Although rapidly growing in popularity and importance, machine learning (ML) can be intimidating to approach due to its technical nature. After all, most of us aren’t mathematicians—we’re business professionals. Surely the complexities of machine-learning are better left to engineering teams and technical professionals, right?
As with most things in life, it’s not so black and white. For most, there won’t be a need to become a true machine learning guru. But as the technology becomes more prevalent, there are many benefits to growing an understanding of how it works.
With a little guidance, anyone can learn to embrace ML as a solution for transforming business operations, and its effects are far reaching. When implemented correctly, ML has the potential to revolutionize the way businesses operate, make work more meaningful, and make employees more productive.
Machine Learning is Everywhere
Helping employees see the widespread use of ML can assist in winning over hesitant employees. Whether they realize it or not, they’re already using ML in some form.
Take YouTube, for example. Every time you like or dislike something on YouTube, you’re teaching a machine learning model about your preference. In doing so, you’re defining parameters that dictate how the machine will behave in the future, i.e., what videos it will recommend for you to watch.
Machine learning in the work environment is similar, only instead of helping a ML model learn about an individual’s preferences, users help the machine learn about business processes.
And just like with YouTube, the more data you “feed” the machine, the better it gets at producing accurate results. And because no one knows the business processes better than the user, they make the best teachers. With every input form they help the machine interpret, the solution grows smarter and more confident. After enough data has been provided, the machine functions as a co-worker that does what you taught it to do, over and over.
Demystifying Machine Learning
One of the primary reasons organizations are slow to adopt ML-based technology is because it’s misunderstood, and therefore, avoided. Instead of trying to understand a potentially transformative new technology, business leaders resist change out of fear of failure.
So how can companies overcome this hesitation? According to Nelson Ricciardi Parente, Hyperscience’s VP of Customer Success, one way to do so is through education.
Says Ricciardi Parente, “From the very first time the Customer Success team meets with the user, the goal is enablement. We’re going to teach you everything we know about the product and how to get the most out of it.”
To make ML-based solutions truly transformative, customers must be a part of the implementation. By getting involved early, users grow familiar with the technology, and can work with the Hyperscience team to roll out the solution in the most effective way possible.
People are wary of adopting new solutions because of the possibility something could go wrong, or because the new technology isn’t well understood. But according to Ricciardi Parente, much of this concern is misplaced.
“The truth is,” he says, “there’s no need for a ‘big bang’ implementation. As long as your existing solution still works, even if it’s not the most efficient, you can gradually move to something new. Eventually, you’ll look back at the old methods and say, ‘you know what, this new platform is more efficient. I don’t need the old process anymore.’”
Ricciardi Parente also says that it’s important these initiatives are viewed as a journey. He recommends organizations work to constantly improve, using realistic goals to reach key milestones. He calls this being “comfortably uncomfortable.”
“It’s a progressive journey, and it takes buy-in from the entire team. Don’t get too comfortable—walking one mile an hour when you can sustain ten doesn’t help anybody, but there’s no need to go 100 miles an hour out of the gate, either.”
What Skills Do You Need to Work with ML?
If being a technical mastermind isn’t a requirement, what is?
According to Ricciardi Parente, the list of necessities is quite short.
“All you really need is an appetite to learn,” he says. “That, and a mindset that is open to continuous improvement. Often, it’s as simple as changing the way we think. We’re creatures of habit, but sometimes that prevents us from doing our best work. By being open to trying new things, you’re 90% of the way there.”
Ricciardi Parente warns that business may encounter resisters in this area, especially with organizations where things have been done the same way for long periods of time. In those instances, he says it’s important for business leaders to take a step back in order to understand where this resistance comes from.
This is where a vendor’s customer success team can provide tremendous value, too. Machine learning is a team sport, and establishing a partnership relieves much of the burden when it comes to product training. By putting someone with strong business process knowledge (company employees) with someone who has a deep understanding of the technology’s capability (vendor employees), the technology can be applied in a way that maximizes its impact.
Making Employees More Valuable
We mentioned above the importance of securing buy-in at the team level, as it naturally eases users into a willingness to try something new. But securing that buy-in starts with just one person—the early adopter.
It’s usually easy to identify these individuals. They’re the first ones to raise a hand when something can be improved. They’re quick to try something new. They might even make suggestions for new technology based on their own research.
Once you’ve found your early adopters, you’ll want to connect them with the vendor’s customer success team. According to Ricciardi Parente, it’s the responsibility of customer success to come alongside early adopters and share knowledge.
“One of our mandates is to transfer knowledge, and the only way we can do that is if we’re willing to teach everything we know. It’s only going to work if both sides are personally invested in the other’s success.”
During the knowledge sharing process, early adopters become internal champions, creating opportunities for exciting new roles, and making them invaluable within an organization.
It doesn’t end with early adopters and champions, though. For others not as quick to the punch, there are still opportunities to learn and grow. Technical skills are always in demand, and by growing an understanding of machine learning, employees can take on additional responsibilities, as well as increase their employability.
The greatest boon offered by machine learning, however, is higher quality work. Automating repetitive tasks gives employees the opportunity to take on higher levels of responsibility, increasing their skillset and increasing their value to your company.
Mastering the Domain
When implementing machine learning, the most important thing Ricciardi Parente wants buyers to understand is that success requires teamwork between vendors and customers. Vendors must be willing to share as much knowledge as possible, and buyers need to be willing to step out of their comfort zones.
“That’s what we’re committed to. We reassure all our customers that we understand the investment that they are making and that we will teach them everything we know so they can be successful. We want to leave customers feeling certain that they’re equipped to succeed with the technology—we want them to be as good at using the platform as we are.”