Corporate accounting functions often deal with repetitive and time-consuming tasks such as data extraction, data transformation, data entry, and data reconciliation, which are good candidates for RPA.
RPA can be integrated with existing systems relatively easily and streamline these tasks based on rule-based determinations reducing manual interventions and ensuring accuracy. RPA can operate 24/7, enhancing efficiency and allowing accounting professionals to focus on higher-value activities and analytical aspects of their work.
Despite many benefits that Robotic Process Automation (RPA) can provide, if it is not rolled out properly, you may end up creating more business challenges rather than remediating existing ones. Below are some of the key things you must take into consideration when you deploy RPA.
Have clearly defined objectives.
Clearly defined objectives will guide the selection of suitable processes, ensuring alignment with organizational goals. They will also provide a roadmap for project planning, helping allocate resources efficiently and manage stakeholder expectations and serve as measurable benchmarks for success, enabling organizations to evaluate the impact of RPA on productivity, cost reduction, and performance. Well-defined goals enhance communication across teams and contribute to a focused, strategic RPA implementation, fostering a smoother integration process and maximizing the technology’s transformative potential.
Consider what level of automation, what level of development effort, and what level of efficiency gain you would like to achieve. Let’s say you can automate 80 percent of your payment process within 2 weeks of development, and the efficiency gain is 6 man-hours. If you automate 100 percent of the same process, it will take 4 weeks of development, and the efficiency gain will be 6.2 man-hours (compared to 6 man-hours based on 80% automation). Is it worth the effort? Bottom line is you should not automate just because you are able to automate.
Understand RPA’s limitations.
You should also understand not all processes can be easily automated using RPA due to its limitations. For example, RPA relies on structured data, making it less effective with unstructured content. Also, RPA lacks cognitive abilities for complex decision-making, limiting adaptability to dynamic scenarios. It requires precise rule-based processes, hindering automation of creative or non-repetitive tasks.
Consider deploying other technologies in conjunction with RPA. For example, you can use a data transformation application such as Microsoft Power Query to handle data formatting, aggregation and analysis, and keep RPA to prepare and post entries, and distribute reports to stakeholders.
Streamline the process first.
RPA works most effectively when applied to streamlined, optimized workflows. Redesigning processes allows organizations to eliminate unnecessary steps, simplify complex tasks, and standardize processes for automation. It facilitates a more efficient and coherent integration of RPA, avoiding the automation of inefficient or outdated practices. Furthermore, process redesigning processes ensures that RPA complements human roles, promoting collaboration and enhancing overall productivity. This iterative approach not only maximizes the value of automation but also positions the organization for long-term efficiency gains and improved business outcomes.
Focus on governance and control.
A well-defined governance structure is indispensable for sustaining RPA success and realizing long-term value. Establish formal policies and procedures regarding RPA bot’s access rights and credential. Regular audits and performance assessments will also uphold quality and regulatory compliance. RPA involves the handling of large amounts of data, and robust security measures will protect against cyber threats and potential vulnerabilities. Implement encryption, access controls, and secure authentication protocols to ensure the confidentiality and integrity of data throughout the automation process.
Also, review the quality RPA bot’s work with a professional scepticism. The quality of RPA bot’s work will be as good as the quality of data. If the data is not in good quality, the output that RPA bot generates will not be in good quality, either.
Focus on sustainability.
Ongoing maintenance is very important in sustaining the efficiency and reliability of automated processes. Regular updates and adjustments are essential to accommodate changes in business rules, software interfaces, or regulatory requirements. Continuous monitoring helps identify and rectify errors promptly, preventing disruptions in workflow. Addressing evolving security threats ensures data integrity. By proactively managing updates and addressing issues, organizations ensure that RPA remains aligned with business goals, adapts to dynamic environment, and delivers long-term value, contributing to sustained success.
Ongoing maintenance also means capitalizing on emerging technologies such as Artificial Intelligence (AI). While RPA executes workflows based on pre-defined rules, which is suitable for automating repetitive and simple tasks, AI focuses on cognitive tasks that require intelligence, which is suitable for complex decision making and analysis.
For example, many RPA applications now have a document understanding AI model that you can use as part of your workflow.
RPA can bring many benefits to corporate accounting functions. However, if it is not rolled out properly, you may end up creating more business challenges rather than remediating existing ones. In order to deploy RPA successfully and harvest all the benefits, you should clearly define objectives and determine what level of automation you are going to achieve, understand RPA’s limitations and find the right application to handle the tasks, streamline the process as you automate the process using RPA, focus on governance and review the quality of RPA bot’s work with a professional scepticism. And finally, make sure ongoing maintenance and continuous improvement take place after deployment of RPA.
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John Kim is director of the Digital Solutions and Transformation practice at Baker Tilly Singapore. He provides a variety of solutions (e.g., technology and data strategy, data transformation and integration, process improvement and automation, and data analytics services) to corporate finance and tax departments.
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