The COVID-19 pandemic was an eye-opener for businesses that realized the importance of automation to complete their digital transformation journey. And, rightfully so, there are processes that are ideal candidates for automation, ensuring efficiency and higher value delivered to clients and end-users.
According to statistics, the Robotic Process Automation (RPA) market stands at US$ 3.17 billion. As more and more enterprises are shifting toward AI technology, they realize the need for Process Discovery to deliver on the promise of RPA.
What is Process Discovery in RPA?
Process Discovery refers to how organizations discover every business process and its execution. To enable an enterprise-wide automation implementation with RPA, Process Discovery plays the lead role in handpicking business use cases most suited for immediate improvement or automation.
However, legacy approaches to process discovery involve too many human touchpoints, translating into a loss of productive time and resources.
The Traditional Approach to Process Discovery in RPA
The traditional approach to Process Discovery can be split into three steps, namely:
1. Documentation
This is the first step to Process Discovery and involves documenting every action a user takes while performing a specific task. From clicking a mouse to interacting with various systems or completing a process like onboarding new employees, all this data is very important and needs complete documentation.
2. Analytics
This is probably the crucial step involving analytics to prioritize the different business processes and tasks that are most suitable for automation. The results are in order of importance and are visible to users for tallying the effect of automation on a particular process executed.
3. Automation
The final step is automating the shortlisted processes. This is where the true business value in Process Discovery is fully realized. Process Discovery in RPA creates meaningful action from the results of discovering the tasks and processes.
Challenges in Manual Process Discovery
To ensure the business process outcomes of an organization are meeting the enterprise objectives, it is important to know the processes from the inside out. The data captured from process models using event logs recording real-time activities paint a clear picture. This data, when optimized, can serve as a solid base for initiating Robotic Process Automation.
Sadly, the old approach to process discovery follows manual processing and capturing of data, leaving enough room for human errors. Hence, manual process discovery is rift with challenges, creating bottlenecks to impede a smooth transition of processes to automation.
Following are a handful of challenges common to every organization following the manual approach to process discovery:
Lack of Visibility into the Tasks
It is impossible to take a microscopic view of every minute detail of every task performed in a singular business process. Managers are tasked to ensure the smooth running of their allocated departments. They will tally the overall outcome, not scan and verify the minute details. Hence, granular nuances existing in the Process stay ignored or unseen.
Lack of Visibility beyond the Tasks
In order to draw a comprehensive picture of every business process, it is important to see the full process tapestry. But, rarely do the employees get the time to focus on other areas falling beyond their job roles. Instead, they cater to what has been assigned; hence, the bigger picture remains invisible.
A Single Task; Different Techniques
Everybody in an organization has their style of working. Hence, one task can be completed in dozen other ways. Process Discovery aims to streamline different techniques and find a unified way of doing things. But, compiling such granular details from multiple sources is challenging when the entire process is catered to manually.
When implementing Robotic Process Automation in certain business use cases, historical data is needed to feed into the system so that the processes can be fully automated. However, the smooth transition to automation will never be fully realized in the absence of accurate data.
How to Ensure RPA Success with Process Discovery
As long as humans are involved, the Robotic Process Automation success of shortlisted business processes can never be fully realized. However, businesses can expedite Robotic Process Automation by automating Process Discovery using AI technology.
With the help of AI technology, the Automated Process Discovery tools can easily capture empirical data and transform them into a structured dataset to be analyzed by either experts or AI. On the other hand, the repetitive tasks are grouped into meaningful events, and a process model is proposed for analysis.
The Process Discovery bots silently monitor as employees work in their systems to capture how each business process function for a specific period. They run on employee machines in a non-intrusive way, without hindering the daily workflow, and collect data in real-time. This paints a clear picture of how the employees use various applications to perform their tasks.
With the help of advanced ML algorithms, the data is analyzed to understand whether the business use case can be moved to an automation tool for a seamless Automation journey.
This is how AI-enabled Process Discovery ensures the success of Robotic Process Automation in specific business processes.
Benefits of AI-Enabled Process Discovery
Understanding where they should start the automation journey can be another major challenge for businesses, especially if they lack the knowledge or understanding of how RPA works. Process Discovery can provide organizations with a blueprint starting with the repetitive or basic tasks and moving toward more complex processes. Then, without human intervention, the entire system can be made fast, reliable, and cost-effective.
Here are a few benefits of Process Discovery: –
1. Increased Productivity
Process Discovery can improve existing automation efforts or bring more business processes under RPA. Automating time-consuming yet crucial functions in each business process can enhance the overall productivity of the employees. The latter can focus their attention on more value-added services while automation takes care of the less important job roles.
2. Removing Bottlenecks
One of the major responsibilities of Process Discovery is identifying the bottlenecks existing in the business processes before automation. Such bottlenecks can impede smooth workflow and cause unnecessary delays. Process Discovery uncovers every single task that can be automated and made faster without human intervention so organizations can fulfill their objectives easily.
3. Better Transparency
Process Discovery can easily make even the most complicated business processes transparent using computer vision. This awards the owners with complete visibility of how processes work so that they can improve and make those processes consistent with RPA.
4. Improved Scalability
Scaling an RPA implementation success directly depends upon data analysis which is a time-intensive task if done manually. But with the insights generated through automated Process Discovery, organizations can make intelligent decisions on which processes to automate next. As a result, the whole Process performs seamlessly with automation using minimal resources and time.
5. Risk Mitigation and Compliance
Process Discovery in RPA lets organizations identify exactly which actions are performed in a process and how it is performed. This is critical to confirm whether the compliance standards are met or not. However, such little mistakes can happen if humans are kept in the loop. Hence, automating these steps ensures the regulatory compliance is observed. This eventually saves owners from costly penalties for violating compliance rules.
6. Maximized ROI
When AI technology is used for Process Discovery, it is possible to capture empirical data needed for better business process maps. For example, user keystrokes cover all process exceptions and variations and provide visibility and options to scale; hence, capturing granular data helps enterprises easily identify minor nuances in the existing processes and automate those processes accordingly. This ensures maximized expected ROI from the Automation program.
7. Competitive Advantage
Since 80% of companies are still using manual process mapping, as per reports, switching to Automated Process Discovery will give your venture a competitive edge over other players in the market.
Conclusion
The enterprise-wide Robotic Process Automation objective will never be realized until the companies successfully implement automation in some business processes and lay the foundation for future process transitions. And the whole journey depends directly on comprehensive data, which, when handled manually, will paint half the picture of reality. Process Discovery in RPA captures granular data, usually overlooked by humans, and presents an accurate analysis of how each Process works minus human bias. This real-time, accurate data and analytics eventually help enterprises start their automation journey at scale and generate good ROI on RPA.
