For a long time now, computers have been used to perform an assortment of tasks that are usually executed by human beings. However, this trend we know as automation has come with challenges, especially in regard to automating lengthier processes with multiple tasks, some of which aren’t monotonous in nature.

In response to that, we are witnessing the rise of what is known as Intelligent Process Automation, with up to 25% of organisations surveyed by Salesforce and Pulse using artificial intelligence for process automation, while 53% plan to do so soon. We are going to break down this technology and its relevance in modern-day business processes.

What is Intelligent Process Automation?

Intelligent Process Automation (IPA) is a set of various technologies used to automate more elaborate, end-to-end processes, primarily by complementing rules-based automation with deep learning and other cognitive technologies.

Fundamentally, IPA involves the combination of robotic process automation and numerous artificial intelligence technologies to automate complex decision-based processes that usually require human discretion.

IPA has the potential to drastically increase efficiency, boost worker performance, shorten response times, reduce operational risk and costs, and culminate in an improved customer experience.

What is IPA made up of?

Looking at the full scope of IPA, it consists of a number of technologies such as:

Robotic Process Automation (RPA)

This technology involves the creation of bots to automate straightforward rules-based tasks, mainly at user interface level. RPA can be used in data entry, document generation, accessing emails, and many other day-to-day tasks.

Artificial Intelligence (AI)

AI can be described as a group of technologies that enable systems to perform tasks which require human judgment, intuition and caution. With AI, a computer can gather information and apply logic to it to make decisions.

Machine Learning (ML)

ML is a technology that enables systems to analyse structured data using algorithms, and draw valuable insights from it, that subsequently help to improve the system’s performance. These algorithms may be supervised or unsupervised, meaning that they can discover patterns in data, with or without explicit instructions.

Natural Language Processing (NLP)

NLP refers to the software engines that facilitate smooth interactions between humans and computers by following specified rules to interpret language within a given set of data and induce decisions based on those translations.

Computer Vision

This is an AI-powered technology that helps computers to string images together and gain a higher level of understanding from them. The computer will then be able to take action based on its interpretation of the images.

Smart Workflow

Smart Workflow is a prepackaged process management solution that integrates the tasks done by machines and human beings. Consequently, users can start and track the progress of an end-to-end process in real time. This software handles exchanges between different groups such as humans and robots while offering statistical data on inefficiencies.

Cognitive agents

These are basically a result of combining machine learning with natural language generation to create an entirely virtual workforce. The agents in this workforce are able to execute tasks, communicate with each other, learn from data sets, and even detect emotion as they make decisions.

Benefits of IPA

McKinsey reports that numerous companies across various industries have adopted IPA, with automation of 50-70% of tasks resulting in 20-35% annual run-rate cost efficiencies. In that respect, here are some ways in which IPA typically benefits organisations:

Saving employees’ time

IPA helps to free up employees’ time, allowing them to focus on more creative and strategic activities. By combining Digital Process Automation (DPA) with AI, you can be sure that the right decisions are being made, with prior planning in workflows, and intervention from AI where necessary.

Improved administration and risk management

Firstly, RPA enables you to have tasks like data entry done automatically, eliminating the impact of human fatigue and errors on the work product. However, in scenarios where RPA is out of its depth, the additional technologies making up IPA can provide the required discretion to produce accurate results.

Therefore, IPA reduces the risk that can arise from human resources and poorly arranged processes.

End-to-end visibility

When using disparate automation technologies, it can be hard to keep track of the full picture. In the case of IPA, you have full visibility regarding every resource dedicated to a process or web of processes. This means that you can easily pinpoint the root causes of any issues affecting the overall user experience.

With IPA, you’re able to learn about your operations as a whole and make adequate adjustments in the right areas to achieve optimal results.

Speed and flexibility

IPA comes with the ability to learn and improve the approach to tackling problems, which shortens the time taken to complete certain tasks. In addition to that, IPA setups offer more flexibility, enabling you to single out a specific component and replace/upgrade it when there’s a need to alter the flow or manner of execution of a process.

This attribute encourages agility, as teams will have a much easier time responding to any hiccups in service delivery.

Harmonizing human and virtual resources

In a more minimalist setting with RPA alone, you’ll be relying on robots to execute individual tasks, often without streamlined contribution from humans where need be. But with IPA in place, it becomes much easier for humans to settle into their revised roles and synchronize their contributions with those of robots and other systems.

This is where smart workflow comes in handy, sitting atop RPA to help in administration of the process handled by RPA.

IPA use cases

Insurance – Chatbots with NLP capabilities are being deployed for appointment scheduling and also underpinning a self-service model as part of a wider effort to improve the customer experience.

Financial services – IPA is already being used to build more accurate credit models that support fair and profitable lending, enhance trades and routing, and harness analytics to comprehend client preferences and price sensitivity. It is also being applied in the onboarding process for customers opening new accounts.

Healthcare – The real time end-to-end visibility provided by IPA is helping pharmaceutical companies and device manufacturers to remain compliant, minimizing fraud and mistakes while heightening security. Automation in document management and regulatory monitoring is also improving drug discovery and vaccination development exercises.

How to kickstart an IPA transformation in your organisation

Luckily, IPA deals more with the presentation layer of information systems, meaning that you don’t always have to invest that much in infrastructure from the start. Technologies like RPA can even be layered over existing IT systems with minimal changes.

But while RPA systems can be up and running in as low as two weeks, a successful IPA transformation ought to follow these steps:

Agree on the IPA’s role in current operations

Before you get invested, it is important to examine your existing tools and procedures and ascertain that they are missing something that is best provided by IPA. By doing so, you’ll know what metrics to track and where or when to switch strategies once you apply IPA, otherwise, you’ll be going in blindfolded.

Cover the full extent of IPA

As you restructure workflows and processes, try to look for all the areas that can benefit from IPA. Sprinkling a few specks of IPA in some areas while leaving out others may not produce the best results. Create a roadmap with priority areas based on ease of adoption along with return on investment.

Build a Minimum Viable Product (MVP)

While you may have an idea of the full scope of IPA technologies you intend to deploy, it helps to start with the most simplistic version of these applications. Focus on improving speed and quality of results rather than building the entire product at once.

Even marginal gains in free time and reduction of errors over a short period of time can help to enlighten you on what works and what doesn’t, and also secure buy-in from the relevant stakeholders.

Maintain momentum and produce value

As you register some improvements due to applying IPA, stay oriented towards the customer qualms rather than the nice-to-have features for internal teams. When applying IPA to a particular area, ask yourself, “How does this ultimately help the user?”

Entrench long-term capabilities for sustainability

Always think about the future state of your operating model and create provisions to scale up over time. Build a collection of reusable solutions and conduct frequent training and sensitization exercises to foster a culture around IPA rather than having it viewed as a quick fix.

Coordinate communications and change management

Single out the right people to advocate for the change you’re trying to bring. Evaluate the cohesion in your teams and look for ways to improve communication in such a way that desired changes are implemented in a timely manner.

In conclusion, a successful IPA transformation will require the right mix between accurate, data-driven objectives, and an agile mindset. People need to be sure that they are taking the right direction, but also be ready to move when all signs point to a need for IPA.

Karamvir Singh is a proven technology leader with over 12 years’ experience designing and executing projects encompassing web, software, networking, and embedded systems technologies.  A native of India, Singh holds an electronics engineering degree and a post-graduate diploma from Centre for Development of Advanced Computing in his home country.  During his career he has built applications and helped launch start-ups in various industries, including education, finance, healthcare, and real estate, in many cases serving U.S. businesses remotely from India.  In 2018 he relocated to Australia to serve as Lead Web Developer for Innotech Control Systems in Brisbane.  Outside of his regular employment, Singh is always searching for new technology challenges.  He has built complex algorithms and written software code for various types of process automation.