RPA deployment will fail without a strong operating model

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Best practices in the successful deployment of robotization programs

Following initial experimentation, organizations that wish to scale with RPA technology need to define and set up a robust and industrial operating model for the end-to-end journey from identification of processes and delivery of robots, to the management of a live bot portfolio to ensure both stability and sustainable benefits.

The fundamentals of the RPA operating model

In simple terms, the RPA operating model is an organization’s “blueprint” which often revolves around setting up a center of excellence, and which consists of five dimensions:

While there are many challenges that need to be defined and addressed when implementing an effective operating model, the main questions are as follows:

  • Will the robots be developed and managed by a central team or a decentralized organization?
  • What are the main roles and responsibilities in an RPA organization (business analyst, developer, robot controller, etc.)?
  • Will RPA teams for development and maintenance be positioned as close as possible to the business or to the IT department?
  • What are the different phases of an RPA project and the steps of go/no go?
  • How is the demand management process organized to prioritize the portfolio of opportunities and define the roadmap?
  • How best to manage communication and change management when putting robots into production?

An uncontrolled RPA operating model inevitably leads to inefficient and unstable robots

Companies that haven’t defined their operating model quickly face challenges in the management and maintenance of robots in production. Although the RPA development phase often works well, organizations sometimes realize that RPA is not necessarily the best solution to apply or that a particular process should have been optimized before being robotized.

The operating model needs to establish clear eligibility criteria for RPA, implement qualification processes to identify potential prerequisites, and validate the relevance of using RPA from an architectural point of view to avoid such situations.

Maintenance of robots can also become a challenge when the code of each robot is not based on shared development standards, but depends on the habits of each developer.

The key to lasting success

Robot management is like running a shared service center. The definition of roles and methodologies across the RPA lifecycle is essential to steer activities and coordinate stakeholders from business to transversal functions – such as risk, compliance, and security – and to IT teams. An adapted operating model also guarantees the stability of the robots by anticipating maintenance needs related to, in particular, the evolution of an organization’s legacy systems.

RPA operations should also establish indicators to measure the benefits, and the actual costs of robots. These parameters must be appreciated from the upstream phases of opportunity qualification and shared with the business lines. Indicators must also be set up for the live phase of robots to enable their monitoring and supervision, including tracking of volumes handled by robots vs. exceptions managed by humans, and processing times, etc.

Where to start?

The operating model design is strongly linked to an organization’s ambition in terms of process automation. The complexity of the organization – number of entities, homogeneity of their business model, geographical coverage – and the degree of centralization in the IT organization, are also key factors to take into account.

The framework presenting the five dimensions of an operating model and the questions above are a good starting point to define the foundations of your RPA model.

To learn more about how Capgemini’s RPA and AI solutions can deliver enhanced value for your organization, contact: fabrice.perrier@capgemini.com

Fabrice Perrier focuses on the impact of intelligent automation in banking and insurance. He supports clients in positioning and deploying such transformations, leveraging the potential of robotics and AI as well as more traditional levers, and ensures conditions for sustainable results by engaging business and IT in new and industrialized operating models. 

Thank you to Adrien Vignes, Senior Manager, Capgemini Invent, for his input to this article.

 

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