Written by David Turetsky
June 16, 2020
We live in a very smart world… especially when we remember how far technology has come in a short period of time. So much technological achievement in our lifetime has made many things that we would have considered miraculous a few years ago are commonplace.
Just think about how streaming video has taken the place of DVDs in the delivery of media content to the extent that have revolutionized how people consume media and traditional media companies have had to evolve to survive. That same broadband connectivity has led the cloud to become our storage and computing methology of choice rather than larger hard drives and more computing power in our client “machines.”
That broadband and computing power has been leveraged to create new shopping, marketing, and even HR applications. There may be plenty of places in Human Resources where computers can be brought in to make employee experiences better, but is the world ready to consume more from what the machines can teach us… yet?
I say, “yes” and “no.” There are many cases where you can imagine the world of HR and the employee being helped by bringing the power of computing and broadband making their experiences better.
AI in the Employee Experience
One example is homeshoring… why can’t people stay at home either as a choice, perquisite, work at home on a regular basis, or permanently? The ability to work at home saves time, money, crowded highways and cities, and reduces stress. But, what does this have to do to AI?
Consider that Employees in an office environment have people that they can turn to in order to make their lives easier. We had office managers, receptionists, assistants, and a mail room. Now, the world of homeshoring brings many problems that WE need to solve on our own, not the least of which is having someone to smile at, talk to and befriend. AI can’t get you coffee, unless you are ordering Starbucks. It can’t send a package unless you use the UPS skill on your Amazon Echo device. It can’t tell you a good joke over at the water cooler, but ask Siri a joke… she will give you a weak, but politically correct one. So, maybe it can be useful to employees finding themselves at home during the pandemic, or afterwards.
Another, more valuable use of AI is that it enables the employee to find answers to questions they ALWAYS have! Questions like: how much time left in my vacation balance? When does my 401(k) vest? Can I take tomorrow off? What is my next career move based on my skills? These are all good questions, some take more assumptions than others. Can today’s AI answer them?
HR and AI… why not?
The problem is that AI is just not that smart. Artificial Intelligence in HR is getting there, and so are the people that are training these AI brains, and that is a HUGE prerequisite. Those that are focused on those problems need to know how to find the right answers. That’s key… the right answers to the questions that employees have.
One problem is that the data that we can use to train these AI brains are not ready to make employee decisions. The current data on decisions around pay and career that could be used as training sets. But the data needs to be scrubbed so as not to continue pay inequities and lack of diverse promotions. Frankly, that effort is entirely too big. It means reviewing decades of decisions on pay, promotions, hiring across hundreds if not thousands of companies.
So where can AI go in the world of HR?
Health and Welfare, Workforce Management, Learning and Development, and Analytics are all good candidates for AI assistance. They have plenty of data available, provide centricity for decision making with good benchmarks for comparisons. Examples include:
Open Enrollment – when an employee is going through their enrollment in benefits, we can look at their personal situation and review what their claims and costs would typically be for different plans offered (HMO versus PPO) and provide for them cost estimates of how expensive the options would be in the future… AI improves this by knowing the patterns of the employee’s absences (sick, vacation time) and enrollment of a new baby with the costs patterning over the next year for sick and well-baby visits to the doctor. The AI infused decisioning makes the decision better and more realistic for the employee.
Vacation time – We all struggle to budget our vacation time wisely throughout the year. The AI can plot our annual vacations, school calendars and business trips on an yearly agenda enabling us to plot out our vacations well in advance to make sure we don’t leave precious days on the table as we are too busy to focus on them.
Enrollment in skill-based training – knowing that an employee just got promoted to a managerial job can provide the opportunity to give that person critical skills to mentor and lead. AI can offer that person courses to help provide these skills or auto enroll them when their calendar is available.
Analytics in decision-making – providing the best examples of metrics that are abnormal for your population versus the rest of the organization has been popular AI targets. You can easily measure outliers for users. The real analytics gold for AI would be to track manager decision patterns to point out the best decision outcomes and provide alternatives that might be more effective given past outcomes. Even this example is using previous decisions for training that might provide inappropriate outcomes instead of the BEST outcomes. Until the AI can have judgement built in that protects the company from making bad decisions, then we are destined to repeat the past mistakes.
Human Resources is extremely complex and we can’t simplify the solution nor the problem in a blog post. Many concerns exist from employee privacy to legalities of making decisions based on previous decisions. EY has penned a fascinating paper on the benefits and concerns around AI in HR. Northeastern University has AI as a component of their MS in Human Resources.
Obviously, there are many other use cases. Maybe you have AI use cases that are valuable to the world of HR. Put them in the comments. Let’s start the discussion so that the robots can listen and learn from our experience.
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