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AIOps Goes Mainstream

By Akhilesh Tripathi, CEO, Digitate

In 2023, we can expect to see significant developments across many areas of artificial intelligence IT operations (AIOps) as enterprises seek resilience and further optimization across their IT operations. AIOps moves ever into the mainstream of business operations, and we can expect to see that continue at pace next year, especially in the field of sourcing and procurement as organizations seek optimum purchasing conditions to maximize return on investment (ROI) for each transaction.

Enterprises embrace AI to gain resiliency among economic uncertainty

During economic uncertainty, enterprises want improved business uptime, productivity gains and revenue assurance. To gain an advantage, they will have to build an autonomous enterprise that is built on the pillars of AI, machine learning (ML) and intelligent automation. This will help with business scaling and resiliency and creating competitive differentiation needed during uncertain times.

The importance of being able to demonstrate business value that during economic downtime will be key. Gartner, at a recent keynote in London, stated that just 17%of organizations are consistently able to demonstrate the business value of IT. That percentage has to get better moving into the new year. In 2023, organizations will increasingly use automation to maximize productivity. Expect greater adoption of AI to make IT systems more resilient without growing costs. Using AI, businesses can automate some of the most essential and elemental IT operations tasks, such as monitoring alerts managing employee onboarding and offboarding. In doing so, companies not only make their IT systems stronger, they also free up skilled IT staff to focus on higher value projects. Expect greater adoption of cloud and multi-cloud operations. Sustainability metrics are also a major focus, so AI has a role to play there in supporting organizational efforts to meet their goals.

Moving from observability to adaptive observability will optimize ITOps resources

In 2023, except to see ‘adaptive observability' gather momentum. This emerging capability takes observability, the ability to deduce the internal state of the system by analyzing the outputs, and applies intelligence based on intelligent deep data analytics, to increase or decrease monitoring levels in response to the system health of specific IT operations. Previously, a very fine monitoring level can result in the collection of large volumes of data that can become unmanageable, and which also carries the risk of missing genuine anomalies in the noise, while a very coarse monitoring level can lead to the collection of too little data and can lead to incomplete diagnosis and insufficient insights.

Adaptive observability integrates two new and active areas of research - adaptive monitoring and adaptive probing - to actively assess ITOps and intelligently route and re-route monitoring levels and data gathering depth and frequency to areas where there are issues. When an issue is identified, the amount of data collected, relative to the issue, is increased. Once the issue is resolved and things are healthy, then it's no longer necessary to collect as much data, and at such a high frequency, resources can be deployed elsewhere in ITOps. Adaptive observability streamlines decision-making, problem solving, and optimizes IT resources. 

Explainable AI will gather momentum, provide clarity around decision making, verification, and reduce risk

The biggest strength of deep learning is its ability to combine multiple layers of processing elements, enhanced ability to utilize large compute power, and improved data training procedures, empowering deep learning algorithms to learn hugely complex patterns from massive volumes of data. One challenge with deep learning is that it has been something of a ‘black box' closed solution, in that it is often not apparent how a deep learning algorithm arrived at a decision. For companies looking to embed greater levels of deep learning into their data management systems, ‘explainable AI' has grown in importance, providing a transparent ‘white box' that is driving greater AIOps (Artificial Intelligence for IT Operations) adoption.

The open nature of explainable AI enables users to better understand how an AI model chooses options and to identify potential sources of error. Explainable AI gains the trust of experts and can truly augment their intelligence and expertise. Understanding the inner workings of the algorithms is important, especially when working on critical applications in, for example, healthcare, where there needs to be visibility of decision-making at every juncture, because oftentimes patient outcomes are at stake. A false positive diagnosis for a condition could see patients having expensive and unnecessary treatment that often has significant side effects, whereas a false negative could mean patients don't receive the treatment they need. Explainable AI makes the reasoning behind algorithmic outputs clear and understandable to users, removing risk and ensuring that decisions can be checked and verified.

AIOps will drive intelligent digitized procurement momentum, delivering tangible improvements in supply chain sourcing and procurement

There's a saying that "it only takes one bad apple to spoil the barrel." One could apply that adage to supply chain management, and in particular, sourcing and procurement (S&P), a vital element that enterprises must closely track in order to maximize value, efficiency, and ultimately, their bottom line.

As more enterprises digitize their supply chain operations and AIOps move into mainstream business processes, Cognitive Procurement leverages data and deep learning, enabling organizations to go from the periodic review and analysis of the downstream procure-to-pay processes to real-time monitoring and reporting to agreed parameters and thresholds. It enables real-time reviews across existing suppliers to gauge reliability, quality of service, service-level agreement (SLA) adherence, and pricing, allied to comprehensive visibility and knowledge of the wider market externally, alternative suppliers, and their offerings. Having a clear line of sight across all options open to them enables enterprises to make optimal S&P decisions. Cognitive Procurement will drive considerable tangible benefits for enterprises in relation to S&P agility, the continuous assessment of performance, identifying supplier bias, and price variance analysis.

Deep learning algorithms deliver organizational value through price variance analysis

Organizations can potentially realize substantial cost benefits and optimize sales by deploying AIOps' deep learning to look at procurement purchases historically and compare prices to current open market conditions across any number of assigned items. By observing what items have been bought, where and for how much - as well as factoring in any nuances that need to be considered (e.g., seasonal fluctuations, incentives and bulk discounts, inflation). AIOps can leverage analysis to generate optimum purchasing conditions, including factoring in tax tagging relative to market geography.

Standardization of monitoring and security across "CloudOps" will become an IT must-have

Enterprises are moving deeper into multi-cloud operating environments to provide greater IT operational confidence with each cloud vendor having proprietary best practices and tools, allied to private enterprise clouds. To streamline ITOps, the need to manage all these clouds in a single "pane of glass" agnostically, to have sight of every server for example, wherever it is, and its status, will become a must-have for enterprises. Discovery, health checks, and auto-remediation are the three core elements for any DevOps and CloudOps teams. Extending the capabilities to multi-cloud along with monitoring and taking corrective action is also a must-have for risk management.

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ABOUT THE AUTHOR

Akhilesh Tripathi, CEO, Digitate

Digitate CEO Akhilesh Tripathi joined the company in 2015 to launch its flagship product, ignio. Under his leadership, ignio became one of the fastest-growing enterprise applications, with a global customer base spanning many industries and Fortune 500 companies. 

Previously, Mr. Tripathi served as the head of Canada for TCS (Tata Consultancy Service), where he grew the entity from a small, relatively unknown firm to a perennial top 10 service provider. His 25-year career with TCS has also included serving as Head of Enterprise Solutions and Technology Practices for TCS in North America.

Akhilesh has also been active with volunteer organizations including the IEEE, Operation Eyesight, and the Canada-India Business Council. He earned an MBA from the University of Michigan (USA) and a Bachelor's Degree in Electronics from Savitribai Phule Pune University (India).