Understanding the maturity model of automation with intelligent bots. Click here for the full story.
With growing interest & investments in new concepts like Automation and Artificial Intelligence, the common dilemma for enterprises is how to scale these for significant impacts to their relevant context. It is easy to do a small proof of concept but much harder to make broader impacts across the landscape of Hybrid Infrastructure, Applications and Service Delivery models. Even more complex is Organizational Change Management for underlying processes, culture and “Way of Working”. There is no “Silver bullet” or “cookie-cutter” approach that can give radical changes but it requires an investment in a roadmap of changes across People, Process and Technology.
Relevance Lab has been working closely with leading enterprises from different verticals of Digital Learning, Health Sciences & Financial Asset Management on creating a common “Open Platform” that helps bring Automation-First approach and a maturity model to incrementally make Automation more “Intelligent”.
Relevance Lab offers RLCatalyst – An AIOps platform driven by Intelligent Automation paves way for a faster and seamless Digital Transformation Journey. RLCatalyst Product is focused on driving “Intelligent” AUTOMATION.
AUTOMATION is the core functionality including:
- DevOps Automation targeting Developer & Operations use cases
- TechOps Automation targeting IT Support & Operations use cases
- ServiceOps Automation targeting ServiceDesk & Operations use cases
- SecOps Automation targeting Security, Compliance & Operations use cases
- BusinessOps Automation targeting RPA, Applications/Data & Operations use cases)
Driving Automation to be more effective and efficient with “Intelligence” is the key goal and driven by a maturity model.
“Intelligence” based Maturity model for Automation
Level-1: Automation of tasks normally assisting users
Level-2: Integrated Automation focused on Process & Workflows replacing humans
Level-3: Automation leveraging existing Data & Context to drive decisions in more complex processes leveraging Analytics
Level-4: Autonomous & Cognitive techniques using Artificial Intelligence for Automation
RLCatalyst Building Blocks for AIOps
AIOps Platforms need to have common building blocks for “OBSERVE – ENGAGE – ACT” functionality. As enterprises expand their Automation coverage across DevOps, TechOps, ServiceOps, SecurityOps, BusinessOps there is need for all three stages to Observe (with Sensors), Engage (Workflows), Act (Automation & Remediation).
RLCatalyst provides solutions for enterprises to create their version of an Open Architecture based AIOps Platform that can integrate with their existing landscape and provide a roadmap for maturity.
- RLCatalyst Command Centre “Integrates” with different monitoring solutions to create an Observe capability
- RLCatalyst ServiceOne “Integrates” with ITSM solutions (ServiceNow and Freshdesk) for the Engage functionality
- RLCatalyst BOTS Engine “Provides” a mature solution to “Design, Run, Orchestrate & Insights” for Act functionality
For more information feel free to contact firstname.lastname@example.org