-B Swaminathan
As hardware and software becomes more powerful, it also becomes more intricate, creating increased demand on the IT departments who are responsible for managing it. And with every new advancement and capability, tool complexity increases. Until recently, IT operations teams have had few options when it comes to tackling the expanding complexity of vital technologies—hiring new IT data science talent and increasing department staff being the most obvious, if not the most cost effective, solution.
The term AIOps was first coined by Gartner in 2016, and grew out of the digital-transformation shift from centralised IT to anywhere operations with workloads in the cloud and on-premises across the globe. As the pace of innovation increased, so did the complexities of the technologies. This placed significant strain on IT operations, who would now be responsible for managing and servicing a range of new systems and devices. AIOps introduced a new model for managing IT operations. Machine learning has revolutionised modern business. In fact, according to The Global CIO Point of View, nearly nine out of ten CIOs are either already employing this technology, or are planning to adopt it soon.
AIoPS In India:
India is a major player yet to develop in the AI market . Although few verticals like IT services, BFSI, healthcare, media are leading the AI market share, there are still major areas which needs to be tapped like education, e-gov , transport etc . Organisations are hesitant to adapt AI with the fear machine learning will reduce human intervention and apparently will displace manpower, resource recruitment . But if you think optimistically, it will create more opportunities in the coming decades, such as AI and Machine Learning Specialists, Data Scientists, Information and Security Analysts, IoT Specialists, Big Data Specialists. Many businesses are not ready as there is a dearth of resources, and they may face skill gaps. The number of scientists and researchers in the field of machine learning and artificial intelligence is not as good as compared to the other segments. However, the government has introduced several programs of personnel training and technical infrastructure and is taking several measures to promote AI & RPA.
In India, the scope for AIoPS into the current Indian enterprises is massive and there are many companies who are willing to adapt & invest in these technologies. “There are some insurance companies that have already included AI into their operation systems for claims management, underwriting processes, enhancing customer support & employee collaboration, product innovation, policy administration etc so as to make the tasks faster. The inclusion of such technologies has sped up the current processes and has helped to fix bigger issues quickly & potentially reduce human errors.”, says Rakesh Goyal, Director, Probus Insurance.
Main Use Cases:
In case of any damage to the vehicles, there is an inspection procedure that is conducted during the underwriting process. Having said that, the AI would help to verify the uploaded images of the vehicles during any such incidents and validate different attributes involved during the verification process of any claims. Some companies have already initiated using AI for such kinds of accurate verifications. For HR, AI can be used to check the mental health or sentiments of the employees. This can be examined through the various questions that the AI tools generate. This then helps to understand the current sentiments of the employee towards the company and also helps to gauge the approx number of years that the employees would sustain in the company based on their inputs.
This could be highly used for employee engagement & retention. With the help of AI, the Ops Team would be further able to handle massive complexity & huge volume of data thereby preventing failures, maintaining the uptime of the processes and achieving continuous service.
According to V.Sendil Kumar, from Shriram Capital, “Indian Enterprises are eager and focussing towards automating the process. The concerns they have are technology skills, third party integrations, security risks and costs. However, organisations have started and they are approaching in a phased manner.”
However, some advances actually do help take certain pressures off of IT Operations (ITOps). Consider the emerging technologies of Artificial Intelligence for Operations (AIOps). AIOps is a combination of the terms artificial intelligence (AI) and operations (Ops). The large Indian companies are taking some initiatives and getting ready but not fully. Reluctance due to Lack of Talent, Lack of Management drive due to other priorities, Lack of a strategic approach to AI adoption, Cultural barrier.
O A Balasubramaniam, who heads the entire IT of Roots India, a leading manufacturing company, feels that AI will first be utilized in Manufacturing and HR will get first priority followed by Finance. “But technology wise speech recognition for streamlining the factory floor, computer vision in the inspection area and natural language processing in HR recruitment are very relevant priorities .It Helps in extensive automation and Security. IT tech leading companies will take it and lead it.”, he says.
More specifically, it represents the merging of AI and ITOps, referring to multi-layer tech platforms that apply machine learning, analytics, and data science to automatically identify and resolve IT operational issues.
Way ahead:
Mangesh Pardeshi, Director, Comnet solutions, a leading solution provider, there will definitely be a boom in the AI and machine learning sectors in the upcoming years. Major businesses are exploring to adapt themselves to AI for better productivity and achieving better ROI. “AIOPS being the combination of big data & machine learning to automate IT operation processes that includes event correlation, anomaly detection and casualty determination. Businesses are combining this data with new applications at the edge to improve operational efficiency and create new revenue streams. AIOPS will help reduce trouble tickets by identifying issues before they impact the business which further enhances productivity.”, Mangesh concluded.
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