Ready or not, workplace artificial intelligence (AI) could be coming soon to you.
If employers want AI data to measure productivity, build efficiencies, better understand the talent you have and the skills gaps you need to close – and HR trend-watchers say you will want these things – then you’ll probably want to master AI management skills as effectively as possible.
Monitoring employee progress and performance through technology is controversial, but it doesn’t have to be. After all, we’ve more or less gotten used to it in virtually every aspect of our non-working lives, from our cell phones tracking us 24/7 to near-ubiquitous surveillance in public spaces and full-body scans any time we walk through an airport.
But transferring that level of comfort – or at least acceptance – to the workplace will likely require care, clear managerial vision and, most importantly, complete transparency with employees.
Origins of tracking
When the topic of using tech to monitor worker performance comes up, the first application that probably comes to mind typically involves call centers and customer support. We know, “This call may be monitored for quality assurance.”
The technology certainly lends itself to tracking the number of calls made, the timeliness of response, or how frontline employees interact with those customers.
But the potential applications don’t end there. AI is being successfully used by manufacturers to track production rates, scrap rates, production bottlenecks and progress toward deadlines. In healthcare, AI is helping to monitor dispensing of medications, measure training and education outcomes, and myriad other applications.
Artificial intelligence, combined with human intelligence, thoughtfulness and planning, is a benefit most companies likely won’t be able to do without.
Here are six strategies to help make it work for you:
1. Figure out what you want to know
For AI to be helpful, begin with a clear idea of what insights you hope to gain. Are you most interested in measuring productivity, or establishing accountability? Do you want information about where your team could use re-training or re-skilling? All of the above? Never implement monitoring systems thinking “Hey, let’s install this software and see where we go.” Have a destination in mind, and create systems to get you there.
2. Set goals and state them clearly
When you first introduce employee monitoring systems, you’ll probably get some pushback. That is why it is vital to be transparent about what you are measuring and why. Be clear about the fact that you’re measuring productivity and establishing accountability. If employees continue to grumble and resist despite your transparency, maybe it’s time to consider what it might be that they don’t want you to know.
3. Have a plan for the data
Learning which employees have which skills gaps won’t help much if you don’t have a strategy for filling those gaps. The data you gain can show you where you need to retrain or upskill, but you have to determine how to get that done.
4. Be transparent with data
That inevitable employee pushback is more likely to continue, even grow, if your team suspects the information you’re collecting is going into a black hole, or that you’ve stashed it somewhere, waiting to use it against them in salary negotiations. So, show them what you’ve learned about productivity and how to improve it. Engage them in creating new efficiencies. Invest in retraining them where necessary. Employees want to be part of the solution, not just trapped in the problem. Making that happen shows you’re interested in making the business better, not in being punitive.
5. Make the investment
Implementing monitoring systems is costly enough that, for now, it’s most useful for larger organizations. But the time is approaching when the investment will be considered simply part of doing business, one that should be folded into calculations of overhead. Because at the end of the day, it’s an investment in people and in productivity. And it’s one that will pay off.
6. Remember: Humans work for you
Data can give you numbers and show you trends. But it can’t calculate the value of a call center worker taking extra time with an elderly customer or one whose situation is complicated. In other words, even when you have great data at your fingertips, you still need good judgment.