Robotic Process Automation (RPA) is a software technology that automates repetitive tasks with greater efficiency. It helps organizations in obtaining a competitive advantage by enabling them to optimize workflows and accelerate digital innovation. But despite the fact that most businesses recognize the critical relevance of RPA, why is it taking them so long to move forward?
Even though there is hype around RPA and its effectiveness in transforming the digital world, many RPA projects fail due to their complexity and incorrect implementation. In this article, we will focus on a few major RPA challenges and ways to overcome them.
Challenges in implementing RPA
- Selection of the correct processes to automate: Organizations, in their haste, sometimes overlook the necessity to choose which operations will benefit the most from automation. Identifying the right processes for automation often becomes one of the major challenges in RPA implementation. Processes that entail a significant amount of structured, repetitive manual work, like paper-based data entry, serial calculation, etc., are more prone to errors. Hence, automating these tasks would be highly beneficial to an organization. On the other hand, if a company makes an error in judgment and decides to automate the incorrect tasks, it will rather maximize inefficiency and lower ROI.
- Lack of infrastructure: Without proper infrastructure, it becomes impossible to get the desired outcomes. Organizations using legacy approaches, using slow and decentralized systems could suffer greatly from RPA failure. If there is no interconnected system within an organization that executes real-time changes on cloud infrastructure or on-premises, it becomes very difficult to accelerate RPA implementation. For a successful RPA implementation, organizations must have powerful, 24/7 available infrastructure to run all scripts without the risk of being damaged by external factors.
- Security concerns: Bots have access to vital company systems like CRM and ERP. Organizations must ensure that the RPA solution that they deploy is secure, especially if it accesses sensitive information. This can be done using various techniques like assigning unique identification credentials to each robot, limiting RPA access to relevant systems for a particular task, building a risk management framework to oversee RPA implementation, etc.
- Lack of skills: RPA deployment within any industry is a critical task that requires skills and excellent team leadership. The implementation of RPA will encounter difficulties if there is a lack of qualified personnel in an organization. However, there is a shortage of skilled resources in the current market. The biggest risk to the effective completion of any project is gathering resources at the start of a new project and replacing them when a significant resource runs out. Hence, it is critical to build a team of skilled employees within an organization to help generate a system that supports RPA. Employees can be trained to understand the complications of RPA so that every task can be completed in an accurate and efficient manner.
- Automation of end-to-end processes: Automating every step of an entire process directly by using rule-based RPA tools is not always cost-efficient. Every firm should have a team that oversees all processes and keeps an eye out for these kinds of RPA difficulties in order to successfully implement the RPA solution.
Overcoming RPA challenges with intelligent automation
In recent years, the market for RPA is party declining due to its implementation challenges. And intelligent automation, which is a combination of RPA tools and artificial intelligence (AI) technologies, is emerging as a solution that can be deployed to overcome these challenges. It is becoming one of the primary technological drivers for achieving digital transformation and automating end-to-end business processes.
Intelligent automation offers far more robust and scalable outcome-driven frameworks when re-engineering business processes since it uses a much wider toolbox of technological capabilities like AI, ML, data analytics, API etc., as well as production or delivery methodologies like low code development, DevOps, Agile etc. to provide improved efficiency. Furthermore, intelligent automation reduces operational obstacles by navigating legacy systems and other functional barriers and helps in overcoming RPA challenges by adopting a culture of innovation and fail fast approach, thereby providing long-term benefits. Additionally, it improves cybersecurity by putting in place checks and balances to examine networks and data structures, evaluate security threats, and take care of any possible issues before they can be exploited.
The RPA tools that are currently available are undoubtedly quite sophisticated and packed with the newest features, but some of them still have limited machine learning capabilities. With the integration of AI and ML into RPA tools, the potential for automation will grow enormously.
To sum up, it is possible to overcome the challenges of RPA implementation by integrating RPA tools with AI technologies. In doing so organizations will be able to automate business processes end-to-end, strengthen cybersecurity concerns, and free up employees to work on critical tasks that require human skills.