence and domination of fiber optic technology. But having to explain to a supervisor why a routine phone jack installation took 5 minutes longer than it typically would, based on the fact that the supervisor was monitoring GPS technology to know that the van was sitting at that address 5 minutes too long, was too much for dad and some of his colleagues. Fast forward to 2019. My father is 69 years old now, and he still has an aversion to technology. He has never had an email address, he has never owned a cellular telephone, and he has never sent or received a text message. He is indeed a rarity these days.
Technology is impactful. Just as one might be adverse or intimidated, hundreds of thousands will embrace the change brought by artificial intelligence, virtual reality, robotics, drone technology, and whatever is next. Regardless, machine learning and robotic-powered technologies are finding acceptance in all areas of business and life, especially in the areas of employment. Three areas of employment that will be most impacted over the next decade are recruitment, learning and development, and employee engagement.
For decades recruiting was a fact-finding industry. Recruiters would pour over resumes looking for buzzwords relating to certain skills, degrees and university names, employment experience and professional interests. Once a candidate was identified, an interview would be scheduled where a deep dive into strengths, weaknesses, habits, and attitudes would be evaluated face-to-face using behavior based-interviewing techniques in an attempt to find the perfect fit. If all of that sounds time consuming, it was.
As Bernard Marr indicates, “Recruitment is one area of HR that is particularly rich in data” (Marr, 2018). As machine learning moves from the fringes of the human resources toolbelt becoming more mainstream throughout all industries, any sourced data, including resume data can be assessed. Predictions can then be generated based on recognized patterns in data that look for required employee attributes, and the system can respond with recommendations. Marr presents us with an example of a company platform that “collates data from across the Internet to help find people who are ideal for specific jobs, whether those people are currently searching or jobs or not” (Marr, 2018). Where, in the past, recruiters could only work with those from whom they sourced a resume, the future of recruiting is open to those who might be perfectly happy where they are and doing what they are doing. For example, LinkedIn is a very simple networking app that has recently added job positing and recruitment platforms. LinkedIn’s Senior Manager of People Analytics explains that they rely on a “breadth of skills to expertly channel the analytics projects in the right direction” (Guenole, Ferrar, & Feinzig, 2017). From personal experience, I have been contacted by universities across the country asking if I’d be interested in hearing about opportunities in their institutions based solely on the skills, experience, and recommendations in my LinkedIn profile.
Another very positive byproduct of using predictive modeling in recruitment, assessment, and evaluation of candidates is the elimination of bias. Regardless of gender, race, education level, disability, or age, “machines can make decisions without any of the potential biases that human being bring to the table” (Marr, 2018). True diversity and opportunity for all is the future of business! In his article, The Beginner’s Guide to AI in HR, Sushman Biswas states, “In addition to improving efficiency, AI is also helping HR leaders overcome human-bias in decision making” (Biswas, 2019). However, as Guenole et. al. caution, algorithms are not always perfect, especially “in high-volume recruitment” (Guenole, Ferrar, & Feinzig, 2017). While the creators of these systems have impressive skills in mathematics, computer science, and statistics, “the legal and social consequences of analytics in employment contexts” may be missing (Guenole, Ferrar, & Feinzig, 2017).
The last great benefits of AI, robotics, and automation to the recruiter is time and money savings. As I characterized above, many thousands of man hour used to be expensed sourcing resumes, evaluating skills, and screening potential candidates. Man (and woman) hours are expensive. In a world of machine learning and AI algorithms, businesses “are likely to save money, effort and resources, ultimately freeing up HR’s time to focus on more valuable activities” (Marr, 2018). Sarah Smart, VP of Global Recruiting for Hilton is quoted as saying that Hilton has “increased our speed to hire by 85%” by utilizing AI as part of their recruiting of candidates (Meister, 2019). Many companies find that automation decreases screening time considerably, improves job application completion rates, and flags any background issues that may be discovered as part of data review. Whenever we discuss time and money; however, many operations minded professionals often bring up the fear of “human” resources being replaced with machine learning. I agree with Marr when he says that automation simply “frees up HR to focus on more strategic things that are critical to business success” (Marr, 2018). Machines will not replace people.
Learning and Development
Sourcing skills externally as well as training for skills internally has been challenging for businesses. For decades, staff workshops, training seminars, and conferences for every imaginable business topic were held in such places and Las Vegas, Miami, and Atlantic City, and those lucky enough to attend may have even learned something. In the age of automation; however, organizations are relying on performance management software to evaluate efficiencies, support learning and development, and leverage training opportunities.
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Automation is pushing businesses to approaching learning, training, and development in new ways to the ultimate benefit of the business. Many companies are using computer aided training software to train their associate in areas of expertise that the associate chooses based on the direction of growth they desire within the organization, or in areas that the company desires the associate to improve and grow. A quick search in the Internet reveals hundreds of software programs that assist managers and leaders. Most of these sources allow companies to personalize the landing page, bring teams together with collaborative learning tools, and even input upcoming projects based on training that has already been completed. Some of these packages even use predictive software to identify future business goals and then apply training opportunities to individuals based on specific projects and what associates have already completed.
For example, a CHRO reviewing learning and development software data might alert a senior manager that an associate has completed 82% of her team training and 65% of her individual training in the area of project management with high accuracy and very little skill gap. The senior manager then could identify that associate as someone to lead an upcoming team project based on the data received. Biswas give credence to the example stating, “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 individuals” (Biswas, 2019). Guenole et. al. also refers to this when discussing which projects companies should undertake, how many people will be needed, and what existing skills already exist in the organization (Guenole, Ferrar, & Feinzig, 2017). Workforce analytics can make this process much easier, and HR managers are catching on.
Employee engagement covers a broad range of subjects from performance and recognition to benefits, wellness, and satisfaction surveys. As Marr says, “once you have found the right people, you need to keep them engaged and satisfied” (Marr, 2018). As technology improves, so does employee engagement, especially with remote associates, or within global companies with satellite location throughout the world. As mentioned above, when I first entered the workforce as a sales associate for International Thomson Publishing (ITP), my main point of contact with customers was via telephone, and our contact with our publishers, remote managers, and satellite locations was limited to a yearly conference meeting we would have in Ft. Lauderdale where we had the opportunity to hear from the leadership, meet face-to-face with peers and authors, be recognized for exceeding sales goals, get feedback, reinforce culture, and receive information concerning any changes taking place within the company or industry. Today, meetings happen much more regularly via webinar or teleconferencing, and text, email, chatbot technology, and Skype help salespeople reach more customers, more quickly and get feedback from managers in real time.
Marr says that AI and automation frees up HR leaders “to focus on more strategic things that are critical to business success” (Marr, 2018) Employee engagement is one of those things and as a result, new jobs within HR departments are being created that focus on happiness, growth, and training. For example, Jeanne Meister, mentions three new employee engagement job titles that have come as a result of leveraging machine learning and blending it with “human touch” areas of HR. One new role that is emerging is Head of Business Behavior, which gathers and analyzes personal, environmental, spacial, and behavioral data on employees “to create strategies to improve employee experience, cross company collaboration, productivity, and employee well-being” (Meister, 2019).
One concept I uncovered as part of this research included the importance for meaning in work. Two or three decades ago, our parents and grandparents couldn’t have care less about whether their jobs had meaning as long as they put food on the table. Today, many workers are trading money for meaning and employee engagement in the form of balanced life programs, health and wellness programs, and growth and happiness departments are aligning employee needs with business goals. Meister reveals that in a survey of 2,285 American professionals across 26 industries, results indicate that workers would “be willing to forgo 23% of their lifetime earnings in order to have a job that was meaningful to them” (Meister, 2019). That is a major cultural shift that tells business that they need to take employee engagement, job fit, compensation, vision, and culture very seriously.
Artificial intelligence, robotics, and automation are impacting the future of employment in all areas and changing the landscape of business by streamlining what I call O3 (Onboarding, Observation, and Ongoing Training). All companies in all areas need to embrace this change in order to stay on the cutting edge and maintain competition within their industry. Technology is not something to fear or ignore. As I’ve told my father many times, those forward thinkers at Ameritech were trying to help you old guys move forward, and that technology you were being introduced to in 2000 was just as useful a tool as the screwdrivers, wire cutters, and line testers in your old lineman’s toolbelt. Don’t begrudge it.
- Barger, M. D. (1984, April 1). What Killed Ma Bell? Retrieved from Foundation for Economic Education (FEE): https://fee.org/articles/what-killed-ma-bell/
- Biswas, S. (2019, February 5). The Beginner’s Guide to AI in HR. Retrieved from HR Technologist: https://www.hrtechnologist.com/articles/digital-transformation/the-beginners-guide-to-ai-in-hr/
- Guenole, N., Ferrar, J., & Feinzig, S. (2017). The Power of People. Pearson Education, Inc.
- Marr, B. (2018). Data-Driven HR: How to use analytics and metrics to drive performance. New York: Kogan Page.
- Meister, J. (2019, January 8). Ten HR Trends In The Age of Artificial Intelligence. Retrieved from Forbes: https://www.forbes.com/sites/jeannemeister/2019/01/08/ten-hr-trends-in-the-age-of-artificial-intelligence/#7a6e01ea3219
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