AI and RPA: How they work together, and why your business needs both

According to a report by Goldman Sachs, AI can increase global labor productivity by more than 1% per year and may attract more than US$200 billion in investment by 2025. At the same time, While active in a much narrower field than ubiquitous AI, the RPA market will exceed $14 billion by 2029.

From a personal perspective, these two independent technologies redefine the goal of achieving process excellence in the work environment. Discussions about business process automation can sometimes be summed up simply as “AI vs. RPA.” Specifically, artificial intelligence (AI) and robotic process automation (RPA) have their own pros and cons as alternative solutions. AI technology can imitate human intelligence and handle complex tasks and data analysis, but its implementation and maintenance costs are high and there is still a certain degree of uncertainty. In contrast, RPA can be quickly deployed and integrated directly with existing systems at a lower cost, but for complex tasks and solutions it is an effective but ultimately limited approach. Individually, AI and RPA can be effective in streamlining processes and automating tasks, but their combined ability of "Intelligent Process Automation" (IPA) to discover and deliver value previously hidden in business processes may Truly transformative. Many companies have realized this fact, driving the rapid growth of the IPA market. It is expected to be worth approximately $37 billion by 2030.

This article will explore the interplay between intelligent automation, artificial intelligence (AI) and robotic process automation (RPA), and the advantages they bring, and the benefits of intelligent processes they bring to the enterprise.

What is Intelligent Process Automation?

First, in order to illustrate the enhanced capabilities provided by intelligent automation, it is important to define traditional RPA.

Traditional Robotic Process Automation deploys software robots (or "bots") to perform high-volume, repetitive, rules-based tasks common to many business processes or workflows. The tasks targeted by RPA tend to be data-intensive and therefore more prone to human error, they have few exceptions or variations in processing methods, and their data structures are consistent. These tasks include data extraction and transfer, standardized reporting, or website scraping. .

By deploying RPA technology, these tasks can be completed in greater quantities, faster, and with higher accuracy than manually. This can significantly increase productivity, reduce costs, and increase process scalability, while RPA tools free team members from the repetitive tedium of these routine tasks and focus on higher-value work that requires their judgment and expertise. .

Therefore, traditional RPA can provide a lot of functionality, but it also has obvious limitations in scope. In addition to being limited to relatively simple tasks, there is another important consideration: RPA robots cannot think. , instead, they do exactly what they are told, and only what they are told.

They neither think nor interpret information outside the parameters of their specific automation. Likewise, software robots do not react to changes in the process ecosystem with which they interact unless instructed to do so. This means that even small process changes, upstream or downstream of automation, can dilute or destroy their impact.

Intelligent Process Automation - Entering the AI ​​Era

Intelligent process automation combines the ability of RPA to automate simple tasks, processes or workflows with the judgment and learning capabilities of AI. While AI handles key process decision points, the robot performs the routine work required at each step. By folding decisions, this cognitive automation can handle more complex tasks faster. AI algorithms can be taught to reason about the process. anomalies and changes and determines the appropriate action to take - instructing the software robot accordingly.

AI’s application of contextual understanding and logical reasoning enables fast, rational decision-making, and machine learning (ML) algorithms mean that, over time, AI systems can learn to make better decisions - Better decisions aligned with business success metrics. By efficiently detecting and resolving process anomalies, AI intelligent automation ensures continuous optimization of the effectiveness of RPA robots.

One of the big advantages that AI brings to the process automation table is its ability to extract accurate understanding from unstructured data and inputs. The AI ​​toolbox is filled with innovative applications that enable this, including computer vision, natural language processing (NLP), speech recognition, intelligent document processing (IdP) and optical character recognition (OCR).

So, whether it’s a free text form field, an email, a business document, or even a live customer query, AI can extract and clean relevant information, and then RPA bots can easily use (or trigger) these cleanses The final data is used to automate the process.

Intelligent Process Automation Examples

The number of use cases for intelligent automation is vast and growing, however, some of the more typical broad applications include:

· Finance/Accounts: AI can read a supplier or customer’s invoice and extract key details such as the amount owed, due date and purchase order number. On the accounts payable side, RPA bots then use this structured data to validate purchase orders, calculate payment totals, submit payments for approval, and process approved payments. For accounts receivable, the robot uses this information to send automated payment reminders, reconcile incoming payments, and flag overdue payments to the AI ​​system for collection. AI-driven smart processes are also very effective at preventing non-compliance with strict financial processes. For example, AI can detect whether an invoice has been checked and approved by the same employee and flag anomalies - potentially sending an RPA bot to prevent a payment.

Customer Service: An increasingly common scenario is for NLP-enabled chatbots to interact with customers, collect information and handle regular inquiries. Historically, it would pass complex questions to a human agent who would use relevant information gathered by RPA tools to efficiently process the query. However, there are new challenges in GenAI, large language models (LLMs), language processing and predictive analytics. Advances mean that, in some cases, AI can directly handle customer interactions with near-human realism - similarly, obtaining relevant contextual information from RPA bots.

Human Resources: Intelligent automation can streamline many human resources processes. For example, in recruitment and onboarding, AI will conduct background checks on new employees and identify any issues. At the same time, Bots help onboard new employees by providing accounts, populating databases, and preparing onboarding materials tailored to each employee.

IT Support and Security: IT professionals around the world face a never-ending battle to avoid simple routine requests for passwords, access provisions, and ticket update requests. Issues get buried in help desk tickets. With intelligent process automation, the AI ​​chatbot can handle many IT support requests and diagnose common issues. It can trigger the RPA bot to reset passwords, provide access and update help desk tickets, all operating within the enterprise’s IT compliance protocols. . IT system outages and malicious attacks are major threats to business continuity. AI technology can be used as the first line of defense, monitoring system status and user behavior in real-time to detect any potential issues, anomalous data access or suspicious activity - providing alerts for human actions and triggering security protocols to protect critical infrastructure.

Two-lane road - AI benefits from RPA

As mentioned in the previous sections, AI has greatly enhanced the capabilities of RPA software. With the addition of AI, the scope of effectiveness of RPA bots increases exponentially, and the bots evolve into something closer to digital workers, capable of making decisions with an eye toward self-improvement.

But it’s a mistake to think that benefits only flow from AI to RPA. Here are just some examples of how RPA provides key support functions to make AI work more smoothly:

AI training Data: RPA robots can quickly collect, clean, standardize and label training data from multiple systems to support AI systems and their decision-making capabilities, which saves a lot of time in manual data preparation.

Connecting legacy systems: RPA can integrate legacy systems with newer AI tools that may lack connectors or APIs to access older technologies.

Understandability: One of the hot topics in AI circles is black-box AI, or decision-making transparency. RPA bots can track the steps taken by an AI model and explain how it arrived at a specific conclusion.

Humans in the Loop (HITL): RPA bots can be programmed to act as a safety net for critical decisions where the AI ​​takes action - specifically flagging potentially suspicious AI output for human review, e.g. , if the AI ​​approves a loan to a customer with a poor credit history, the rule-compliant RPA software set up to review the loan application may flag it as non-compliant (and risky). RPA bots help bring automation and AI together by requesting human review, approval or exception handling for questionable AI output.

Monitor AI performance: RPA bots can track the performance of AI systems, watch if errors or deviations creep in, and flag data issues. AI systems make decisions based on the data they are trained on. Sometimes, if the data the AI ​​learns from changes, errors or biases can creep into the AI's logic over time. RPA robots can be programmed to continuously test and track how well the AI ​​is working.

In short, RPA software can enhance, guide and monitor AI technology, ultimately transforming it into intelligent process automation solutions.

Secrets to Intelligent Automation Success

The business advantages brought by the combination of AI and RPA are unquestionable, however, it is important to maximize the ROI of automation investments and unlock hidden aspects of enterprise processes All value opportunities require a third player in the ensemble - process intelligence.

Process Intelligence combines detailed process mining insights with standardized process knowledge to provide AI with the language and learning materials to understand, optimize and automate end-to-end processes.

Process Intelligence Graph generates a "digital twin" of an enterprise's processes across every application, business function and location in the ecosystem. It provides real-time, data-driven insights into what is actually happening in the enterprise. How things work and how processes interact.

If an AI implementation is only as strong as the data fed to it, the Process Intelligence Graph ensures an ideal, evolving data foundation from which the AI ​​system can orchestrate and activate RPA bots.

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