In a new as-a-service economy, characterized by continuous disruptions and very low entry-barriers, enterprises are becoming more prone to being part of the crowd. To counter such vulnerabilities, integrating Artificial Intelligence into the IT landscape has been one of the highlights of today’s enterprises. Many enterprises across verticals are willing to accept intelligent automation in their day-to-day operations for cost efficiencies, routine task and process automation and portfolio rationalization. But here it becomes very important to set realistic goals from intelligent automation implementation and cognitive technologies. Hence it becomes imperative to understand the layers of intelligent automation which can be addressed and delivered in the IT landscape of any enterprise.
I like to classify automation into three tiers:
Traditional rule based automation which has its scope in automating particular tasks. Some e.g. includes scripts to migrate applications, services scripts, upgrading enterprise applications etc. Tasks & Stack automation too falls under this tier.
Knowledge based Process Automation: Process can be a combination of multiple chains of tasks bundled together. Many times, SOP (Standard Operating procedure) and many L1/L1.5 tickets automation falls under this tier. With Knowledge based Process automation, such candidates are either eliminated or automated to achieve business outcomes.
Cognitive Automation This tier comprises of AI algorithms like natural language processing, semantic data processing, knowledge management, reasons, and expert systems. The vision here should be in integrating many DIY tasks so that instead of creating tickets for those issues, the end-user can themselves resolve the issue using cognitive capabilities. Another use case can be the use of Machine Learning models to monitor the current IT infrastructure and come up with recommendations (actionable intelligence) to proactively reduce infra related incidents.
Performance and Governance Automation, for e.g. business process monitoring and managed services analytics are required at each of the above automation tier. These are also implemented to monitor the bots deployed. [Read the definition of bots here]
The goal of any automation solution, at any point of time, should be to provide end-to-end automation. This requires the right practice and solutions to be proposed and deployed in the customer’s environment. A consulting approach by the service providers can be the key here.
Defining the Business Value
Intelligent automation should be platform agnostic, readily plug-&-play and easy in deployment. The categorization of intelligent automation into the three tiers allows to provide values to customers in terms of reduced cost of operations, marriage of technological levers with business objectives and mitigation of risks. The above separation of intelligent automation into three tiers also helps in easy customization of each component to suite the business needs. The commitments to leverage intelligent automation to help customers move to an IT landscape which is characterized by loosely-coupled, best of breed components and improved flexibility has led to tremendous business improvement in their IT lifecycle.
Measuring Outcomes
Because of intelligent automation being implemented across various divisions of enterprises, different types of performance metrics have entered the business domain. FTE reduction, Decreasing Mean Time to Resolve (MTTR), reducing number of hops, accurate assignment index of tickets, up-time of infrastructure, availability of apps etc. have gained wide acceptance.
Commuting one lever higher
The role of intelligent automation does not stop at just achieving the mentioned automation metrics, and hence, there has to be a feedback and learning mechanism which can, in the future help the end-user to either self-heal the issue or predict and inform the agents/end-user about the possible outage. The predictive analytics engine of the intelligent automation system should be capable of ensuring this. Intelligent automation has to not only predict but also resolve those new typical L1/L1.5 issues by itself in future from then onwards (a concept of self-learning system).
The Moral and Ethical Issues
FTE reduction has been gaining traction in the minds of the agents for some time now. They feel that intelligent automation will take away their job. I, believe this to be untrue. I believe that a human resource should not be wasted in trivial, mundane and repetitive job, rather, the human mind should be leveraged to produce amazing innovative and creative results. In my opinion, by freeing up these agents & re-training them, their skill set can be improved and they can move up the value-chain, thereby achieving more on their professional front.
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