Getting RPA to the next level

 What are some of the key factors crucial to successful RPA deployments?

Automating specific business processes within an enterprise requires several moving parts to come together. At first, the ease with which tasks can be automated is an eye-opener for enterprises. This leads many organisations to automate too many complex tasks, too soon. The temptation to resolve long-standing business limitations leads to quick deployments without waiting for ROI to be measured. Since automation is a critical part of digital transformation, it’s necessary to have a strategic vision about how it should be implemented.

Process identification is a critical place to start, as repetitive tasks can be easily automated with RPA. RPA has the power to free up resources, redefine standards and transform business operations. But only if it is approached systematically and the enterprise is ready and has chosen the right vendor. Kickstarting a process of building proof of value is important to get more buy-in for the adoption of RPA.

Furthermore, building a culture of acceptance within the enterprise is important. This is critical for the expansion of automation and transformation of the business. Employees must be empowered to share the automation process through upskilling and enabling them to view bots as their tools. An upskilled workforce that is capable of running automation and has awareness about its benefits, will benefit from a holistic approach to automation. This can also be further augmented with an effective customer success team that can help boost automation initiatives.

What is the difference between RPA and IPA?

While RPA has been great for automating routine and repetitive tasks and it has delivered cost savings across industries, IPA, or intelligent automation, is the next step in its evolution. Intelligent automation provides an intelligent self-learning layer on top of RPA. For enterprises on an automation journey, it’s important to differentiate between the two and choose solutions that offer the best of both worlds. Purpose-built cognitive solutions that are inherent in a platform can help avoid delays in solving product issues and lower failure rates.

IPA leverages AI and other data science technologies to not only automate tasks but actually make smarter decisions and even pinpoint the right business processes to automate. It helps enterprises avoid the trap of automating complex processes too soon and raising expectations about the effects of RPA. Cognitive RPA pushes the boundaries of what RPA is capable of and hugely reduces errors and processing time. Cognitive RPA is also highly data-intensive and demands a lot of thought and upfront work to ensure success.

Is hyperautomation the next big trend in RPA?

Industry analysts, such as Gartner, have highlighted hyperautomation as a top strategic technology for 2020. It encompasses end-to-end automation that leverages multiple technologies from RPA to machine learning and AI. It extends beyond individual processes and creates a scenario where business processes are dynamically discovered and automated. In simpler terms, hyperautomation is designed to not just mimic human actions, but human intelligence as well. It paves the way for high-level digital transformation by getting complex business processes to work in harmony.

The potential business impact of hyperautomation is huge. It is changing the game by becoming highly data-driven and automating complex work that relies on input from multiple people. It further empowers enterprises by sorting through semi-structured data that is hidden in emails and other documents. By converting this data into actionable insights that can be further automated, hyperautomation is creating intelligent digital workers.

This is broadening RPA’s ability to solve end-to-end business problems, and it represents a significant step in the automation journey. The future is RPA + AI, and hyperautomation marks the beginning of that evolution.

What is driving the need for automation?

The Covid-19 pandemic has forced organizations to digitally transform with remote working and the need for business continuity. Adding to this is the fact that business needs are constantly changing in complex digital working environments. Customers are also placing higher expectations on enterprises and want them to keep up with their evolving habits. This is leading to enterprises receiving piles of incoming data from several sources. Enterprises spend a lot of man-hours sorting through this data and performing routine jobs that are repetitive in nature. Across different industries, these jobs seem different, but they all have a common characteristic – they are repetitive and high in volume. Automation can help tackle these issues by outsourcing these jobs to software bots so that human workers can concentrate on high-value creative tasks.

Not only does this free up resources to lower operating costs, but it also allows frees human workers to focus on work that is more creative and productive. In a competitive business scenario, automation gives enterprises the edge in the market. It enables them to better meet customer expectations and build loyalty. Automation that is scalable, reliable and repeatable has the potential to alter how enterprises operate now and in the future. RPA simply ensures that humans and bots perform the tasks that are the most suitable for them. This leads to faster time to market and product development at a faster pace.

How do you address scalability issues related to RPA?

Automating for success is a long-term game. While initial steps in the RPA journey must be ‘rule-based’ and process-driven, the long-term vision must be to implement AI and machine learning capabilities and analytics. This requires an effective scalability strategy. Looking for quick wins is not an effective long-term strategy and building Centers of Excellence (CoEs) to deliver proof of value is critical. Going from 5 to 500 bots requires a functional support platform that CoE’s are adept at providing.

Many enterprises make the mistake of automating too much too soon or trying to create a few complex bots that handle everything. This is based on assumptions on how RPA platforms operate. Effective RPA education and training can help navigate this. This brings a renewed focus on process identification in order to get bots to work with each other. Effective support structures behind the scenes can complement automation scalability. Having the right specialists, customer success teams and related technologies can make or break scalability challenges.

This can steadily build a chain of bots that can deliver maximum value. Enabling bots to be reused and redeployed for other tasks when the moment arises is also a crucial objective. Getting an automation platform up and running is the first step and relatively easier to manage. Scaling the automation is where the true challenge arises and that requires the best mix of people and technologies.

Attended or unattended RPA – what should users choose?

This depends on the nature of the task involved and the maturity of existing automation processes in the organisation. Attended automation empowers human workers by giving them ‘virtual assistants’ and configuring bots to work together with humans. On the other hand, unattended automation is where bots work and communicate with each other.

An organisation’s vision and buy-in play critical roles in choosing between attended and unattended automation. The decision-making process will determine whether employees are going to trigger bots or bots will perform rule-based processes. It’s common to see unattended automation controlled by CoE’s in order to run back-office tasks to reduce costs and improve ROI. Both are effective options to enhance productivity and lower costs and have a unique role to play in automating for success depending on where an enterprise is in its automation journey.