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If you’re interested in how business automation, machine learning (or both) can help your business, it’s important to understand the difference between these two emerging data science technologies. Though they may sound similar, it’s important to learn their critical distinctions, how each can help different aspects of a business, and — if applicable — how they can become a powerhouse by working together.
Business process automation (BPA) uses technology that’s smart enough to follow established rules — it stays between the lines. It can quickly handle hundreds of straightforward tasks, but doesn’t necessarily know how to react to undefined situations. Machine learning, on the other hand, simulates human thought. It can “think,” “say,” or “do,” human-like tasks to create insights more powerful than imaginable. Machine learning can analyze and react to new information, and suggests solutions at a superhuman capacity. Automation collects and processes data, while machine learning understands and interprets that data. Together, they’re a powerful combination. Knowing their distinctions can help you understand how they could improve your bottom line, provide a competitive edge, or both.
Not to confuse the matter, but there’s another type of automation. Data scientists who implement business process automation create infrastructure that can lend to faster and more accurate decision making. Customers want instant information at their fingertips. BPA answers this demand by opening the door to back-end functions so they can retrieve data nearly instantly.
Like BPA, robotic process automation (RPA) also follows established rules, but in a very different way. Instead of creating new infrastructure, RPA takes advantage of software interfaces humans already use. RPAs can be great at accelerating data entry, where employees spend minutes (or hours) entering data into legacy systems, for example, that are too expensive or disruptive to replace. RPAs can do the same thing, but within seconds. Having clear business goals and an understanding of how software ages (with updates and interface changes) can help guide a choice between the two.
“As many as 45% of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.” — McKinsey
With 53% of employees saying they could save up to two hours a day with automation, there stands to be a lot of time — and money — saved. This means companies stand to grow revenue considerably when they implement automation. In addition to time and labor savings, think about how employees could spend time on creative or strategic operations, how your business could perform more efficiently, and what this means for productivity. At two hours a day, 10 hours a week, and about 500 hours a year, how could this impact your businesses bottom line?
Machine learning is a distance branch of artificial intelligence that uses algorithms to find patterns in huge amounts of data. Its use is only growing, and powers product recommendation engines, such as the “you may also like” suggestions on Amazon or Netflix. The positive effects of a well-trained machine learning system have helped companies grow, scale, and delegate their human resources more effectively. Common goals can include optimizing operational efficiency, increasing customer engagement, gaining a unique competitive edge, reducing risk or liability, or lowering the cost of resources. For marketers, it can unearth insights humans couldn’t due to its ability to sift through massive amounts of customer data.
Business automation doesn’t just affect low skill jobs, but can quicken the pace of activities performed by physicians, senior executives, CEOs and financial managers. Some employees may experience negative sentiment about adopting automation and machine learning technologies, so it’s important to understand how these can impact the organizational culture of a business. Smart leadership can get in front of organizational changes to redefine jobs and processes, plus give teams:
Imagine you combined robotic process automation (RPA) and machine learning to streamline client invoicing. Not only can it save time, it can nearly remove human error:
This process could save your accountant hours upon hours of time of the span of a year by transforming a task that took minutes into one that takes seconds. (Source)
“Half of today’s work activities could be automated by 2055.” — McKinsey
Automation for entire processes won’t happen overnight. McKinsey Global Institute analysts predict automation will touch nearly all occupations, but not immediately. Half of today’s work activities could be automated by 2055, but even that prediction is not certain. What can be automated depends on individual activities, as opposed to occupations. Here are three sectors where automation, combined with machine learning, are making significant impacts:
With automation, it’s important to pinpoint the tasks that can be done better by a machine. Break down the activities that can be automated. Deconstruct jobs into the type of tasks that are more amenable to automation. Repetitive activities can lend better to automation. Others, such as those that create unique solutions, or those that use change management frameworks, and analytical tool kits lend themselves better to cognitive automation.
As capabilities of machine learning progress, companies have been able to create success by presenting and predicting information in a way humans can’t (or can’t afford to) do manually. It can uncover deep insights that your human teams might not have time to find.
With a clear understanding of these two technologies, business leaders can move confidently toward creating goals — and finding solutions — to transform organizations into more efficient, cost-effective, and insightful operations.
Want to learn more? A Stratorsoft data scientist can speak with you to discuss what kind of technology could be the most powerful fit for your organization.
