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Robots At Work: What AI, RPA, and Machine Learning Mean for Business Process Management

AI has long been thought of as the future of the workplace, but the truth is this: AI is already here. Chances are, your workplace is probably already using some sort of artificial intelligence in the workplace.

Your email provider probably uses AI to filter out spam. The marketing department might use a chatbot to engage potential customers online. The worker in the next cube who is always listening to music? His streaming music service uses AI to serve up more of whatever he’s listening to. So AI is already at work, but in a lot of these examples, it’s coming into the office through a third party — a personal phone, a web application, a service your company subscribes to. 


What would AI mean for your workplace if it were directly and deliberately applied to your organization’s business processes? For answers, let’s look at the different kinds of machine intelligence available. 

Robotic Process Automation (RPA) 

Robotic Process Automation is probably the simplest version of a thinking computer. Using rules-based commands, RPA automates the sort of repetitive tasks that are performed the same way every single time. 

These are the sorts of mind-numbing administrative tasks that machines are better at than people because machines follow rules well and don’t get tired, and the tasks never vary. RPA is used for validation, for example, or for diagnostics. 

RPA is the business processes equivalent of servos putting together a car on an assembly line. Yes, human employees can tighten the screws on that one auto part, and they have done this work in the past, but servos can do it better and faster, and are less likely to make a mistake. Like assembly-line robots, RPA frees up human workers for more complex, creative tasks.

Machine Learning (ML) 

Machine Learning is smarter than RPA, because it’s a methodology in which machines actually learn. Under ML, software and machines detect patterns and adapt to the changes in those patterns. 

ML allows machines to do work that humans are not good at; taking in a lot of data, gleaning meaningful insights, and making decisions or suggestions based on that data. In this case, ML functions as a trusted advisor to human workers. Think C-3PO offering Han Solo statistical insights about the probability of successfully navigating an asteroid field, only with ML, the insights are welcome. 

In real life, this might look more like software mining historical data about the lifecycle of a purchasing contract and generating suggestions while a company is negotiating a purchasing contract. That way, the negotiators have relevant information fed to them while they are at the table. 

Artificial Intelligence (AI) 

That brings us to Artificial Intelligence (AI), the most intelligent of the bunch. AI allows software to make decisions with a human level of intelligence.

This is not to say that today’s AI is as smart as say, Lt. Data from Star Trek, but it does allow software to make basic decisions based on massive amounts of data, the sorts of decisions that would take time for a human to make. 

For example, take scheduling. There’s a lot of back and forth in scheduling a meeting, especially if several people are involved and they’re not all on-site. Maybe they’re even in different time zones. AI compares everyone’s schedules, looks at conflicts, converts time zones, and can offer a range of meeting times that work for everyone. If everyone’s schedules are updated, a long scheduling email chain is avoided.

AI is what it eats 

But what if everyone’s schedules are not updated? Then there’s still going to be a frustrating series of emails.

AI, ML, and RPA are only as good as the data — or processes — they’re based on. For example, Netflix’s recommendation algorithms only work as well as they do because they’ve got a lot of data to work with. The more you watch, the better Netflix gets at knowing what you’d like to see.

If you don’t have a lot of data, or if your data is not in great shape, there’s only so much that AI and ML can do for your business. Likewise, if your processes aren’t well-defined and repeatable, RPA won’t work well for you.

So, before you bring in the robots to make your job easier, make sure you’ve got what they need to do their job well: clean, well-maintained data ready for them to digest. Need help with that? We partner with business process automation experts K2 and can help determine if your business’s data is ready for AI. Learn more today. 

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Joe Halfman

About Author Joe Halfman

Joe Halfman is a former Business Process Management Consultant with Omni Resources. Joe is a graduate of the University of Wisconsin-Oshkosh with over 20 years of software development and consulting experience. When he’s not assisting Omni customers with streamlining and improving their business processes, Joe enjoys riding his mountain bike, golfing, and spending time with his family up North at the cottage.