How to turn your employees into AI stakeholders

The pace of technological innovation over the past 10-15 years has been staggering. Entirely new fields spawned overnight; legacy industries upended or bankrupt just as quickly. This led to major and incredible advances in quality of life for many (although with the spread of misinformation and the weaponization of social media, some might quibble with that conclusion…), but it also led to rapid loss of jobs and uncertainty for those not versed or trained in the knowledge economy. (Much of this was also the case in the wake of previous iterations of technological or manufacturing disruption leading to a new brand of economy, but that’s part of the problem… what happens to those who are getting left behind?). Employees are understandably hesitant when “groundbreaking” new technology comes around, because it could mean they’re out of a job if it’s too groundbreaking. And as A.I. is getting better and better and better, employees are rightfully nervous to embrace all it can offer because of what it might mean for their jobs (or soon-to-be former jobs if the worst comes to fruition). But, if you can teach your employees about how A.I. can truly improve their jobs — how smarter digital solutions can materially impact their day to day — you might just make stakeholders out of them yet.

What do I mean by smarter digital solutions?

And how do you teach employees about how A.I. can truly improve their jobs? Well first of all, you need to have a firm grasp of what A.I. is, what it isn’t, and how it can be used.

“For organizations to get the most that they can from AI, they should also be investing in helping all of their team members to understand the technology better,” according to Emma Martinho-Truswell at the Harvard Business Review. “Understanding machine learning can make an employee more likely to spot potential applications in his/her own work. Many of the most promising uses for machine learning will be humdrum, and this is where technology can be at its most useful: saving people time, so that they can concentrate on the many tasks at which they outperform machines.”

To get your employees to the point where they can identify those opportunities for A.I. collaboration, you have to teach them how A.I. learns, what it’s good at, and what it should be doing. Only then will they become stakeholders in its implementation instead of hesitant adopters by decree.

Machines learn by processing massive amounts of available data, spotting trends, and then acting on those trends and data as per your programming instructions. The example HBR goes with, and I think it’s a great one, is that of expense reports. Any company with a sales staff or executives/managers who have to do a lot of traveling can attest to the time suck expense reporting is. It takes you away from doing your actual job, but has to be done for the company to operate appropriately. This is the kind of ‘humdrum’ the author is talking about, where A.I. can truly make your job better by taking B.S. off your plate. But only if your employees know this is an appropriate opportunity for A.I. to make their jobs easier can you find all these inefficiencies and take automated action.

According to Martinho-Truswell, “My expenses software is a perfect example: it has the receipts of its millions of users to learn from, and it uses them to help predict whether a cup of coffee from Starbucks should be categorized as travel, stationery, or entertainment.”

If you’re able to teach your staff what machine learning excels at, your staff can then pinpoint things it’s not good at. “Problems that are novel, or which lack meaningful data to explain them, remain squarely in the realm of human specialties. Help your employees to understand this difference by showing them tools they already use that are powered by AI, either within the organization or outside it (such as social media advertising or streaming service recommendations)” [emphasis mine].

And finally, you have to understand where A.I. falls short before empowering it with tasks beyond its expertise. “A machine cannot understand, for example, the biases that data reveals, nor the consequences of the advice it gives,” Martinho-Truswell continues. What does that mean? Well, while A.I. might be great at culling through thousands of random résumés to find those that are likely to best match your job opening, I would never trust A.I. with making an ultimate hiring decision — there are too many human variables at play with that particular final decision. When your employees understand what A.I. can’t do and what you’re not going to delegate to it, they’ll be much more open to collaborating with A.I. willfully.

The bottom line is that A.I., when understood fully and used correctly, can markedly improve employees’ lives. If they are taught what it can do, how it learns, and how it can be helpful to them, they can transform from hesitant impediments into empowered stakeholders who bring solutions for increased efficiency to you instead of flowing from the top down. That’s tech you can use!