Every moment spent in a business workflow consumes company resources. Introducing automation, Artificial Intelligence (AI), and Machine Learning can not only reduce operating costs but also increase productivity and reliability. Although at times the three terms automation, AI, and Machine Learning are used interchangeably, they are unique in definition and purpose. The implementation of each can change business forever.
Business process automation can allow a business to scale up its processes and also helps to stabilize the business. In many companies, there may be a process only a few people know how to perform, and if any of these employees left the business would be crippled. Automating business processes enables tasks to be better designed and protects company operations from being affected by employee vacancies. If a business process is automated, new employees can be quickly trained and make an impact sooner.
To better understand what automation, AI, and Machine Learning are and what they can do, below is a brief introduction to each.
What is Business Process Automation (BPA)?
Business Process Automation (BPA) is a very broad term that can be used to describe many different things. In general, business process automation can be defined as the ability to facilitate and automate repeatable processes. It comes in many different forms, but the ultimate goal is to complete repeatable processes with minimal human intervention.
For a business process to be automated, it should have the following criteria:
- The process is consistent and well defined.
- The process is repeatable without being subject to much change.
- The process requires error-free operation.
Many processes fit the criteria mentioned above. For example, a business that handles payroll for many hourly employees could completely automate their regularly performed payroll process. If the business had a system where employees logged the number of hours worked each day, week, or month, based on previously defined hourly rates, the entire payroll could be automated to process these employees’ paychecks every pay period.
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What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is a very broad term encompassing several topics. At a high level, it is the implementation of machines or systems that are capable of completing tasks requiring human intelligence. Not all computer programming requires AI because a lot of processes can be completed through a series of algorithms or steps.
Some business processes may require AI while others may not; it depends on the business process. To determine if a business process requires AI for automation, it should have the following criteria:
- The process requires human intelligence or reasoning.
- Due to changing data or inputs, the system is required to learn and improve over time.
- Complex problem solving is required that cannot be determined from a resolution tree.
A good example of this is spam email filtering. Emails can be filtered easily by a human but cannot be replaced by a series of algorithms. This application requires AI because the system must analyze the emails and through reasoning indicate whether it’s spam or not. An example of a process that does not require AI is sending an email after a form is completed. This does not require any reasoning other than a simple true or false statement for whether a form has been filled out successfully or not.
What is Machine Learning?
Machine Learning is a specific form of AI. Compared to AI, Machine Learning is a well-defined function for particular purposes. At a high level, Machine Learning allows computers/systems to analyze data, recognize patterns and form decisions on future cases based on previously analyzed data. Machine Learning improves over time as it gains more data. One major factor to the success of Machine Learning is the amount of existing data. Once again, this implementation should be used when a series of algorithms cannot be used to determine a solution. If solutions are determined on a case-by-case basis depending on previously encountered events, then Machine Learning is the right solution.
The factors to determine Machine Learning ‘s necessity are similar to those of AI. To determine if a business process requires Machine Learning to automate a process, it should have the following criteria:
- The process requires training for solutions to be determined.
- Due to varying factors, solutions are determined on a case-by-case basis.
- Existing data is available to help explain why each previous solution was made.
A good example is found in the financial sector: Machine Learning can be implemented to analyze and review historical stock data. Although the changes in a stock price depend on various factors, pattern recognition may be a factor in predicting its movement. After training, the system can look to identify patterns within different stocks to recognize and predict potential movements based on previously seen data.
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