The Deep Learning HR Service That Could Have Made Carly Fiorina President

    One of the things that has been very clear to me over the last decade is that too many CEOs see layoffs as a tool they can aggressively use to meet quarterly numbers and/or spike their stock. They have no real concept that eliminating large numbers of qualified employees is like letting a two-year-old with a drill get rid of the extra weight in a race car. The damage done often exceeds the benefits of a layoff. No one should have learned this lesson better than Carly Fiorina, who had a decent plan to become President of the U.S. That plan had these foundation elements: Be seen as a successful CEO of HP, take the California Senate seat from the weakest California Senator, and then use this background to become President.

    But Fiorina got fired from HP, largely as a result of losing the loyalty of the HP employees, and getting stabbed in the back by one of her closest advisors, and then lost the Senate race, largely by the same number of people she laid off, plus their immediate families. Had she been more compassionate with regard to her downsizing efforts, she’d likely not have been fired, have won that Senate seat, and we could have had Fiorina as President, not Donald Trump.

    We’ll break this down into three parts: the problem that Fiorina needed to solve, the tool I’ve recently run into that could have solved it, and the future of deep learning in HR.

    The Problem That Needed to Be Solved but Wasn’t

    At issue for Fiorina was a near complete disregard for the needs of the employees working for her. I’ve seen a lot of CEOs reach a mental level where they think they are so far above those that work for them that they lose all empathy for them. Don’t get me wrong — too much empathy can make a CEO unable to make the hard decisions. But if a CEO loses all empathy, the result is typically some kind of perceived employee abuse and Fiorina’s layoffs were surrounded with those perceptions. The people she got rid of had a significant tendency to hate her as a result, and this same perception spread to her staff, making it nearly impossible for her to execute. It is my belief that had she simply handled her large layoffs better, much of what eventually caused her failures later in life wouldn’t have occurred and she’d have had a far stronger shot at becoming the U.S. President. On the debate stage, when she spoke, she was actually a far stronger performer than Trump, but she couldn’t get over the perceptions surrounding her failures resulting from poor employee treatment.

    So, a tool that could have allowed her to do the layoffs without the employees feeling she had tossed them aside like last month’s garbage would have massively improved her chances of success not only as CEO but as an eventual presidential candidate.

    RiseSmart Deep Learning Outplacement

    This all came to mind last week when I was briefed by RiseSmart, a company specifically formed to handle layoffs and other downsizing efforts in a way that minimizes the impact on the departing employee. The company uses deep learning to best match employees with job opportunities at scale, minimizing the impact of the layoffs on their lives.

    What a deep learning system can supply is the kind of customized job and skill matching that typically would require a dedicated employee. But you can’t scale dedicated employees anymore, and that creates a great opportunity for a deep learning system to step in and provide a very effective human-like service at scale.

    Unlike simple question matching, deep learning has the capability to learn from mistakes and assure that not only do the skills match, but that the personalities and career path match as well. And finally, also through experience, the system can learn to identify those employees who have questionable backgrounds or should be removed from the workforce so that they don’t become problems for the hiring firm.

    The Future of Analytics in HR

    Now, what I didn’t share is that my second degree is in Manpower Management, basically the science behind Human Resources, which is why RiseSmart was an interesting conversation. I had minors in Marketing and Computer Science, but my passion at the time was in better placing people where they could be the most successful, with success being measured not by financial rewards alone but by overall happiness and wellbeing. That is a part of the employment process that has sadly been lost through misguided and largely failed efforts to assure workplace equality.

    Deep learning could be used to identify those with the potential to lead the company early on and assure that they got the breadth of skills needed to eventually do the job. It could be used to identify and rehabilitate employees who were unhappy and not screw up motivated employees who had passion and deep love for their firm (something that happened to me years ago). It could be used to identify and prevent the conditions that lead to embezzlement and abuse, and identify and remove employees with those tendencies before much, or any, damage was done.

    In short, it could become the heart of the kind of “great place to work” policy that was, at one time, a breakout idea that was killed accidentally when IBM bought ROLM.

    Wrapping Up: The Friendly AI

    We have a lot of focus on how the implementation of AI will cost jobs, but firms like IBM have been arguing that the implementations they imagine, at least initially, will enhance, not replace, workers. Enhancing HR would be at the core of any such effort, focusing on making employees happier, more productive, and more fulfilled rather than just unemployed.

    And, in addition to making us happier in our jobs, AI could have made Carly Fiorina President. There is a lot of irony in this, given that the old HP, the firm she once ran, was a tech firm that could develop such a solution itself.

    Rob Enderle is President and Principal Analyst of the Enderle Group, a forward-looking emerging technology advisory firm.  With over 30 years’ experience in emerging technologies, he has provided regional and global companies with guidance in how to better target customer needs; create new business opportunities; anticipate technology changes; select vendors and products; and present their products in the best possible light. Rob covers the technology industry broadly. Before founding the Enderle Group, Rob was the Senior Research Fellow for Forrester Research and the Giga Information Group, and held senior positions at IBM and ROLM. Follow Rob on Twitter @enderle, on Facebook and on Google+

    Rob Enderle
    As President and Principal Analyst of the Enderle Group, Rob provides regional and global companies with guidance in how to create credible dialogue with the market, target customer needs, create new business opportunities, anticipate technology changes, select vendors and products, and practice zero dollar marketing. For over 20 years Rob has worked for and with companies like Microsoft, HP, IBM, Dell, Toshiba, Gateway, Sony, USAA, Texas Instruments, AMD, Intel, Credit Suisse First Boston, ROLM, and Siemens.

    Latest Articles