Mar 8, 2018 Ralph Perdomo
It’s not difficult to confuse autonomy with automation.
For starters, both sound an awful lot alike.
Both conjure a future where we’re free to do more strategic things. A future free from the drudgery of monotonous chores and tasks. A future where we can finally do what we ought to be doing—in both work and leisure. Both represent a hopeful, utopian future where we can finally live our best lives.
So you’d be forgiven for mixing up these vaguely like-sounding terms.
Created by Ryan Mason
In reality, they’re worlds apart.
Here’s why that autonomous future won’t look anything like an automated one—and why that’s a good thing.
It’s about control
Autonomy means many things to many people.
Through the lens of a workplace setting, autonomy means:
“...having a job where you can make at least some of the decisions on your own. [...] from having a say in your own goals or the projects you work on, to deciding when and where to do your work.”
Or more succinctly, as Joan F. Cheverie manager of professional development programs at EDUCAUSE puts it, “autonomy is the antithesis of micromanagement.”
What’s more, autonomy in the workplace is an excellent determinant of an employee's overall happiness—which makes sense, because it’s human nature to want to be in control of our path.
Contrast this with the auto industry’s more antithetical interpretation of autonomy—as is the case with autonomous vehicles—or self-driving cars. Consider GM’s version of autonomy, which is so dissimilar from that of the workplace’s that it’s seeking an appeal from the Department of Transportation to allow its self-driving car from having any driver inputs.
Yes, in GM’s definition of autonomy, the driver loses a steering wheel and pedals—the driver relinquishes all control.
It’s in the tech
But it’s the same technology blazing the trail in self-driving cars that’s also putting automation into high gear. We’re talking, of course, about artificial intelligence—also known as computational intelligence or machine learning.
“[AI is] flexible to changing environments and changing goals. [AI] learns from experience, and it makes appropriate choices given perceptual limitations and finite computation.”
This means AI learns by doing. In the case of GM and its self-driving car, a programmer doesn’t tell the vehicle how to deal with an obstacle it encounters on the road. Instead, the programmer teaches the AI how to identify an object. It’s then up to the AI to figure out what an object looks like and—processing all the signals from its litany of sensors—calculate a safe path around it.
You can think of artificial intelligence, then, as being similar to a good manager. After all, both are successful when empowering the agent—whether it’s a self-driving car or an employee—to decide its path. It’s ironic how AI provides the autonomous part of an autonomous vehicle.
Only the good bits
This is how AI provides autonomy in workplace automation—it empowers accountants and executives alike to accomplish more and more strategic goals. Or as Watson, IBM’s personified artificial intelligence engine puts it, “[AI provides] a future where technology frees users to focus on what matters most.”
That’s the reality of automation technology. AI and automation is a tool that will not displace the workforce; instead, it will transform the tasks they perform—it will enable workers to be more autonomous.
In the case of payment automation, artificial intelligence may very well execute a smart workflow that compares a PO against its invoice, validate the supplier through tax and other government records, and automatically pays and reconciles the books—freeing the accountant from these lower-level tasks.
And in doing so—in freeing that accountant—he or she can better manage the payables’ workflow more holistically.
After all, no amount of AI or automation can work through a problem the same way a person can. Because a self-driving car may just to pull over to the side of the road—something no accounts payable department can do whenever a problem is encountered.