By 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human. - Gartner Predicts
Artificial intelligence is a concept that is causing many ripples in the technology space. Growth in hardware technologies, analytical models and engines, & finally data are the chief reasons creating this hype. In recent times, we have seen many ground breaking news on AI, ranging from self-driving cars to technology major acquiring AI startup, to defense, to healthcare.
Among all these hype, the basic question for the business world is: How can AI help them bring their cost down and performance efficiencies up!
In my view, AI is still in its nascent stage for adoption by enterprises. Though enterprises have a large pool of data, they are skeptic in terms of how benefits can be availed given their scope and size.
I shall try to trace the use-cases corresponding to stages of application & then to maintenance of infrastructure.
Some examples are:
1. Fast-Coders in the Making AI via Machine Learning has shown its capability to understand human language (e.g. Siri). Siri, not only responds to your queries, but also understands the intent behind your query. Envision a scenario that you are using any SDK to write your code. Now the moment you put // or /* … */ (documentation comments) and write the intent/functionality/use-case of that code in plain English, the bot pulls out the relevant code from code repository (SVN/Team Foundation Server) and helps you complete the code. Alternatively, it can refer to those codes and help you to finish a piece of logic faster!
Therefore, in this case, we have a coding-helper bot, trained on the code repository of the enterprise (for more maturity, code available on GitHub can be used), and can suggest code modules/functions which can be used by the developers for faster coding.
2. Automated Testing Automated testing has become an integrated solution as part of many managed services offerings and is a highly competitive field. Almost all major service providers have presence. For more information, you can refer to any of the analyst reports, Everest etc.
3. Bots for Maintenance AI bots may soon replace physical human beings in doing mundane maintenance tasks like swapping server racks. There may be bots, which are monitoring each of your IT estate and predict network and storage failures, storage limits threshold crossing, temperature regulations etc. Maintenance activities are bread & butter of many IT companies and currently many such companies are working to utilize their expertise to build bots for predictive and preventive maintenance.
4. IoT is here to Stay The concept of tools, devices, objects (electronics used in daily life), and infrastructure being connected to each other and working in tandem to create an ecosystem of smarter & responsive devices brings with it unprecedented convolution. The challenge here is going to be on how to make sense out of all the unstructured data, which can help in deriving actionable intelligence. This is where enterprises will have to use the AI algorithms for classification etc. for actionable acumen.
5. Robust Cyber-security We all know about the two attacks (largest as well) on the security breaches in Yahoo network. Similar case came for Apple as well.
AI can be used to divulge in-progress attacks as it can learn the patterns across devices and network, and report any anomalies! Hence, mitigation action can be taken while the breach/intrusion is still in-progress!
Artificial Intelligence, though far from being accepted as an end-to-end solution, has been adopted in the form of various point solutions at application and infrastructure level. The time is not far when enterprises start taking definitive steps to integrate it within their overall strategic framework to achieve business goals.
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