Taking the first step is usually the hardest part when implementing change. And when the change involves stepping into the unknown, the first step can be even harder. But when others are seeing results from such a change, whether it be improved efficiency, revenue, scalability or another key success indicator, the potential change becomes a case of ‘when’, not ‘if’.
Few changes fit this mold more than robotic process automation (RPA). While it is a relatively new area for many, the gains many are seeing already are enormous.
53% of the respondents to a recent Deloitte survey have already initiated implementation, and over the next two years, as many as 72% are expected to engage actively in the automation venture. Five years from now, unless a dramatic change occurs, almost all businesses will have automated most repetitive, menial tasks.
Which makes a lot of sense if you consider the fact that early adopters already reported payback at less than 12 months, with software robots providing an average 20% of full-time equivalent (FTE) capacity. Moreover, the expectations of the Deloitte respondents have been met or even exceeded regarding RPA effectiveness when it comes to:
- improved compliance (92%)
- improved quality (90%)
- improved productivity (86%)
- cost reduction (59%)
From activities common to all organizations, such as HR / Payroll, Sales, Finance and Operations, to industry-specific activities, like the revenue cycle functions in healthcare, RPA can be implemented across a vast landscape of operational methodologies.
5 critical success factors to grow your business through RPA
Several factors are necessary for successful RPA deployment.
- An RPA Centre of Excellence, meant to provide a holistic, centralized vision of the goal-oriented RPA journey. The CoE can ensure the right level of centralization, which is likely to provide not just short-term process automation but also a coherent longer-term plan.
- Finding the right automation software. This is about evaluating the available offer and finding the best match with your own business objectives. A checklist allowing you to assess the vendors’, as well as the software’s specific qualities, always in relation to the goals that you previously spelled out, is a must to this end.
- A strong infrastructure support network is crucial for smooth scaling to enterprise-wide use of robotic process automation. The network should include a virtual environment, server hosting, and management, as well as service facilities.
- Security risk management must ensure that the confidential data that the automation platforms have access to (inventory lists, credit card numbers, addresses, financial information, passwords, etc.) is not misused via the privileges attributed to software robots or to those that develop the workflows for the robots. The management of security risks by vigorous monitoring is a top-priority issue for the development of RPA.
- A broad governance framework is essential to properly handle the pervasive effects of automation, e.g., changes in the organizational structure, process updates, variations in demand, communication with stakeholders.
11 real-world robotic process automation (RPA) use cases
This is a process that “calls for automation” because invoices are generated based on the company’s structured data. With faster and more accurate sales processing, payments can be made earlier, the cash flow is improved, and customers are likely to be more satisfied by quick and correct invoices.
Automated procure-to-pay leverages software robots’ integrative capacities. Bots can easily extract invoice and payment data from a variety of systems like enterprise resource planning, customer relationship management, banks, vendors, logistics companies, etc. Moreover, procure-to-pay automation warrants a single reference point for all transactions, and hence a unified assessment.
The process is crucial because it reduces churn and it motivates customers to actually use the products that you offer. Having robots deal with onboarding activities yields immediate and quasi error-free results, thereby increasing customer satisfaction. By means of optical character recognition (OCR) and cognitive automation, customer onboarding can be fully automated even in companies that rely on legacy systems.
Data entry and migration
The high costs of error reduction are a critical improvement to manual data entry and migration. Given its capacity for basic pattern recognition, and for text conversion into editable and searchable machine-encoded text, RPA can yield very fast results, while also minimizing the risk of error. This further enables improved data analysis and decision making.
CiGen has automated 95% of effort for migrating a legal firm’s database to cloud. The company reached ROI in 2 months, obtained 15 times faster processing time, saved 8 weeks of paralegal time per migration with 100% data accuracy.
In an economic company, data ought to be extracted from a variety of sources like personal records and files, operational performance datasheet, etc. Digital technologies like basic pattern recognition, OCR, or screen scraping facilitate data extraction by means of software robots and drastically reduce the need for manual tasks, with all their drawbacks.