When even toothbrush manufacturers now claim to use artificial intelligence (AI) in their products, it’s a sign—not that AI has come so far as to be ubiquitous, but that there’s definite confusion about the meaning of the term.
AI, robotic process automation (RPA), and machine learning are distinct but related concepts, and the names are increasingly being used interchangeably (and incorrectly). (Tweet this!) That makes things particularly confusing for businesses looking to stay ahead of the curve. Here, we cover some of the basics concerning RPA vs. AI vs. machine learning, including definitions and the most common uses of each. Understanding the difference between RPA, AI, and machine learning tools will help you identify where the best opportunities lie for your business, so you can make the most of your next technology investment.
RPA Vs. AI Vs. machine learning: What’s the difference?
Robotic Process Automation
Here’s Gartner’s definition of RPA:
Robotic process automation (RPA) is a productivity tool that allows a user to configure one or more scripts (which some vendors refer to as “bots”) to activate specific keystrokes in an automated fashion. The result is that the bots can be used to mimic or emulate selected tasks (transaction steps) within an overall business or IT process.
RPA tools perform highly logical tasks that don’t require knowledge or human understanding. For example, if you traditionally input account numbers onto a spreadsheet then run a report with particular filter criteria, you can automate the process so those numbers are filled in ahead of time in a grid. RPA will then mimic your actions of clicking on buttons and setting up filters, and generate the report for you. RPA is appropriate for any type of task that can be easily performed if there are clear conditions associated with carrying it out, such as: “If this is true, do this. If this is false, do that.” It’s important to note that RPA tools don’t learn as they go along. So if something within a specific task changes—for example, a form field is renamed or a data source changes—the RPA bot will have to be reconfigured to continue to work properly.
What is RPA used for?
Every industry has short, repetitive, manual processes that could benefit from RPA, but the highest adopters are companies within the banking, financial services, insurance, and telecom industries. Mortgage lenders, for instance, are using it to verify loan documents, and financial organizations are employing RPA for bank reconciliations. (See more examples here.)
And thanks to the U.S. government’s recent mandate to shift its employees’ focus from low-value tasks to high-value ones, even federal agencies like NASA are starting to adopt RPA to reduce repetitive administrative tasks. (Here’s more about NASA’s RPA experience.)
What to know before you invest:
RPA is appropriate for organizations looking to reduce the time employees spend on low-value activities, and improve the efficiency of daily operations. Though it is more limited in its capabilities than AI, RPA also costs less to implement. It can typically be overlaid on your existing IT infrastructure, or it may be embedded within a newly acquired software application. Neither option usually requires a complex integration process.
Gartner’s definition of AI is:
Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.
In other words, artificial intelligence refers to computer systems that can perform human-like tasks. They can intake large quantities of data and, on their own, build algorithms that help determine the right way to perform a task.
Thus, the main difference between RPA and AI is intelligence—both technologies perform tasks efficiently, but only one is capable of doing so with some semblance of human intelligence.
What is AI used for?
Virtual personal assistants and chatbots are two popular ways in which AI is currently being leveraged in the business world. In the tax world, AI can make tax forecasting more accurate with predictive analysis; it can also perform in-depth data analysis, making it easier to identify tax deductions and tax credits. As the technology continues to develop, no doubt organizations will find an increasing number of ways to make use of it.
What to know before you invest:
In the long run, an AI solution can be trained to learn about your business and potentially deliver valuable business insights. However, many companies find they are unprepared to capitalize on the benefits of AI because they don’t have the right processes and people in place to support these implementations. In general, introducing a true AI solution into your business requires a larger cultural shift in mindset and broad support across all leaders and departments.
Gartner defines machine learning as follows:
Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.
Machine learning is a component of AI, so it doesn’t make sense to use the two terms interchangeably. The difference between RPA and machine learning is that RPA lacks any built-in intelligence, while machine learning’s intelligence lies somewhere between RPA and AI.
Note that machine learning uses structured and semi-structured historical data to “learn” and make predictions without being explicitly programmed. However, it falls short of AI’s capabilities because machine learning only works within predefined knowledge areas.
For example, consider these technologies in a property tax context. You can create a machine learning model based on thousands of tax bills. The more tax bills you provide, the more accurately the model will make predictions for future tax bills. But if you use that same model to address an assessment notice, it won’t know what to do. You would need to build a new machine learning model that learns how to deal with notices of assessment.
This example shows the lack of human-like interpretation to recognize the similarity between the documents. An AI application would likely recognize it, but this falls outside machine learning’s capabilities.
The main difference between RPA and machine learning is the presence of some level of intelligence or ability to learn. In addition, AI is distinct from machine learning because it can exhibit human-like thinking and handle complexity.
What is machine learning used for?
In a tax context, machine learning can be used for training classification systems, classifying documents, extracting information from documents, and similar tasks. It can classify documents based on historical ground truth that algorithms have been trained on. For example, you can train a machine learning algorithm to identify a document type by training on a set of previously labeled known documents.
What to know before you invest:
Before investing in machine learning, consider the cost of training your machine learning models. While not as expensive as taking the AI route, you still need to review your budget. In addition, it’s essential to have clean data available to train your models—all data points should be accurate and labeled correctly. Training your model with unclean data will impact its effectiveness.
SPA: Bringing Robotic Process Automation & Artificial Intelligence Together
Is RPA part of AI? The answer to this question is no, but RPA and AI can work together. The integration of the two technologies leads to a third concept, called smart process automation or SPA, which extends the scope of RPA.
SPA (also called intelligent process automation, or IPA) enables an automated workflow smarter than RPA thanks to machine learning. (Machine learning refers to teaching computers to progressively improve their performance on a task by training them to detect data patterns or relationships that will help them draw conclusions.) If RPA solutions can be characterized by “doing” tasks, machine learning solutions focus on “thinking and learning.” SPA solutions can “learn” how to perform a task by relying on patterns and inference. That means the computer can start to predict how a human might respond in certain instances, helping it to perform the activity with a greater degree of accuracy and speed. Categorizing data is one example of a task where machine learning comes in handy—it’s repetitive and time-consuming, but it also sometimes requires a degree of cognitive ability.
AI, RPA, and SPA all present exciting possibilities for the future of work. But making the right choice for your organization requires understanding your processes and needs, and where your business stands with regard to technology innovation and adoption.
RPA + SPA = Advanced Software For Property Tax Professionals
RPA and SPA are becoming more commonplace in tax practices. The daily work of data entry, information tracking, filing returns, and processing tax bills keep property tax professionals busy—so busy they aren’t able to utilize their skills to the fullest extent. Automating all these things makes it easier to comply with jurisdictional requirements, and allows your skilled workers to focus on strategic work rather than administrative tasks.
- MetaTaskerPT is the only property tax software that uses advanced SPA technology to automate data entry. It extracts all available strategic content from your property tax documents (structured and unstructured) and verifies it for accuracy. MetaTaskerPT performs data extraction faster (typically less than a 24-hour turnaround time) and more accurately (greater than 99 percent) than an in-house team of humans.
- TPT incorporates RPA technology to simplify tax management and compliance tasks. It allows teams to automate a number of foundational (but low-value and repetitive) activities, including report generation, data validation, form population, and bill payment.
TotalPropertyTax and MetaTaskerPT are changing the game for tax practitioners, both modernizing their processes and helping them become faster and more efficient. You can learn more about our software—and how it can help your organization’s tax team—by visiting our website or talking to us.