Humans have always tried to better at things with a goal to be better today than yesterday and have an even better tomorrow. We are aware of the changes that bots are bringing in the way we work. Robots can take care of those mundane and repetitive tasks, offering humans more time to focus on creativity and variability. Robotic Process Automation or RPA is the software technology used to automate tasks and it can learn, mimic and execute rule-based business processes.
Hyperautomation is an extension of legacy business process automation beyond the confines of individual processes. It provides a high-speed route to engaging everyone in transforming the business, supported by automating more and more complex work that relies on knowledge inputs from people. Integration of disruptive technologies like Artificial Intelligence (AI), Machine Learning (ML), Natural language processing etc. with RPA, hyperautomation is achieved.


Humans are prone to making mistakes especially in the case of repetitive tasks and that are better when automated as bots seldom makes mistakes. It could be a huge financial burden on your business if you rely on hiring more and more staff for redundant tasks. Though the initial investment of creating a custom automation solution might be higher than hiring someone, it’s an investment that pays for itself over time and is completely scalable. Consistent results are a must to keep the client satisfied and it’s a lot easier with business automation to deliver a consistent quality service.

Many variables add to a business’s operational costs when it is being scaled. More number of manual process you have, harder it is to scale the business. When the staff will have more time to focus on things that matter, that leads to making businesses better while ensuring good collaboration. A clear responsibility distribution is possible with automation and that increases staff’s accountability for work done and makes quality control easier. It is indeed much easier to track automated and digitalized processes. The system knows the exact time, effort and resources required to carry out a specific task. Decisions when based on facts rather than guesses are better in determining a long-term vision for the organization.


RPA is task centric, and rule based. It fits in the space between a graphical user interface (GUI) where you enter data in an application or a website and the space of entering data into an application programming interface (API). So, the bots are working sometimes on top of the front end of an application just like a user would and sometimes they call the back-end APIs to take advantage of the functionality offered. AI has machine learning as its subset and is data-driven modelling designed to mimic human decision making. Hyperautomation fits in the space between RPA and AI where it’s taking the best of RPA and merging that with the capabilities of machine learning to not only automate human actions but also automate human decision making.

For instance, let’s take the case of a mortgage pre-approval process at a lending company. The process was previously a mess of manual work, copying and pasting data into word documents, and a non-standardized pre-approval process. It starts off with a customer calling the national call center for the lending company expressing his interest to buy a home for which he needs a pre-approval letter from them. The representative would take down information from the customer like their name, co-applicant and their income, price range of homes they are looking at etc. These data would then be mailed to a loan officer who will have to decide about that pre-approval. The decision about granting or not granting the pre-approval would then be sent to a person who will send the actual pre-approval or denial letter to the customer in a non-standardized format.

After implementing an automation process into the system, they automated several pieces of the pre-approval letter generation process. The customer now makes a call to national call center of the company and the agent there enters the details into the newly implemented system, alternatively, the customer could send in an application through email or through fax and those details could go through a bot. At this point, a bot is deployed for a partially automated decision making and it is trying to make a decision about the loan based on a hard-set value. If the item is approved the system will automatically generate the approval letter. In case the item is not approved it goes to a loan officer who will evaluate the application and will decide on the approval. Here we see a human involvement in decision making which can be used along with the available data to retrain our ML model. So, the going forward the process is getting improved and the next time a similar loan application comes through the bot should be able to make a sounder decision as opposed to having to send to a human again.

Legacy system automation is pain-point that most of the organizations find challenging. Following the microservices pattern could be considered by breaking the monolithic system into granular ones. Top cybersecurity considerations include blocking and tackling. Building blocks of cybersecurity should have vulnerability scanning, patch management, identity and access management and it should be brought into the system as soon as possible. Bots shouldn’t be running rampant; access, authorization needs to be controlled. Keep the tasks assigned to a bot narrow and have a population of bots assigned to each application and make sure bots have access only to things that they really need.

Are you ready for Hyperautomation?

Businesses are looking at Hyperautomation for maximum growth and agility. The ongoing digital transformation has increased the demand for automation of business operations across industries. Hyperautomation enables full-fledged and sophisticated automation. The impact –

  • Rapid and intelligent automation of all possible business processes
  • Creating digital workers with intelligent RPA
  • End to end automation of complex business operations and breaks business silos
  • Enables high collaboration among the workforce by connecting them and the data
  • Faster and accurate real time processing of both structured and unstructured data sets
  • Better visibility into the enterprise and thus enhanced and speedy threat detection
  • Enhanced quality of data driven insights and business intelligence
  • Enhanced Customer experience and supply chain management
  • Enables creation of a digital twin of the company and a lot more

If an organization is not doing any automation, first step is making sure that the processes are in place, appropriate scenarios documented so that any automation initiative could be successful. Then you may take a low hanging fruit approach and go after simple in-app automation. As a next phase build on it, incorporate draft or cross system journey. It shouldn’t be something where without the thought upfront and strategy that you should jump into. It’s important to understand where you want to get into but more importantly where you are today so that eventually all the investment will pay off.