The last thing an IT team wants to hear is ‘there is an issue’ which usually has them rushing to ‘battle zones’ to try and resolve – ‘problem with the apps?’, ‘is it the network?’, desperately trying to kill the problem while it grows larger within the Enterprise. No credits for crumbling SLAs, the fire-fighting continues long and hard sometimes.
IT Operations are most times battling heavy volumes of alerts, having to deal with hundreds of incident tickets that come from the environment, from the performance of its apps and infrastructure. They are constantly overwhelmed trying to manage and respond to every alert in order to avoid the threat of outages and heavy losses.
Increasing components within the infrastructure; today a stack can have more than 10,000 metrics, and that sort of complexity runs the threat of increase in points of failure, and with the addition of speedier change cycles provided / supported by DevOps, cloud computing and so on, there really is very little time to take control or take action. Under such circumstances, AIOps is fast emerging as a powerful solution to deal with the constant battle, with the efficiency that AI and ML can bring in. We are looking more and more into unsupervised methods / processes, to read data and make it coherent, make it ‘see the unknown unknowns’, and remediate/ bring problems into focus before it impacts customers. Adopting AI into IT Operations provide an increased visibility into operations through Machine Learning and the subsequent reduction in incidents, false alarms and the advantage of predictive warnings that can do away with outages. It means insights are implemented thru automation tools leading to saving time and effort of the concerned teams.
With AIOps gathering and processing data, we require very little or almost nil manual intervention where algorithms help automate, due diligence gets done, and rich business insights are provided. AIOps becomes the much sought-after solution to the multitudinous problems in complex IT Enterprises.
“The global AIops Platform market is expected to generate a revenue of US$ 20,428 billion with a CAGR of 36.2% by 2025. – reports Coherent Market Insights
Gartner recommends that AIOps is adopted in phases. Early adopters typically start by applying machine learning to monitoring, operations and infrastructure data, before progressing to using deep neural networks for service and help desk automation.
The greatest strength with AIOps is that it can find all the potential risks and outages that may happen in the environment which can’t be done or anticipated by humans, and these operations can be conducted with greater consistency and time to value. The
complexity of an IT Enterprise is so huge though this makes an ideal scenario of ML, Data Science and Artificial Intelligence to help solutioning with specific, machine learning algorithms which is impossible for humans to reduce them in simple instructions and remediations. AIOps becomes the real answer to tackle critical issues and at the same time, it eliminates all the false positives that usually makes up a large percentage of ‘events’ that is reflected in monitoring tools.
Gartner predicted that by this year about 25% of the enterprises, globally, would implement an AIOps platform. And that obviously means increasing complexities and huge data volumes but deep insights and more intelligence within the environment. Experts say that this implies that AI is going to reach right from the device or environment till the customer.
AIOps is fast paced; it is believed that in the next decade majority of large Enterprises will take to ‘multi-system automations’ and will host digital colleagues – we are going to have virtual engineers to attend to queries and tasks. IT Service desks are going to be ‘manned’ by digital colleagues, and they are going to take care of the frequent and mundane tasks with almost nil or minimal human intervention. It is predicted that this year will see the emergence of ChatOps, where enterprises are going to introduce “AI based digital colleagues into chat-based IT Operations”, and digital colleagues will make a major impact on how IT operations function.
Establishing digital service desk bots brings in speed and agility into the service. Reports say that actions which hitherto took up to 20 steps can now be accomplished with just one phrase and a couple of clarifications from the digital colleague. This can save human labor hours and have their skills channeled to more important areas with mundane and frequent tasks such as password resets, catalogue requests, access requests and so forth being taken care of by digital colleagues. They can be entrusted with all incoming requests and those which cannot be processed by them are automatically escalated to the right human engineers. Even L3 & L4 issues are expected to be resolved by digital colleagues with workflows being created by them and approved by human engineers. AI is going to keep recommending better and deeper automations, and we are going to see the true power of human / machine collaboration.
Humans will collaborate more and more with digital colleagues, change requests get created on a simple command with resolutions to be had within minutes / or assigned to human colleagues. Algorithms are expected to integrate operations more and more. Life with AI is going to make tasks such as identifying and inviting right people into root cause analysis sessions and have post resolution meetings to ensure continuous learning.
With AIOps, IT operations is going to reconstruct most tasks with AI and automation. It is reported that 38.4% of organizations take a minimum resolution time of 30 minutes on incidents and adopting AIOps is definitely the key. We may be looking at a future where we would have the luxury of an autonomous data center, and human resources in IT can truly spend their time on strategic decisions and business growth, work on innovation and become more visible to an organization’s growth.
Nowadays, the number of software systems used by organizations has increased drastically. Since IT infrastructures comprise of complex software systems, the number of incidents has also increased. An incident response process requires collaboration between various IT teams in an organization. Better the communication between IT teams, faster is the incident resolution. Organizations need an effective […]
In the past few years, AI has proved transformational for numerous industries, including the manufacturing and logistics industry. AIOps (Artificial Intelligence for IT Operations) is being used by manufacturing and logistics firms to improve their productivity.
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