Although interest in RPA implementation continues to rise among businesses, nearly half of these projects face the risk of failure due to limited visibility into existing processes and insufficient knowledge of RPA vendor solutions.1 Addressing these challenges requires clear frameworks for successful implementation. See essential steps for preparing, implementing, and launching effective RPA projects:
1. Understand existing processes with interviews & processes mining
Understanding existing business processes to implement RPA can be achieved through interviews with operators but relying solely on this approach has drawbacks:
- Costly – Conducting interviews takes time and resources.
- Error-prone – People may have imperfect memories and be subject to cognitive biases.
A more effective approach combines interviews with insights from task and process mining. Process mining software analyzes logs to reveal real-life process flows, while task mining tools can capture video recordings of employee actions (with personal data removed). This approach leverages real-time data to help identify bottlenecks, unnecessary steps, and critical insights. With process mining tools, companies can:
- Prioritize automation opportunities.
- Gain detailed understanding to support RPA implementation process.
- Monitor process changes post-implementation to confirm deployment success.
Key Performance Indicators (KPIs)
To effectively assess processes and identify high-priority areas for RPA implementation, consider tracking these KPIs:
- Process Complexity: Measure the number of steps, decision points, and handoffs within a process.
- Cycle Time: Analyze the average time taken to complete a process or task.
- Error Rates: Track frequency of human errors or rework requirements.
- Bottleneck Frequency: Identify steps with consistent delays or resource constraints.
- Automation Potential: Estimate the percentage of a process or task that could feasibly be automated.
2. Improve and simplify the existing processes
Processes often become complex due to regulatory or market pressures, and while lean or Six Sigma projects can drive improvements, they are not always feasible. Many processes hold untapped potential for simplification and optimization.
Just consider the use of fax machines in the US healthcare system. Numerous media report how the US healthcare system relies on hospitals sharing records with faxes or hand delivered documents because digital healthcare records were not built in a compatible manner across different institutions.2
Prior to implementing RPA, consider streamlining and refining processes, which can:
- Simplify the steps, making automation easier.
- Reduce the programming and auditing effort needed.
- Enhance the overall customer experience.
3. Choose and implement an RPA solution
In this phase, businesses decide whether to adopt a pre-built RPA solution or develop a custom one using tools like Python RPA library. Companies with limited resources or personnel may benefit from partnering with an RPA vendor.
1. Select your RPA solution
Review the latest RPA options, including no-code solutions, to find a tool that aligns with your organization’s needs. Visit our comprehensive list of RPA software vendors to see detailed options.
2. Develop your solution
- Begin with a detailed process map, identifying specific automation points. Collaboration with subject matter experts is crucial, especially if the process is undocumented. After defining the role of RPA bots, they can be programmed. Teams should consider trade-offs like quicker deployment vs. flexibility and follow best practices in lean development and quality assurance.
- RPA marketplaces, such as those by UiPath or Argos Labs, offer reusable bots and plugins that can save development time.
Here’s an example of a process map:
3. Choose your partners
There are numerous RPA partners and consultants that can help roll out an RPA solution. While it may seem like a quick & cheap approach to use just an RPA solution for RPA deployment, case studies indicate that companies save significant time and money by using a best-of-breed approach (i.e. using process mining and machine learning tools in combination with RPA).
4. Facilitate your deployment with other tools
Deploying RPA may present challenges such as selecting the right tasks to automate and monitoring the process. Using process mining and Artificial Intelligence (AI)/Machine Learning (ML) tools can streamline deployment.
1. Pick your task mining/process mining solution
When selecting a process mining tool, consider the following:
- Determine if the tool provides task mining, ML applications or simulation capabilities like DTO, and assess whether these features are necessary for your needs.
- Explore whether certain RPA tools offer process mining capabilities within their automation platforms.
- Evaluate open-source process mining tools, especially if you have a skilled team capable of leveraging such tools effectively.
Learn more on which tool to choose by reading our data-driven benchmarks, such as:
- Analysis of Process Mining Market & Best Software
- Celonis Process Mining: Products, Features & Competitors
- Compare 20+ Process Intelligence Software
2. Choose the AI/ML providers
While RPA is great for automating rules based tasks, it is hard to automate more complex tasks such as getting data from documents with RPA. RPA providers may provide ways to program such functionality but since this is not their focus area, the solutions do not tend to be the best performing ones. It makes sense to research AI/ML providers that solve the specific problems you are trying to solve.
Some common RPA use cases that can be accelerated by AI/ML providers include:
- Invoice automation is easy to automate with almost complete no-touch processing by integrating an API.
- Numerous RPA projects include document data extraction and specialist ML providers provide competitive solutions in that field. Check out our comprehensive, data-driven list of document data capture companies.
You can also read our article about agentic process automation to learn more about AI capabilities of RPA tools.
5. Test your solution & run a pilot
Testing and piloting are essential phases of a successful RPA implementation, ensuring the solution works as intended before scaling up. This stage allows businesses to validate their automation strategies, uncover potential issues, and fine-tune performance.
Best practices for pilots
- Start small with low-risk processes:
Select a process with minimal complexity and risk for the initial pilot. These are typically repetitive tasks with clear rules, such as data entry, invoice processing, or employee onboarding tasks. Starting with a low-risk process minimizes potential disruption while building confidence in RPA capabilities. - Create a testing checklist:
To ensure comprehensive testing, include scenarios that mimic real-world usage:- Exception handling: Test how the bot manages unexpected inputs or errors, such as incomplete data fields or incorrect file formats.
- Integration challenges: Verify that the bot interacts seamlessly with other systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) tools.
- Scalability: Simulate high workloads to assess the bot’s performance under increased demand and identify any latency or resource bottlenecks.
- Incorporate user environment variations:
For attended automation, test across different user environments to account for variables like screen resolutions, system configurations, and software versions. This ensures that minor discrepancies don’t disrupt operations. - Define clear objectives for the pilot:
Set measurable goals for the pilot phase, such as:- Task accuracy rates.
- Percentage of automation achieved.
- Reduction in task completion time.
Metrics and feedback
To evaluate the success of the pilot, track key metrics and gather feedback from stakeholders. Examples of useful metrics include:
- Time saved per task: Measure how much faster the process is completed with automation compared to manual execution.
- Error reduction: Quantify the decrease in errors, particularly for high-stakes tasks like financial reporting or regulatory compliance.
- Automation level: Assess the percentage of the process that has been automated and identify any remaining manual steps.
- Cost savings: Estimate the financial benefits achieved through reduced labor costs or improved efficiency.
- User satisfaction scores: Collect feedback from employees or customers who interact with the automated process. Positive feedback can signal successful integration, while negative feedback highlights areas for improvement.
- Bot utilization rates: Monitor how effectively the bots are being used to ensure that resources are fully leveraged.
6. Go live
The go-live stage requires careful planning and support from existing teams:
- Establish governance and assign roles and responsibilities.
- Create a fallback plan to address any issues post-deployment.
- Communicate new processes to all relevant stakeholders.
- Analyze Results: Use process mining tools to track bot performance and assess whether the project meets its goals.
7. Maintain the robotic process automation installation
As market and regulatory conditions change, processes and RPA bots may need updates. Establishing an RPA Center of Excellence (CoE) or working with service providers can ensure effective RPA maintenance. Process mining tools can help teams monitor changes, triggering necessary modifications to keep the RPA system current.
FAQ
What are the common mistakes and challenges in implementing RPA to the business processes?
The RPA implementation process often faces hurdles that can hinder a successful RPA implementation. Identifying and addressing these challenges is crucial for ensuring that robotic process automation (RPA) delivers its promised benefits of automation, cost savings, and enhanced business performance. Below are some of the most common pitfalls and strategies to avoid them during your automation journey.
Automating the Wrong Processes
One of the most frequent key challenges businesses face is selecting wrong processes for automation. Tasks chosen for automation must align with the strengths of RPA tools—typically rule-based tasks with repetitive workflows that require minimal human intervention. Automating complex processes that involve unstructured data or frequent decision-making can lead to project failure.
Solution: Conduct a thorough feasibility assessment with business analysts to identify specific processes that are high-volume, repetitive, and prone to human error. Use process mining tools to map and prioritize these opportunities.
Underestimating Change Management
A lack of attention to change management often leads to resistance among employees and insufficient adoption of rpa tools. Employees may fear job displacement, misunderstand the role of digital workers, or fail to adapt to new workflows.
Solution: Develop a strong communication plan that positions robotic process automation as a way to augment the workforce rather than replace it. Involve key stakeholders in the implementation plan and provide training on RPA platforms and automation solutions.
Ignoring Data Security Risks
Implementing RPA without considering data security can expose businesses to compliance issues or breaches, especially when automating sensitive business processes like data entry or financial transactions.
Solution: Collaborate with an experienced RPA vendor to ensure the RPA technology complies with data protection regulations. Include encryption, secure APIs, and access controls as part of the RPA development phase.
Overlooking Scalability
Many organizations fail to plan for scaling their RPA workflows beyond initial pilots, which limits long-term value. This challenge often arises from neglecting technical expertise or selecting tools that cannot support large-scale deployments.
Solution: Choose an RPA solution that integrates seamlessly with existing systems and has a proven track record of scalability. Establish an RPA Center of Excellence (CoE) to drive consistent standards and oversee the scaling process.
Neglecting Clear Project Objectives and Success Criteria
Without clearly defined project objectives or measurable success criteria, it becomes difficult to assess the effectiveness of the automation solution.
Solution: Use a process definition document (PDD) to outline project goals, such as reducing manual effort, achieving faster turnaround times, or improving decision-making accuracy. Regularly monitor metrics like bot uptime, error rates, and cycle times to track progress.
By proactively addressing these key challenges, organizations can avoid common pitfalls and successfully implement RPA solutions that improve business functions, reduce errors, and optimize workflows.
For more on RPA implementation
Before RPA implementation and after the solution rolls out there are necessary steps to follow. Read in-depth about:
- Identifying and Prioritizing Processes to Automate with RPA
- 3 Steps to Get Buy-In For RPA
- 5 Ways to Measure RPA Post Solution Success
If you believe your business will benefit from automation solutions, scroll through our data-driven lists of:
External Links
- 1. “Get ready for robots“, EY, Retrieved 15 November, 2024
- 2. Why American medicine still runs on fax machines | Vox. Vox
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