Why Robotic Process Automation Is Not Artificial Intelligence

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Why Robotic Process Automation Is Not Artificial Intelligence

Artificial intelligence has become a buzzword and is increasingly overused, designating even low-level automation. This leads to misinterpretation of its capabilities. It is worth making a distinction between real AI and robotic process automation (RPA).
To put it simply, it’s the difference between a thinker and a
doer. RPA follows strict rules and executes flawlessly repetitive operations,
much like a good clerk. On the other hand, AI can do much more; it deals with vast volumes of information which it converts into actionable insights by detecting any underlying patterns and connections.
Another way to explain the difference between the two is stating that RPA only mimics human thinking by performing strings of tasks, while AI strives to replicate human decision-making processes.
It is not uncommon to be confused between the two since some
companies have deliberately branded their RPA products as AI to justify the price tag. True AI products are almost as valuable as a well-trained and
experienced employee, capable of providing support to junior staff members, guiding them through complex processes.
Ilya Kirillov, CEO of InData Labs, comments:
We help organizations deploy AI to empower employees to make decisions and complete non-trivial business goals with increased effectiveness. In different industries, AI solutions can be used to implement intelligent
document processing
, exceed clients’ expectations and, on the whole, contribute a lot to the automation and robotization of business processes.
It is not that one system is better than the other. Each has its specific
use cases, but it is essential to know the differences and select accordingly.

What is RPA good for?

As automation opportunities arise in every business vertical, software companies strive to create solutions accordingly. The fact that they call this artificial intelligence is not always accurate, however. Here are a few use cases which are more likely to fall under the robotic process automation category.

Customer relationship management

Onboarding of new customers comes with a heap of papers,
which are mostly the same every time. Therefore, this process can be
successfully automated and embedded into an online platform. This is usually less stressful for the customer as it can be done in their own time while the data stays integrated with your system. Since it is a matter of following rules and doesn’t require training with vast sets of data, it’s not AI.
For existing customers, RPA can take care of your CRM instead
of leaving this task to your sales team who can then focus on human interaction more. Keeping records in your systems accurate and up-to-date is a significant challenge where RPA can help. Simple changes like names, addresses, and e-mails don’t need a human operator: a customer can input them into a form, and the automation tool will put everything into respective boxes.
Last but not least, RPA can take care of the due diligence workflows,
including collecting, sorting, and organizing relevant data.


Financial workflows

Accounting is one of the most time-consuming and error-prone business
functions. Since it relies heavily on data entry and calculations, any tool
which ensures the automatic transfer of the information instead of using human input can lead to more accurate and cost-efficient results. Here, the role of RPA is to keep records updated and handle changes correctly in real time.
Other possible uses include document checks when it comes to
loan applications, insurance claims, and more. Automated systems are best for ensuring compliance since this basically boils down to a stream of repetitive, standardized tasks.

A step further: machine learning

Between RPA and AI, there is an intermediary step, which is machine learning (ML). The difference between RPA and ML is that the former is rule-driven, while the latter is data-driven. Machine learning is both a part and a precursor of AI, as it already incorporates prescriptive analytics and can be used to run decision-making engines.
However, this is not yet AI since it relies on sets of mathematical rules. ML algorithms become iteratively more accurate in recognizing patterns they are trained on.

Time for AI

The goal of all these intermediary developments is to create
software which mimics the human ability to make decisions, logical deductions, and act accordingly. IBM Watson and Google’s Deep Mind are brilliant AI engines which can be adopted for any industry. These can understand what they are seeing, classify documents, create plans, solve problems, and interact with objects, systems, and humans.
One of the straightforward applications of AI is natural language processing. Itmakes chatbots suitable replacements for humans in certain tasks, since bots can detect intent, understand requirements, and provide a personalize solution. Compared to RPA, which would require a numerical input from the user (“for English press 1”), AI learns as it goes by observing and comes with different answers depending on the context and previous conversations.
When it comes to document processing, AI can take heaps of
paperwork and sort it, much like a secretary would do. This is a major step
forward compared to just applying RPA tools for optical character recognition, which identifies symbols but have no idea what they mean. A useful AI tool can even be trained to spot potential errors in document processing and forward these to human checkers. Such a system could revolutionize insurance claiming, accounting and finance as well as medical record keeping.

Putting it all together

Knowing the differences between these stages of automation is important, not only to classify them but also to understand which is the best for a particular project.
We can expect that more complex problems will require using a
combination of them. For example, an AI chatbot interacts with a customer
instead of a call-center agent. In the background, a machine learning-powered recommendation engine finds the best matches for the customer’s needs, while an RPA tool makes the necessary updates in the customer’s profile for further reference.