Choosing the Right Processes for Automation

In Robotic Process Automation (RPA), success starts long before the first bot is built. One of the most common reasons automation initiatives fail is poor process selection. Choosing the wrong process to automate wastes time, money, and internal goodwill—often stalling adoption entirely.

This whitepaper presents a proven framework for identifying high-impact automation opportunities, prioritizing them strategically, and avoiding costly missteps. With structured evaluation criteria, advanced tools like process mining, and a data-driven ROI approach, organizations can create an automation pipeline that consistently delivers measurable business value.

Many organizations begin their RPA journey with “quick wins” — simple, repetitive tasks — but not all low-hanging fruit delivers meaningful results. Strategic process selection ensures long-term scalability, alignment with business goals, and clear ROI.
Selecting the right processes is not just a tactical step; it's a strategic imperative that:

• Reduces the risk of automation project failure. Proper evaluation identifies and mitigates potential roadblocks before a project even begins.
• Accelerates the path to positive ROI. By targeting processes with a clear and measurable impact, businesses see the financial benefits of their investment sooner.
• Builds a sustainable foundation for enterprise-wide automation. A series of successful projects creates momentum, builds internal expertise, and fosters a culture of automation.

A successful process for automation is not just repetitive; it meets a specific set of criteria that make it a perfect candidate for RPA and AI. These criteria can be broken down into five core areas:

• 1. Repetitiveness & Rule-Based Nature: The ideal candidate process is structured, stable, and well-documented. It must be based on a clear set of logical rules with minimal exceptions. If a process requires significant human judgment, negotiation, or creativity, it is less suitable for simple RPA and may require a more advanced intelligent automation solution with AI. Examples: Data entry, invoice processing, and compliance checks.
• 2. High Volume & Frequency: Processes that are repeated daily or weekly—and involve a high volume of transactions—amplify the benefits of automation. Automating a task that runs a few times a year will provide a limited return, whereas automating a process that runs thousands of times a month will provide significant time and cost savings. High-volume processes offer a faster and more compelling ROI.
• 3. Error-Prone Manual Work: Tasks with a history of frequent human errors are excellent candidates for automation. A bot performs these tasks with consistent execution and near-perfect accuracy, eliminating the need for costly rework, preventing compliance issues, and improving customer satisfaction.
• 4. Clear Business Impact: The chosen process should be directly linked to a tangible business outcome. The best candidates for automation are workflows that directly influence revenue, cost savings, compliance, or customer experience. Prioritizing these processes ensures that the automation delivers measurable value that is visible to the entire organization.
• 5. Technical Feasibility: A process must be technically viable for automation. The best candidates are digital, relying on applications that can be interacted with by a bot. Frequent changes to the applications or the process itself can make automation difficult to maintain and should be avoided for initial projects.

To move beyond gut feelings and make data-driven decisions, organizations should use a combination of tools and frameworks.

• a) ROI Matrices: This simple but powerful tool helps prioritize processes based on a cost-benefit analysis. A typical matrix plots the effort to automate (complexity, cost) against the potential business impact (cost savings, efficiency gains). This visual framework quickly highlights the "quick wins" (low effort, high impact) that are perfect for a first pilot project and the more complex, long-term strategic initiatives.
• b) Automation Readiness Scorecards: A scorecard provides a structured way to evaluate each process against the key criteria outlined in this paper. The scorecard can assign a score to each criterion (e.g., rule clarity, stability, exception rates) to generate a final "automation readiness score." This ensures a consistent and objective evaluation across all potential projects.
• c) Process Mining: This is a crucial, data-driven tool for modern automation. Process mining software analyzes event logs from your existing systems (e.g., ERP, CRM) to visualize how processes are actually performed. It uncovers undocumented variations, identifies bottlenecks, and quantifies the exact time, cost, and frequency of a process. This allows businesses to identify automation opportunities with precision and build a strong business case with verified data.

Even with the right framework, there are common pitfalls that can derail an automation program. Avoiding these mistakes is as important as identifying the right opportunities.

• Automating a Broken Process Without Optimization: Automation amplifies existing flaws. Trying to automate a fragmented, inefficient process without first optimizing it will result in "robotizing a mess." The motto should be "optimize, then automate."
• Starting with Overly Complex Workflows: While complex workflows may have a high ROI, they also carry a high risk of failure for a team new to automation. Complexity in early projects can cause delays and reduce confidence in automation. Starting with a manageable, high-impact process builds confidence, proves value, and develops internal expertise before tackling more intricate projects.
• Excluding Operational Stakeholders: Automation is a change management initiative as much as it is a technology one. Failing to involve operational teams, process owners, and subject matter experts from the beginning can lead to resistance, missed requirements, and low adoption rates. The frontline teams who perform the work every day have critical insight into process exceptions and bottlenecks, and their input is invaluable.

A structured roadmap ensures a repeatable, scalable process for identifying and selecting automation opportunities.

1. Assess Current Workflows: Use process mining or manual interviews to gain a deep understanding of your existing processes. Document and map them to understand all dependencies and variations.
2. Engage Cross-Functional Teams: Form a Center of Excellence (CoE) with stakeholders from business operations, IT, compliance, and leadership to collaborate on the initiative.
3. Evaluate Against Criteria: Use an ROI matrix and an automation readiness scorecard to filter and score potential processes based on rule clarity, stability, and potential ROI.
4. Pilot, Measure, Scale: Select a high-scoring process for a pilot project. Closely track its performance, measure the ROI against predefined KPIs, and, once successful, use the learnings to scale the solution across the business.

The right process selection is the foundation of successful RPA adoption. By moving beyond a reactive, ad-hoc approach and adopting a structured, data-driven framework, businesses can ensure that each automation initiative delivers measurable value, fosters employee support, and builds a sustainable, intelligent automation program. Companies that follow this approach won’t just deploy bots—they’ll build a scalable, future-ready automation ecosystem.

Whaletify helps organizations unlock the full potential of RPA and AI-powered automation. With over a decade of expertise and a proven track record of delivering thousands of automated processes across more than 57 countries, we specialize in identifying, designing, and implementing high-value automations that drive real business outcomes.