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.