What is the difference between robotization and AI?

What is the difference between robotization and AI?

Artificial Intelligence (AI), or artificial intelligence is a hot topic you see everywhere these days. More and more organizations want to start using AI, but other technologies are also often referred to, such as Robotic Process Automation (RPA). Confusion arises when the two terms are used interchangeably, which is why in this blog we will take a closer look at the differences between robotization using RPA and AI. But first, let's briefly explain what both definitions mean:

Robotic Process Automation (RPA)

RPA is a software solution that imitates human actions on a PC. You see the robot on the screen controlling the system, moving the mouse and "overclocking" data. Because the robot can control front-end systems, it goes beyond current automation. This robot is like a virtual employee that can perform any digital work of a repetitive nature. These are tasks that are common, add little value, but take a lot of time. They are work that involves many manual operations and the use of different applications and systems.

Artificial Intelligence (AI)

AI is more difficult to define. With AI, computers are used to perform tasks that would normally require human intelligence. Think of interpreting an unstructured e-mail that comes in, which without AI could only be handled by a human. AI provides the ability to use the computing power of computers to interpret unstructured data such as emails.

AI is also frequently combined with Business Intelligence. Think of questions such as: how many orders do I expect to receive next week (Based on weather, traffic, date, historical data etc.)? Which customers are threatening to leave our organization et cetera?

What is the difference between RPA and AI?

One obvious difference is that RPA cannot "think for itself" and perform repetitive work according to fixed rules. RPA is used for routine work that always goes the same way. The robot can detect exceptions and forward them to an employee, but it cannot process them itself.

AI is able to "think for itself" and assess unstructured data. For example, AI can process different PDFs, emails, passports and images. Ideal when, for example, different formats of invoices are used within your organization or when hard copy papers are still being used. A self-learning AI model is even able to evolve itself based on all the times the model is applied.

Because AI can be self-learning, it also learns from its own mistakes. Whereas RPA only performs actions that are pre-programmed and passes all exceptions to an employee, AI continues to teach itself the process and exceptions. If the AI model does not reach a reliable outcome, an employee can perform a further assessment based on which the AI model learns from this in turn.

Case study

Learning an AI model based on human judgment occurs, for example, in the case where an invoice arrives and the AI model knows with insufficient certainty that it has read the correct data. Because it is preset that only at a certainty of 99.5% or higher is it allowed to process the invoice, an invoice with a lower certainty is submitted to an employee. This employee can then indicate whether the data has been read correctly or not. If not, the employee indicates where the correct data was, so that the AI model learns where to get the data from next time.

In short:

• You deploy RPA in routine operations where employees are still processing, moving and checking data between systems and applications that are not (fully) integrated with each other;
• Combining RPA with AI automates work where employees still have to manually review and process exceptions (e.g. different invoice formats, etc.).

Collaboration between RPA and AI

The power of these technologies really comes out when both solution are used together! In the example below, you can see how RPA and AI reinforce each other.

RPA without AI

• A service request comes in through an external portal;
• Robot retrieves data from portal and asks employee to determine urgency;
• Employee determines urgency based on description of spill (interpretation text);
• Robot creates notification and schedule based on employee input

RPA & AI

• A service request comes in through an external portal;
• Robot retrieves data from portal and asks AI model to determine urgency;
• AI model determines the urgency based on the description of the spill (interpretation text) and gives a reliability score;
• When the reliability score is above y percent, the robot creates a notification and schedule based on input from the AI model. Lower than y percent? The robot requests a second opinion from the employee.

AI Builder (model) from Microsoft Power Automate

Microsoft Power Automate's AI Builder is a turnkey solution that makes the power of AI available in an approachable way. The AI Builder has a clear roadmap for building AI models to optimize business processes. This AI Builder adds the thinking power needed to read the documents. With AI Builder, you train the model to process various documents. After training and testing, the model is published and then used in Power Automate flows, for example. The results of these models come with a certain level of certainty. This allows you to know which documents go through fully automatically and which documents employees still need to review.