Intelligent process automation refers to a range of technologies that can be used in conjunction with each other to significantly extend the use cases and future possibilities of automation.
By way of context, driven largely by a desire on the part of businesses to ‘do more with less’, the appeal of automation is huge. Current estimates suggest that three quarters of companies have introduced automation in at least one of their business processes, and many have multiple automation initiatives in the pipeline.
Especially over the last few years, and thanks to the emergence of advanced forms of AI, automation can now be extended to a much wider range of processes, including complex, unstructured, and decision-based activities. In simple terms, IPA allows you to use smarter bots across your business. Specific solutions that offer these capabilities often use the label, ‘Intelligent Process Automation’.
Here’s a closer look at what intelligent process automation actually means, the technologies it utilises, and some of the ways in which it can be put to work within your organisation.
What is Intelligent Process Automation?
Intelligent process automation (IPA) essentially refers to the coming together of two groups of technologies; robotic process automation (RPA), with artificial intelligence (AI), to enable the automation of relatively advanced business processes.
What Is the Difference between Robotic Process Automation and Intelligent Process Automation?
Automation of business processes is not a new concept; and one of the best ways of understanding intelligent process automation is by comparing it to ‘standard’ robotic automation.
The “dumb” robotic model relies on software robots designed to follow predefined workflows. They follow the narrow rules they are given, without deviation, and without any element of decision-making. This makes it an accessible and relatively low-tech approach for automating business processes that comprise simple, repetitive, and easy-to-define tasks.
Like RPA, intelligent process automation also uses robotics. Alongside this however, it also integrates a range of AI-based technologies (see below). This significantly expands what the bot is capable of; including things like making a choice between multiple courses of action, interpreting natural language, processing unstructured data, and learning from data fed into it.
RPA v IPA Example
To illustrate the difference between robotic process automation and intelligent process automation, we’ll take the office of finance as an example.
Most corporate finance systems offer the ability to automate a range of tasks. Functions that rely on basic RPA for this may include basic lifting and shifting of data (e.g. from a sales database into the accounting solution), issuing proforma payment reminders at scheduled times, calculation of payroll amounts, and generating payslips.
More advanced platforms may use IPA to enable you to automate a much wider range of unstructured, judgment-based tasks. Examples may include the ability to generate forecasts and budgets based on a combination of internal and external data sources, the processing of invoices that contain complex, unstructured data, and the ability to flag up instances of fraud by identifying unusual patterns or anomalies in transaction data.
What Technologies Are Used in Intelligent Process Automation Solutions?
Technologies used in IPA depend on the use case, and the level of complexity of the solution in question. Typically however, if a software vendor describes its product has having ‘intelligent process automation capabilities’, you would expect it to have at least some of the following elements in play:
Decision Management Systems (DMS).
These applications use a combination of AI capabilities such as advanced analytics, decision trees and complex event processing to apply a combination of business rules to real-time data to make automated decisions. Examples include analysis of credit applications in finance, and the assignment and prioritisation of tickets in helpdesk environments.
Advanced Analytics
Using a combination of predictive and prescriptive analytics, IPA-enabled applications are able to analyse potentially very large volumes of structured and unstructured data, identify patterns, generate forecasts, and make recommendations. Examples include the ability to provide personalised recommendations to customers in marketing. This category of technology can also help automate and augment your capabilities linked to strategic management of the business; for instance in the preparation of multi-year financial forecasts.
Natural Language Processing (NLP) and Speech Recognition
NLP can analyse sentence structure, context and sentiment, allowing IPA applications to interpret and respond to human language. Closely associated with this, speech recognition capabilities enable the system to listen to speech-based audio input, convert it into text, and process it using NLP. These types of capabilities are most often used in IPA applications such as customer service and sales chatbots, and IT help desks for employees.
Optical Character Recognition (ML)
OCR enables an IPA bot to scan and analyse the contents of a document, identify, interpret and extract relevant information, and process it further. This category of AI gives you the potential to automate a wide range of routine, frequently occurring administrative tasks that involve physical documents and images. Examples include invoice processing, document archiving, corporate mailroom sorting, and the sifting of job applications in recruitment.
Machine Learning (ML)
Machine learning algorithms are able to identify patterns or trends in data, applying this knowledge to make predictions or recommendations without being explicitly programmed. Basic RPA bots follow simple tools to complete simple tasks. In other words, they do not get smarter over time. In contrast, if it carries ML capabilities, an IPA bot has the potential to learn from the data it is exposed to, and become more accurate and useful to your business over time.
When Should Your Organisation Use Intelligent Process Automation?
When to use intelligent process automation depends on a number of factors, including the nature of the business process in question, the complexity of the task, and your desired outcome.
For example, in the case of a straightforward, rule-based task involving structured data, an RPA solution may be the most cost-effective and easy-to-deploy application to meet your needs. By contrast, for tasks that are more complex, perhaps involving unstructured data and requiring an element of interpretation or decision making, this is exactly the type of scenario where intelligent process automation can help you achieve your goals.
