Intelligent Automation Projects: Roles and Responsibilities

The key to success in any venture is getting the right people aligned and onboard. It's the same with any Intelligent Automation project. The key to a project’s success is the collaboration, communication, and distribution of responsibilities within the team. Leaders need to design the correct teams, select the right players, develop the capabilities and organize a Center of Excellence to successfully implement automation projects in an organization.

When designing your automation team, be sure to have people in these roles:

• Subject Matter Expert (SME)
• Process Owner / Team Lead
• Delivery Manager
• Automation Engineer
• Machine Learning (ML) Engineer
• Data Analyst
• IT Ops
• DevOps Engineer and Site Reliability Engineer (SRE)

Subject Matter Expert

Subject Matter Experts, or SMEs, are the organization’s employees who are experts in the process being automated. They know the business process inside and out, can teach the automation team about each step in the process, and then validate that all relevant information has been captured so that the automation can go to the production phase. In some deployment scenarios, the SMEs are the automation team. Enterprises have them learn the tool and then automate their processes.

In cases where the automated process uses human-in-the-loop feedback, the SME not only watches the automated process perform but also completes the manual tasks that are part of it.

Process Owner

Process Owners are business experts that manage the SMEs. They often support the automation project by helping collect process metrics, using their expertise to ensure that the needs of the business are met.

Delivery Manager

Delivery Managers help coordinate and communicate all things project-related. They are essential to facilitating progress during development and to handling important issues that can arise during the project. 

Delivery Managers are required to have extensive knowledge of all components of the software platform, RPA and machine learning, and relevant use cases, as they work closely with the automation team during the automation development stage. After the business processes are developed and put into production, the management task goes from the Delivery Managers to Project Managers.

Automation Team

An Automation Engineer, Data Analyst and Machine Learning Engineer are the core of the automation team. They are the key players responsible for the automation project implementation and successful delivery, so they need to have excellent knowledge of the tool and some specific skills. 

This automation team can consist of trained employees of the organization or external partners with good tool expertise.

Automation Engineer

The role of an Automation Engineer involves designing and developing software robots on the stack of OCR and Java/Groovy coding within the automation platform. The process of implementing robots presents many technical challenges in the areas of machine learning, large-scale data processing, and complex business rules. Automation Engineers participate in launching the training and tests of IE/Classification models, where they need to work closely with Data Analysts.

Data Analyst

Data Analysts assist with preparation, cleaning, and analysis of ML training data as well as business process data for RPA automation and validation. This facilitates the development of the best practices for maintaining strong and useful data sets for the production.

Data Analysts are responsible for identifying and extracting valuable information from structured and unstructured data to explain business performance. Using this information, they identify the best analytical models to present to business users and the best approaches to explain these models.

Machine Learning Engineer

A Machine Learning (ML) Engineer is responsible for building and integrating generic ML models, for example using AutoML SDK provided in WorkFusion’s Intelligent Automation Cloud. They work closely with Automation Engineers during the development of business processes, and with Data Analysts on retrieving statistics and model results analysis, as well as defining and implementing post-processing logic.

IT Ops

The IT Ops team is responsible for setting up, running and maintaining the servers and machines that the automation project requires, and generally making sure the automated solution has a stable environment. The team also provides integration with the organization’s infrastructure and usually reside on its side, although this role can be done by partner companies.

IT stakeholders must be involved at the earliest stages of the automation program, as they know how Intelligent Automation interfaces with the existing technology and complies with their security standards and protocols.

DevOps and SRE team

DevOps Engineers check the possible bottlenecks of running in production from the very beginning of the delivery process. Focus is on Ops challenges and how they could be resolved during design and development. SRE team's focus is more on maintaining massive workloads in large-scale environments. They deal with monitoring applications or services after deployment. The goal is to make sure you have a process that is reliable, secure and is always operational, no matter what.

Center of Excellence

A Center of Excellence is a department built and trained in the organization for implementing Intelligent Automation. It is responsible for maintaining the solution once it is moved to production. It is a “think tank” team in the company that provides leadership, best practices and guidance in this focus area. Each company that steps into the automation world should also build a CoE with its focus area in machine learning: an ML CoE. The ML CoE’s mission is to provide technical and thought leadership and to ensure that use cases are delivered through standard processes with the help of reusable components. 

CoEs are usually either centralized — which concentrate the capability at one point in the organization — or federated — where a central CoE provides the framework for different business divisions to build their own CoEs, which is helpful for more complex organizations.

Participation in the project

The roles described above participate in the automation project at different stages of its implementation. This chart demonstrates the usual distribution of their work throughout the project duration:

Depending on the automation project scale and needs, many other roles can be also involved in it. You can learn more about these and other aspects of Intelligent Automation in the Automation Essentials course.