AIOps On The Road To The Autonomous Enterprise

Innovation AIOps On The Road To The Autonomous Enterprise Akhilesh Tripathi Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Jun 13, 2022, 06:00am EDT | Share to Facebook Share to Twitter Share to Linkedin Akhilesh Tripathi, CEO of Digitate , manages the product, market, channel and partner strategy for Digitate’s flagship product, ignio.

getty The concept of IT as a cost center ripe for cost-cutting suddenly, and well-deservedly, disappeared when the global economy slammed into 2020. Winners and losers quickly divided along the lines of their stance toward IT. Organizations that entered the fray under the banner “IT as a cost” found themselves on the losing end of a bet-your-business game.

On the winning side were enterprises that viewed—and strategically invested in—IT as a source of business innovation. Innovation Under Fire The issue is that human efforts can’t keep up. The more up-to-date and custom-tailored your IT stack becomes, the more complex it is to manage.

Today, the sheer volume of technologies, transactions, interdependencies, variables, change and possible points of failure requires automation and artificial intelligence (AI) to drive it. Innovation is hampered as a result. Because of this transformation, the concept “AIOps”—AI for IT operations—arose.

By leveraging the power of AI and machine learning, organizations can accurately comprehend and control the complexity of a sprawling IT landscape. The goal is to move from the classic observe-engage-act model to a more active policy of predict-prescribe-prevent. This relies on a human to act on the AI’s recommendations.

The logical next step is to let AI take recommended actions automatically to close the loop. Beyond that is the vision of an “autonomous enterprise,” in which AI and automation combine to enable a business’s key technologies to solve their own problems without prompting. This may seem like the proverbial “art-of-the-possible,” but it’s very doable—with the right technologies.

MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event How AIOps Can Make IT Management Smarter AIOps solves a problem created by a previous generation of solutions—technology management tools. These tools are designed to respond to user requests, monitor operations and identify problems. When a failure happens, or a user submits a ticket, they send out alerts; modern versions can even try to diagnose the problem and offer solutions.

But such tools are hampered by being merely reactive. They’re limited by technology and management silos to narrowly focus on individual components. They also generate enormous numbers of events and alerts, which must be manually triaged and resolved.

The problem is compounded by the ongoing shortage of IT workforce and the draining nature of eyes-on-glass monitoring, especially when workloads or components can change any minute. In contrast, AIOp can interpret vast stores of data produced by a large-scale, complicated and diverse IT landscape. It can figure out not only what to do at the point of technology failure, but how to avoid a failure in the first place.

Enhancing Operations with Human Feedback Of course, humans are still a success factor in making your IT operations autonomous. The closed-loop, one-two punch of AI and automation requires humans to be deeply involved, first in training the intelligence and then serving as its backup, manually resolving complex or ambiguous issues. As trust develops in the AI models and the actions they suggest, humans are released from time-consuming routine fixes and rushing to resolve high-stakes incidents.

Freed up for higher-level tasks, they can become more productive (and less likely to burn out, an important factor now that key IT skills are in short supply). Moving AIOps from Concept to Reality There are many approaches to moving AIOps from a concept to a driver of digital transformation. Enterprises using the automation-first approach—where machines using the right AI technologies become the first line of problem solvers before passing it on to humans—have seen the maximum value benefits of AIOps.

There are a few ways to do this. Deploying AIOps begins with context. For each observable element such as IT service, traffic volume or component state, the AIOps solution must learn what normal operations look like.

So its first step: ingesting data from multiple sources in the enterprise’s IT operations (e. g. , assets, people, activities) to establish transparency and auditability.

This will help map a unified and comprehensive view of normal activity across all data silos. This observation is complemented by the tacit aggregated knowledge that the workforce possesses, which is codified into the system. The result: a complete model that combines context across all applications and systems—a digital twin of your IT landscape.

Then the AIOps solution continually compares its digital twin against real-time operations, looking for changes. When it identifies anomalies or aberrant behavior, it consults historical patterns and the rules and policies it’s been given by human operators. If it recognizes that anomaly, it can predict negative outcomes and autonomously take action to prevent them.

For instance, if activity spikes regularly occur in the main financial database at the end of the quarter, AIOps and automation acting in tandem can autonomously calibrate and provision the load. As a result, the AIOps solution autonomously maintains a “dynamic normal” state of operations throughout your whole IT landscape. Closing The Loop With AIOps Alternatively, organizations can start with finite, high-impact use cases to solve focused problems by integrating AIOps with existing tools.

Event management, SAP IDoc management, auto-triaging or automated root cause analysis are some common examples. These use cases will typically result in immediate efficiency or productivity gains. Successful, quantifiable results can guide your next moves, critical for IT organizations strapped for time and budget.

Also, you’re upskilling your in-house talent pool to develop AI expertise and confidence. The bottom line: combining AI and automation can transform IT functions and boost appreciation of its critical role as a key business partner. Automation becomes a business proposition enabled by technology, rather than a technology that improves the business.

The results of the AIOps journey are not immediate, but they can be dramatic. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Follow me on Twitter or LinkedIn .

Check out my website . Akhilesh Tripathi Editorial Standards Print Reprints & Permissions.