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Robotic process automation (RPA) is a cornerstone of the growing trend towards hyperautomation. According to Gartner, the hyperautomation market will reach $596.6 billion in 2022, a $1 billion increase over 2020. Hyperautomation enables organizations to leverage process agnostic software such as RPA, artificial intelligence (AI), and business process management (BPM) across an enterprise.
Gartner predicts hyperautomation will lead organizations to deploy process-agonistic software between 2022 and 2025. Companies will look to integrate technologies into a more connected solution that focuses on visibility. Businesses need to map business processes, see operations across multiple systems, and increase the capabilities of rule engines to handle complex rules. With redesigned processes, enterprises can lower operations costs by 30% by 2024.
To realize cost savings, organizations must overcome RPA challenges that lead to nearly half of the projects failing. Whether it is a lack of preparation or a failure to update the solution, businesses must prepare to address the RPA challenges before they begin an RPA initiative. The following discussion outlines eight best practices that companies need to consider before implementing an RPA solution.
Have a Clear RPA Strategy
To avoid RPA risks and challenges, organizations need a clearly defined strategy that answers the following questions:
- What are the RPA objectives?
- How will RPA be implemented?
- How will results be measured?
- Will the process need to scale?
- Will the infrastructure support scaling?
Without a clear strategy, companies cannot assess a project’s success, nor can they communicate a clear objective that can be used across an enterprise.
Align Business and IT Strategies
For years, IT charted a path to improve its technology stack without knowing an overall business strategy. That disconnect led to misaligned resources resulting in long lead times for technology initiatives and increasing frustrations throughout an enterprise. Deloitte considers this misalignment to be a significant RPA challenge.
Aligning strategies requires more than meetings and memos. It requires a culture change. Companies have to remove silo-thinking to establish equity among all stakeholders. Instead of viewing IT as supporting business strategies, organizations need to consider technology as a transformational element of their business strategy.
To achieve alignment, start with a plan. Organization change management offers ways to restructure IT to better align with business strategies. Communication frameworks such as Zachman can help break down complex processes for better communications. These tools can set the stage for creating a clear RPA strategy essential for project success.
Successful RPA projects specify responsibilities. A well-integrated solution crosses departmental lines and requires support from employees throughout the enterprise. Teams need the authority to approve designs, monitor execution, evaluate success, and initiate change. Poorly-defined roles can result in duplicate efforts and project delays.
Few projects go as planned. Without clearly defined responsibilities, participants do not know how to navigate changes in project plans or how to resolve conflicting requirements. The lack of clarity can result in project failure as the outcome fails to meet expectations.
Select Appropriate Business Cases
Choosing the proper business case for RPA implementation usually revolves around:
- Automating repetitive tasks to free expensive labor for higher-value tasks
- Automating manual tasks to reduce the need for added labor
Hyperautomation technologies such as RPA and BPM allow companies to create value by moving beyond simple automation. Hyperautomation can automate more complex tasks to improve end-to-end business operations. As they look for use cases, they want value across an enterprise, not just within a department or functional area. For example, businesses may look at how an RPA solution ingests data from an online form and provides it to multiple back-end solutions rather than a single application.
Choose a Suitable Process
Not every process should be automated because some may not provide the ROI. When looking at possible automation processes, consider the following:
- Look for processes that use standard rules and repetitive manual data entry. Automating these processes will have a significant impact on operations.
- Avoid processes that require frequent changes. Stable operations reduce ongoing challenges that frequent changes present for automation.
- Find processes that are standardized across an organization. Procedures that vary from department to department increase an automation project’s development and maintenance costs.
- Select processes that are used frequently. Using processes that execute daily will have a more significant impact than those that occur monthly or annually.
- Identify complex processes. For initial RPA deployments, look for straightforward procedures to shorten delivery times and minimize error potential.
- Avoid mission-critical procedures. Once organizations are comfortable with RPA processes, they can automate critical processes requiring human verification using artificial intelligence (AI).
Picking high-impact, low-risk processes increases the odds of a successful deployment. As RPA becomes a part of a company’s culture, complexity can be added with lower risks. However, calculating the ROI on any project means knowing the RPA risks and challenges of any business case.
Optimize Before Automating
Before automating a process, businesses should review the existing procedure to ensure only necessary steps are included. For example, sales administration prints monthly regional sales forecasts to place in a reference binder before creating a consolidated report for management. The process could be automated since the forecasts are presented in a pre-defined format.
However, the process should change to have the forecasts saved to a network location that the RPA solution could pick up, ingest the necessary fields, and produce a report in the standard format. Instead of having a hard copy filed in a binder, the original input could be archived as part of the process.
Sales administration doesn’t have to print and file reports or email completed reports to the various sales manager. Adding a distribution list to the RPA process ensures that appropriate people are informed without sales administration. By optimizing the process before automating, generating monthly sales forecasts could eliminate human intervention once the report is filed.
Ensure a Suitable Infrastructure
Hyperautomation technologies require a robust infrastructure. AI uses massive amounts of data to make appropriate decisions. RPA and AI need processing capabilities that allow rapid response to rule-based engines. As automation becomes more intelligent, RPA will grow in complexity requiring even more network resources to deliver optimum performance.
Not only does automation require robust network resources, but it also needs to operate 24/7. Companies should plan for a backup or failover solution to ensure continuous operations. Cloud-based solutions can help ensure a 24/7 operation.
Plan to Scale
Cloud implementations can also help with scaling challenges that most RPA solutions face. Cloud providers can reconfigure resources in real-time to address changes in demand to ensure performance requirements are met. Deloitte reports that only 3% of surveyed organizations could scale their RPA solutions.
Organizations can look at making processes operate independently or in parallel. They can increase the number of bot instances rather than add functionality to a single bot, increasing its size and potentially reducing its performance. By monitoring RPA solutions after deployment, architectural changes can be made to simplify the process, making it easier to scale.
Nividous’ platform delivers robotic process automation, artificial intelligence, and business process management developed natively for the platform. All features are accessed through a single platform that can move an organization from automation to hyper-automation. Start with a rule-based RPA process, add AI-enabled intelligence, and incorporate a BPM component to deliver end-to-end automation.