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While the two work best together, AI has a wider reach and has more capabilities than RPA.
RPA and AI 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 concerning technology innovation and adoption.
It would be wrong to say AI and RPA will be needed by companies alone. Both their credibility and functionalities are requisite for smooth functioning.
However, let us now see when we specifically look for RPA and when for AI.
The Holy Trilogy
Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) are three distinct but overlapping areas of technology.
They get conflated, with people sometimes asking, “Is RPA AI?” To be clear, RPA is not AI but can be used to assist AI with simple tasks, as we’ll explain below.
Let’s look at what RPA and AI have to offer, how these complementary technologies are best deployed, and to what advantage.
Benefits of Automation
We have found the value of automation is felt almost immediately as organizations can:
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Increase throughput
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Ensure compliance
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Reduce costs
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Increase employee engagement
And with success comes the impetus to automate more processes.
As such, intelligent automation can make decisions as if it were a human because of algorithms and insights gleaned from data.
According to Deloitte, “Executives estimate intelligent automation will provide an average cost reduction of 22%.” However, they also found that, “organizations currently scaling intelligent automation say they have already achieved a 27% reduction in costs on average from their implementations to date.”
Automation of applications and processes, plus the automation of decision-making, forecasting, and predictions from multiple sources of structured and unstructured data in real-time, empowers organizations with greater productivity and accuracy in their planning cycles.
What Is Artificial Intelligence?
AI is viewed as a form of technology to replace human labor and automate end-to-end (unattended automation).
In other words, artificial intelligence refers to computer systems that can simulate human intelligence.
It uses unstructured data and develops its own logic.
AI can be used where the processes are highly variable and do not rely on a clear-cut set of rules, such as purchase decisions, language translation, etc.
Processes that rely on unstructured data from documents, articles, images, videos, and emails (such as invoice extraction, email routing, and speech to text).
What Is AI Used For?
AI is now opening doors for facial and speech recognition, voice recognition, voice assistants, and data analysis.
All of which provide excellent data capturing that can be used to trigger a judgment-based response.
It combines cognitive automation with machine learning, hypothesis generation, natural language generation, and algorithm mutation to create insights and produce analytics at the same capability level as a human, or even higher.
Cognitive reasoning engines apply the captured data extracted from learned patterns to make business and operational suggestions.
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 credits.
AI’s ability to aggregate, manage, and understand unstructured data sets it apart from RPA.
AI can go beyond ‘execution’ to ‘thinking,’ for example:
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Understanding documents
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Visualizing screens (including virtual desktops)
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Comprehending conversations
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Discovering tasks and processes to automate
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Processing language
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Handling semi-structured or unstructured data
Optical Character Recognition
A production environment — or any environment that relies on vendor relationships — can benefit from AI to analyze and select vendors.
AI employs OCR to gather and analyze data from multiple inputs in different formats and uses data analytics to compare vendor capabilities, reliability, and pricing.
What Is Cognitive Automation?
Cognitive automation is a progression of AI that uses large amounts of data, connected tools, diagnostics, and predictive analytics to create solutions that mimic human behavior.
Using natural language processing (NLP), image recognition, neural networks, deep learning, and other tools, cognitive automation attempts to mimic human behavior, including emotional reactions and other natural human interactions.
What Is Robotic Process Automation?
RPA is a software robot that can mimic human actions. The software is used to standardize and automate repetitive tasks. RPA robots automate the tasks as per defined rules.
Dubbed the digital worker RPA mimics human behavior to automate consistent routinized workflows to boost productivity.
The rules are programmed, and the bots can extract structured inputs from applications like Excel and enter them into SAP.
RPA robots perform the same way every time. They don’t learn from one repetition to another, and they will not improvise or come up with a better way of doing their programmed task.
However, they can execute a high volume and wide array of tasks accurately and complete tasks 4-5 faster.
Every automation project has to start somewhere.
Whether the focus is on one process or several, robotics process automation solutions can help you pinpoint the most optimal processes to automate and implement at your pace.
What Is RPA Used For?
RPA tools perform logical tasks that don’t require knowledge or human understanding.
By itself, RPA can adeptly handle some of the most common, and time-consuming, processes that support your business, such as:
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Logging into applications
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Connecting to system APIs
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Copying and pasting data
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Extracting and processing structured content from documents
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Opening emails and attachments
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Scraping data from the web
Every industry has short, repetitive, manual processes that could benefit from RPA, but companies within the banking, financial services, insurance, and telecom industries are the highest adopters.
Mortgage lenders, for instance, are using it to verify loan documents, and financial organizations are employing RPA for bank reconciliations.
Instead of making people redundant, RPA robots are more like virtual assistants who let you offload repetitive tasks that aren’t complicated but consume valuable employee time.
Is RPA A 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.
RPA technology, especially the top RPA tools, continues to progress and develop more advanced features, making it confusing when looking at similar technologies, like AI.
Thanks to machine learning, SPA (also called intelligent process automation, or IPA) enables an automated workflow smarter than RPA.
RPA is the entry-level of automation, so once you have it deployed to manage your simple processes, you can use artificial intelligence to take care of any process or action that requires more complexity.
RPA vs. ML
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?
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 conclude.
Machine learning can be used for training classification systems, classifying documents, extracting information from documents, and similar tasks in a tax context.
The more tax bills you provide, the more accurately the model will predict 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 assessment notices.
This example shows the lack of human-like interpretation to recognize the similarity between the documents.
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.
RPA vs. AI
RPA is a process-centric technology because RPA is all about automating repetitive, and rule-based business processes.
The agile RPA software has gained tremendous popularity and success due to its ability to continually complete repetitive processes that would otherwise be completed manually.
Thus, freeing employees of repetitive tasks and eliminating human error.
For example, RPA generates bills or processes invoices, and so on. AI is known as data-driven technology, which provides good quality data.
For example, AI helps read bills and invoices and extract their data to convert it into structured and intelligible information.
Another example would be AI’s ability to send a thousand emails and notice emails that start with “Dear” are more likely to ask for a refund than messages that start with “Hello.”
An RPA bot working from this AI analysis could be programmed to route “Hello” messages to one person in the office and “Dear” emails elsewhere.
RPA and AI are valuable technologies that can be used for the organization’s digital transformation.
The best way to define AI would be to say it is an umbrella term that describes several technologies like RPA and a computer’s ability to learn and mimic human thinking like judgment-based decisions, reasoning, and cognition.
In essence, artificial intelligence is like a human brain.
It carries out the “thinking” process to make decisions and judgments based on the information that it has available (i.e., data, patterns, trends, analysis).
On the other hand, robotic process automation completes tasks based on rules, a.k.a. like the human body.
Combining RPA And AI
Since organizations have both structured data (e.g., form fields) and unstructured data (e.g., free text, natural speech), many processes require RPA and AI to fully automate a process from end to end or improve a robotic process once it has been deployed.
When paired together, AI and RPA transform organizations immensely to reduce costs, streamline workflows, and achieve operational efficiency.
A Future Expansion of Artificial Intelligence
Increasing demand for business process automation (BPA) through artificial intelligence (AI) and software robots is anticipated to be the key growth-driving factor for the market.
More cognitive capabilities are needed as organizations look to automate more complex business processes with data from unstructured sources (like scanned documents, emails, letters, and natural speech).
The future of RPA and AI is glowing increasingly brighter, as more businesses continue to demand solutions that boost productivity and reduce cost.
The technologies that continue to emerge from AI will soon bring on the complete automation continuum that businesses are desperately searching for.
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