A definitive timeline for RPA implementation

RPA Implementation: Key Stages and Definitive Timelines

Robotic process automation (RPA) was first adopted back in the 1990s for the purpose of user interface (UI) testing and came to prominence by 2012, very quickly becoming a pivotal change driver for large-scale businesses that adopted this technology.

Today, RPA is well beyond its pilot stage and is increasingly being adopted and leveraged for process optimization, efficiencies, and transformation. While many may consider RPA a buzzword, most organizations recognize RPA as a key lever for their digital transformation journey.

Though RPA and its application as a transformational tool is discussed as a popular topic, the implementation cycle of RPA is not talked about enough.

The time that goes into implementing this technology is rarely discussed and, therefore, having a definite understanding of implementation methodologies, stages, and timeline is a crucial aspect of the successful execution and delivery of RPA projects.

Before we can have a definitive timeline, it’s important to understand the stages that are involved in RPA implementation.

Stages of RPA implementation

Implementing RPA roughly involves the following six stages:

  1. Identify – Identify the opportunities where automation is possible and access is feasible. Assess the process, criticality, business impact, validate the assessment, etc.
  2. Design – This stage involves building a detailed process design for RPA developers to commence development.
  3. Development – The RPA developers build the bot according to the approved requirements and blueprint provided during the design phase.
  4. Testing – The automated process is tested in a testing environment by the RPA developers and the QA team. User acceptance testing (UAT) is part of this stage.
  5. Deployment – The RPA bot is deployed into a production environment as a pilot and, subsequently, the full-fledged deployment is carried out, and the user can run it as per their requirement.
  6. Monitoring and change management – This key stage helps monitor the bot performance continuously and ensures error-free operation. Proactive change management can anticipate and prevent downtime.

RPA implementation timeline

The complexity of processes that have been ear marked for automation needs to be analyzed and categorized as soon as possible. Doing so will help businesses get an accurate timeline for the project and avoid unnecessary deferment of their deployment schedule at a later stage saving both time and money.

The process complexities will affect the timeline for RPA implementation. Complexity is assigned to a process depending on the scope, the goals, business rules, number of applications/tools the bots have to access for the process, criticality of the process, etc.

The complexity of RPA projects can be classified into:

  • Low-complexity RPA projects
  • Medium-complexity RPA projects
  • High-complexity RPA projects

Low-complexity RPA projects

Low complexity RPA projects typically involve the following processes:

  1. Requirement and analysis
  2. Design
  3. Build and test
  4. User acceptance testing
  5. Deployment

Processes categorized as low complexity typically don’t take long to implement, lasting between 3 and 4 weeks depending on various factors.

Low complexity processes are processes that are straightforward processes having few business rules, lesser number of applications/systems, minimum to no decisioning.

Medium-complexity RPA projects

In processes categorized as medium complexity, the design phase alone can span two to three weeks, since a well thought out and detailed design is the major component of a successful RPA implementation.

The overall implementation of medium-complexity projects is likely to take up to 6 or 7 weeks.

While the stages remain the same as the low-complexity projects, there are more business rules, decision points, applications, and fail-safe methods included, thus requiring additional time to implement as compared to low-complex processes.

High-complexity RPA projects

Complex processes of this magnitude require much more of a lift than the lower and medium projects, which can ultimately take even longer than what is initially expected or projected.

For highly complex processes, timelines of 10 to 12 weeks are not uncommon and can even take longer.

Any RPA implementation timeline that goes beyond ten weeks will inevitably be complex. It will have several applications, numerous business rules, process specialists’ interventions, critical tasks, compliance and regulation impacts, as well as impact on customer experiences. All this would mean that the impact of a small change in the processes of applications could impact the above-mentioned metrics.

Processes categorized as high complexity often have other tools/technologies that the bots rely on for inputs, such as cognitive image extraction tools, etc. Therefore, an understanding of the complexity of the project is critical during the decision-making stage.

Common setbacks in RPA implementation

More often than not, businesses find themselves struggling with RPA implementation. This is mainly because the specific processes that require automation are not accurately assessed, and businesses are either under-equipped to handle them or aren’t approved for RPA design and integration.

A stringent step-by-step roadmap is crucial for efficient and successful RPA implementation. It addresses the concerns of all stakeholders without compromising anyone’s interests.

Decisions made upfront can snowball throughout an RPA implementation project and become expensive to do over, causing delays and cost overruns.

Without the right team and right skill sets and experience, it will be hard to see early success of an RPA implementation. But, with the right roadmap, requirements, production goals, team and experience involved, any organization can see success early on.

Prakash Hariharasubramanian

Prakash Hariharasubramanian, Director & Practice Lead, Intelligent Process Automation (IPA), HGS
Prakash has led various IPA implementations across multiple industry verticals in his tenure of 7 years with HGS. In his role, Prakash develops IPA practice frameworks, creates IPA solutions, and serves as a key automation evangelist for HGS.

Recent blog posts:

Boosting Efficiency with Human Supervision and Automation
Embracing a Human-First Strategy in the Age of Gen AI Platforms
Moving from Analog to a Digital CX Operating Model
Understanding the Customer Hierarchy of Needs