Game changing automation in IT Managed Services for speed and reliability

Automation in IT managed services programs is helping organizations differentiate themselves on two fronts - speed and cost of business. Game changing automation however, is not just about deploying automation software. Rather, it is about bringing together strategy, domain knowledge and technology to harness managed services in a faster, more reliable and scalable way.

While enterprises have enabled accelerated data transfer between processes through RPA, they have not challenged the status-quo on process definition, causing them to miss the digital transformation boat. There is also increased emphasis on technology with little attention to people transformation.

This article discusses how organizations can create an integrated IT automation strategy to leverage the synergies between multiple technologies and improve overall system reliability, reduce cost of operations, and drive customer-centric transformation.

Choosing Candidates for Automation: The Guiding principles

Although automation is a lever of transformation, not every process requires automation. Identifying candidates for automation requires a thorough analysis of the tasks in the context of its likely business benefits. The identification process must take the following factors into consideration to ensure a superior ROI:

Target Audience and value: What benefits will the proposed automation bring about? It could be transformation in user experience or significant shift in end-user efficiency. Identify the operational value such as ‘reduced manual errors’, ‘right resolution to user issues’, ‘reduced effort in solving issues’, etc.
Volume of impact: The proposed change should justify the transformation of user experience and efficiency it is likely to bring about. The number of users who will directly benefit from the automation is important.
Enterprise KPIs: The operational benefits must roll up into enterprise performance measures such as improved SLAs, CSATs, profitability, cost of operations, market penetration and so on.
Risk: Scrutinize the target process, end-user operations and solution in order to drive change management, ensure meaningful target processes and redeployment of legacy workforce.

While this is not an exhaustive list of considerations, it is important for enterprises to define the ‘What’, ‘How’ and ‘When’ of automation projects.

Setting Up an Automation COE and Program Management Office

As a next step, enterprises must entrust a governance body comprising IT and business stakeholders with guiding principles and set up a Program Management Office to oversee the following:

Ideation and planning: Create a team to evaluate and prioritize ideas based on the automation governing principles and build a pipeline of automation initiatives for implementation and rollout.
Change management: Drive ownership and accountability for transitioning users to the transformed state.
Knowledge management: Promote a culture of reusing automation assets and maintaining an asset library of developer guides and user guides on all assets created for automation.
Continuous improvement: Assess the overall business success of automation initiatives and identify areas of improvement in terms of end-user experience and process throughput. Work with project teams to improve SLAs and BLAs through automation.
Experience design: A potentially good automation idea may not deliver the desired user experience, if designed incorrectly. Poorly designed user experiences, in turn, can lead to end-user frustration and dissatisfaction. Set up a COE to focus on standardizing the experience and ensuring the delivery of high-quality digital experiences through automation.

Architecture

Scaling automation to the enterprise level requires the support of an architecture that is modular, extensible and secure enough to support:

Multiple users
Large volume of transactions and a variety of IT systems
Ease of solution building and reuse
An ecosystem of solutions built in-house as well as those available from product vendors and service providers

The solution architecture must therefore be underpinned by a platform that can host automation systems in a secure and scalable framework. The platform must be designed to support a developer workbench that can create automation projects quickly and align them with best practice-based automation solution architecture. To enable these objectives, the platform must be equipped with the following features:

Orchestration engine: Enable a workflow-based system to build automation services quickly from existing solutions and components
Robust and scalable integration: Seamlessly integrate with internal legacy systems, third-party automation products as well as third-party service providers
Authorized access and security: Integrate with enterprise Single Sign-on (SSO) systems, ensure easy configuration of authorized access, and enable encryption and certificate-based access to protect various access points
Resilience to failures: Deploy an architecture that supports high availability and scalability with robust recovery and failover options

Conclusion

IT Service Automation underpinned by state-of-the-art technologies, scalable architecture and robust governance practices is key to ensuring the desired business benefits of automation. Investing in teams comprising IT and Business stakeholders can help enterprises define detailed automation principles while setting up an automation platform. The platform will then serve as the glue to bring various solutions into a seamless ecosystem and ensure synergy. In the next blog, we will discuss the right mix of techniques to give automation the edge it needs by focusing on AI and governance.

About the author

John D’Souza

Enterprise Architect, Wipro Limited

John is an Enterprise Architect leading the Wipro HOLMES^TM Enablement team and specializes in IT automation and AI. He is an Automation Advisor to large programs embarking on the automation journey and an expert in multiple domains of IT architecture. His wide-ranging experience spans information architecture, integration, application architecture, and traditional machine learning and deep learning applicable to business data, natural language and image data.

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