RPA: Deploying the Potentials of Automation for Business Efficacy

Robotic Process Automation or RPA is one of the top technologies in today's market, and slow-growth to hypergrowth organizations are adopting automation in their day-to-day tasks its value is increasing at a great pace. RPA technology allows a software robot to mimic human behavior. It can navigate enterprise software like ERP systems, FSM software, or service management tools using the application's user interfaces just like a human would. However, a robot is able to work much faster, and more efficiently without ever slowing down.

Counting on its benefits for corporate environment, recent industry research on accounting and finance professionals found that in reality, RPA software has huge potential to eliminate the most time-consuming and repetitive manual processes that make up an accountant's day-to-day work. Robotic Process Automation can improve efficiencies to deliver more accurate intelligence data and also provide real-time access to financial data with reporting and analytic capabilities.

As the amount of financial data keeps on increasing because of the Big Data boom, this technology can help finance professionals to start adding real value from a strategic viewpoint and start contributing more towards the bottom-line of their company.

What to Automate first?

RPA automates actions, inputs, behaviors, etc., in the user interface. It is ideal for integration projects that require human manipulation of user interface elements and involve heterogeneous systems. RPA technologies exploit standard APIs, so integrators won't have to tinker with existing applications, workflows, processes, or system architecture. In this way, RPA can help reduce the amount of work (along with much of the risk) necessary to integrate heterogeneous applications and systems. This is one of the biggest factors driving the cost-effectiveness of RPA implementations.

RPA is not a panacea. Some tasks, even if tedious or repetitive, will require manual human oversight and control. As always, careful analysis of in-process workflows enables you to determine the best overall candidates for robotic automation.

In general, the following are good places to start with RPA:

Rote and repetitive tasks. Pointing-and-clicking a mouse. Copying-and-pasting text.

Testing and validation. Some visual interfaces require substantial time and effort to test prior to deployment. RPA can radically accelerate this process, improve testing, and reduce costs.

Redundant tasks. Basic tasks that multiple users tend to perform in parallel in an organization.

Manual tasks with limited variability and few exceptions. Tasks that are consistent, repeatable, and/or highly predictable are excellent candidates for robotic automation.

Human-orchestrated "integrations." A user manually copies data from one visual interface and pastes it into another, or a user manually imports the output of one program into another.

Any tedious or time-consuming task that looks like it might be a good candidate for automation. Automating the task should free one or more humans to do more productive work.

How RPA Impacts C-level Decision Making?

Though automation software is expected to replace up to 140 million full-time employees worldwide by 2025, many high-quality jobs will be created for those who maintain and improve RPA software.

When software robots do replace people in the enterprise, C-level executives need to be responsible for ensuring that business outcomes are achieved and new governance policies are met.

Robotic process automation technology also requires that the CTO/CIO take more of a leadership role and assume accountability for the business outcomes and the risks of deploying RPA tools.

Additionally, the COO, CIO and chief human resources officer, as well as the relevant C-level executive who owns the process being automated, should all work toward ensuring the availability of an enterprise-grade, secure platform for controlling and operating bots across systems.

What's next: How RPA is evolving

When RPA first arose as a category, it evolved from macros that automated simple tasks into programmable bots based on a set of human-defined process rules.

These bots helped improve efficiency in isolated situations, but organizations soon struggled on two fronts. First, discovering and defining processes for the bots to automate—at scale—has been a challenge for RPA from the start. Second, the management of the bots themselves and the process-defined rule-sets that direct their actions have become a big bugbear.

This is what has led to the growth in RPA platforms, which can help on both fronts. RPA tools help automate the discovery of the processes and provide tools for line-of-business users to more easily build automations based on their process needs, often based on pre-built bot libraries. Additionally, platforms define rules that govern and orchestrate the way bots run.

RPA vendors are trying to flex the limits of process definition by developing machine learning capabilities to automatically discover and learn processes. Increasingly, vendors are building in the ability to record and analyze user actions and then use machine learning to automatically define process rules and reduce the number of manual steps.

However, the heavy lifting still typically falls on business stakeholders and the automation team to get things rolling.

Usually, however, RPA projects require the aid of consultants and integrators, which is why analysts project a threefold increase in spending on RPA software through 2022.