Has the evolution of Digital Workplace, also transformed AIOps

Artificial Intelligence for IT Operations (AIOps) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics. The concept of AIOps has been in existence and talks since the last 3-4 years, Gartner was predicting that IT Operations personnel were in for a major change over the next few years and traditional IT management techniques were viewed as unable to cope with digital business transformation. It was deemed fit to say that the evolving platform/practice/framework would be know as AIOps.

As we review the current state of affairs for the organizations in 2021, one of the major goals to be accomplished is themed around automation with quick wins in the area of the Technology Operations (or as we say BAU or Keeping The Lights On). With new worl force focused on engineering related/enabled jobs, we either need to keep finding IT Operations personnel available in the market or focus on automating the operations related activities (mostly L1 for now).

Digital transformation encompasses DevOps and the adoption of cloud and new technologies like containers. It represents a shift from centralized IT to applications and developers, an increased pace of innovation and deployment, and the acquisition of new digital users—machine agents, Internet of Things (IoT) devices, Application Program Interfaces (APIs), etc.—that organizations previously didn’t need to service.

All of these new technologies and users are straining traditional performance and service management strategies and tools to the breaking point. AIOps is the ITOps paradigm shift required to handle these digital transformation issues.

AIOps deals with multi-layered technology platforms that automate and enhance IT operations through analytics and machine learning (ML). AIOps platforms leverage big data, collecting a variety of data from various IT operations tools and devices in order to automatically spot and react to issues in real-time while still providing traditional historical analytics. Since the scope of AIOps can be very large, it's important to take baby steps to achieve some of the quick wins and get started with the laying a framework that delivers to keep the leadership interested. Here are some of the key steps to start your work on AIOps:

1. Start with using the right set of monitoring tools - acts an set of eyes keeping a check on your environment

2. Engage your ServiceDesk/NOC/Service Management teams - act as a facilitator to get the right team on common ground while troubleshooting

3. Automate the troubleshooting steps through scripts, run books etc. and ensure the next time this problem comes the script take care of the L1 steps - saves time, money and resource

This is not a new phenomena, but an enhanced version of PDCA i.e. Plan, Do, Check, Act (as referred in ITIL). The goal is to receive continuous insights which provide continuous fixes and improvements via automation. This is why AIOps can be viewed as CI/CD for core IT functions.

As technology evolves, so does the framework !

As an organization, you might continue to ignore the need of AIOps, but with the modern goals in place you will end up performing some pieces of it anyways. So it;s better do it in an organized manner. We have talked enough about the framework, let's also try to understand the need for AIOps and list down the problem statements that are currently being faced or will cross our path in future:

• The amount of data that ITOps needs to retain is exponentially increasing. Performance monitoring is generating exponentially larger numbers of events and alerts. Service ticket volumes experience step-function increases with the introduction of IoT devices, APIs, mobile applications, and digital or machine users. Again, it is simply becoming too complex for manual reporting and analysis.

• Infrastructure problems must be addressed at ever-increasing speeds. As organizations digitize their business, IT becomes the business. The “consumerization” of technology has changed user expectations for all industries. Reactions to IT events—whether real or perceived—need to occur immediately, particularly when an issue impacts user experience.

• Developers have more power and influence but accountability still sits with core IT. As I talk about in my post on application-centric infrastructure, DevOps and Agile are forcing programmers to take on more monitoring responsibility at the application level, but accountability for the overall health of the IT ecosystem and the interaction between applications, services, and infrastructure still remains the province of core IT. ITOps has to take on more responsibility and continue to perform these duties in a traditional manner will not be cost effective.

As IT moves beyond human scale, IT tooling needs to adapt. But simply reacting defensively is not enough. The organizations that embrace AIOps will see the challenge it is meant to address as an opportunity to grow, evolve, innovate, and disrupt. Here are some ways that AIOps-enabled organizations will transform their business in the next five years.

• Technology becomes more human: Analytics and orchestration enable frictionless experiences, allowing ubiquitous self-service

• The automation of technology, and, hence, business processes: Costs lower, speed increases, and errors decrease while freeing up human capital for higher-level achievement

• Enterprise ITOps gains DevOps agility: Continuous delivery extends to operations and the business

• Data becomes currency: The vast wealth of untapped business data is capitalized, unleashing high-value use cases and monetization opportunities

If you plan to kick start AIOps related initiatives in your organization, try the below listed steps to idealize the plan and start celebrating small wins

1. Don’t wait. Become familiar with AI and ML vocabulary and capabilities today, even if an AIOps project isn’t imminent. Priorities and capabilities change, so you may need it sooner than you expect.

2. Choose initial test cases wisely. Transformation initiatives benefit from starting small, capturing knowledge and iterating from there. Take the same approach to incorporating AIOps for success.

3. Develop and demonstrate your proficiency. Demystify AIOps for your colleagues and leadership by demonstrating simple techniques. Identify skills and experience gaps, then assemble a plan to fill those gaps.

4. Experiment freely. Although AIOps platforms are often products of substantial cost and complexity, a great deal of open-source and low-cost ML software is available to enable you to evaluate AIOps and data science applications and uses.

5. Look beyond IT. Leverage data and analytics resources that may already be present in your organization. Data management is a huge component of AIOps, and teams are often already skilled. Business analytics and statistical analysis are key components of any modern organization, and many techniques span problem domains.

6. Standardize where possible, modernize where practical. Prepare your infrastructure to support an eventual AIOps implementation by adopting a consistent automation architecture, infrastructure as code (IaC) and immutable infrastructure patterns.

7. Visualize full adoption. There are many variables: Available products will evolve, as will the AIOps “state of the art” and the infrastructure and applications for which you’re responsible. Consider the build-versus-buy continuum and how much of each to use.

To succeed in creating a culture of automation, identifying and promoting internal champions are fundamental steps to getting the workforce on board. Champions demonstrate to their peers that these disruptive technologies can impact their professional lives for the better, taking over monotonous, mindless tasks and opening up growth and upskilling opportunities. This is particularly relevant for companies seeking to attract and retain millennial talent. Digital natives are known to have a low tolerance for tedious activities. Successfully implementing AIOps and automation can help ensure these workers enjoy fulfilling jobs, thereby increasing employee satisfaction and improving staff retention rates.

If you think automation and AI are mere buzzwords or passing industry trends, think again. Sure, robust IT infrastructure is the backbone of a smooth-running business – but this is not enough to keep up in today’s competitive market. An agile IT strategy that leverages automation and AI to optimize resources and drive positive evolution is simply non-negotiable for companies wishing to make their mark.