Intelligent Automation in As-A-Service Economy

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.

Making Intelligent Automation work

In today’s world, data is flowing like never before. They originate from myriad of sources, intelligent devices, millions of connected devices to name a few. Hence, it becomes imperative that these data are properly processed and the information derived is progressively and incrementally analyzed.

Intelligent automation is the integration of automation and artificial intelligence. It has become top-of-the mind issues for CXOs around the world. Intelligent automation, going forward, is going to define who becomes the winner and who loses out in the long run. It has already helped enterprises to go beyond the conventional methods of doing things and achieve unprecedented levels of quality and efficiency. The ability to command savings on incremental dollars spend on ITSM issues like service desk operations w.r.t to the current systems has made business leaders across verticals to seriously ponder on integrating intelligent automation into their IT estate. The disruptions in this field is revolutionary, and happens at each level of tasks or process performed by the enterprises.

How can Intelligent Automation Help?

The class of business & IT problems which can be solved using intelligent automation is growing rapidly. With technologies like Natural Language Processing, Pattern recognition, Voice recognition and Machine Learning, the value to the enterprises can be delivered is immense. I have shared my views in my previous post.

What Makes Intelligent Automation Work? What are the Challenges?

Data. The only input to an intelligent automation system is data. Intelligent automation platforms consume data, run algorithms and then come up with some data models, meaningful information and predictions which can be then used to achieve efficiency improvements. The nature of intelligent automation is such that it starts to learn with every new use case which comes it way, thereby, reducing human intervention to a minimum.

There are majorly 3 challenges in making the intelligent automation work

Data is getting generated from multiple sources: images, videos, flat files etc. Then there is data segmentation w.r.t geography, category, time, transactions, performance etc. Also a lot of value can be generated by studying the sentiments of the agents and the end-users of the systems (very potential source of data). Hence, with so disparity in the source of data, it becomes difficult to sanitize the data so that it can be normalized and fed to the intelligent automation system to process, learn and then perform. So business decision as to sourcing of data and what data should be used to initially teach the intelligent system becomes critical.

The second challenge is building and training process of the intelligent platforms themselves. It is a highly math-oriented, algorithm intensive process which requires lots of capabilities in the form of skilled developers and engineers so that they get the analytical model right. Here, it becomes important that these people know the latest mathematical modeling, techniques and statistical methodology so that they build and train the intelligent automation platform which can deliver maximum benefit to the business enterprises.

The last challenge is to get the acceptance of intelligent automation in the IT estate of the enterprises. It becomes meaningless if the predictions of the intelligent automation platforms is neglected at the ground level. It makes little sense if the predictions of intelligent automation, does not result in some action by the stakeholders, does not invoke the innovative thinking to proactively resolve business or IT issues on a day-to-day basis.