It’s Time to Take a Customer-First Approach to AIOps

Artificial intelligence for IT operations (AIOps) has several use cases that IT operations managers can’t deny: reduce alert noise with statistically significant outcomes (up to 80%), correlate alerts and events to uncover the critical business issues immediately, analyze data across environments to find root causes, and resolve routine issues (like patching) automatically. Gartner predicts that large enterprise use of AIOps tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.

I’ve been working in the AIOps field for a few years now, and I’ve learned that many organizations even with relatively modern IT operations management (ITOM) software are behind the curve. Vendors keep releasing new algorithms, yet the original ones are barely being used or worse, still sitting “on the shelf” in the customer’s environment. This is because it can be difficult to understand when and how to use artificial intelligence (AI) tools in the daily workflow, and because IT professionals may not inherently trust (or even want) machine-driven recommendations and actions.

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AI and machine learning technologies are upending traditional assumptions and conventions in many industries – and IT is no exception. In the land grab to bring to market the latest sensational AIOps capabilities, vendors have unwittingly de-prioritized customer adoption. This has resulted in a chasm: the technology is getting ahead of the customer’s willingness and ability to implement these solutions in their production environment.

The feature-first perspective in the software industry is nothing new, yet for AIOps to reach its full potential, it’s time to take a customer-first perspective.

Let’s walk through a few ideas to minimize barriers to adoption and speed up time to value for AIOps:

1. Start on adoption during build.

Machine learning developers and product managers need to understand potential customer barriers during the design and test phase and put themselves in the user’s shoes. How will an individual interact with the system and what level of transparency is appropriate to ensure a frictionless experience?

This is a best practice for any software development team, but given the mystique of AI software, it’s even more important. Document expectations for the user experience by giving users early access to the software, such as a beta program, where they can freely deliver feedback on UI, workflows and outcomes of AIOps features.

2. Connect the dots with visualizations.

A significant barrier for any machine learning application is that users may not fully understand the use cases nor trust what the algorithms are doing. Ensure that software-driven actions and recommendations are transparent at the moment and location they occur in the application.

It should be easy for users to see the cause and effect of machine-driven actions as well as how the AI reached a conclusion.

The concept of “explainable AI” has become popular in the last year and vendors can help by introducing visualizations into their tools. For instance, you could have graphs showing the progression of a model being trained or the learned sequences in alert correlations. In-app simulation tools can help a customer see the impact of an AI-delivered recommendation without actually making the change.

3. Amp up training.

Create internal-facing white papers or videos to demonstrate the workings behind algorithms to your technical staff and tools specially designed for users.

The best way to learn is by doing, so designing visual cues and tutorials within the application will be most effective. Ensure that you have dedicated AIOps subject matter experts who can guide customers through implementations and be on call for advice as needed.

Common questions include, when should the AI be allowed to automate an action or decision and how can you override an automated decision? It’s not a bad idea to involve machine learning developers in creating training sessions and guides.

Finally, the age-old concept of nurturing power users within the customer organization can go a long way.

4. Diffuse concerns over jobs.

Nobody wants to use a new technology which might eventually replace them. For the most part, AIOps is not going to jettison IT operations staff. Instead, AIOps will empower Ops and support teams to handle increasing alert volume and complexity with existing staff while ensuring that expensive engineers such as SREs can focus on higher-level data performance analysis and optimization initiatives.

SEE ALSO: What is the AI adoption success formula?

5. Design algorithms for flexibility.

Machine learning algorithms break if they cannot respond to the dynamic nature of modern workloads.

For instance, if you design the algorithm based on rules dictating actions between specific applications and infrastructure components, what happens when those technologies disappear or are replaced, as they often do in the cloud? Now you’ve got gaps that could result in a catastrophic system failure and an AIOps tool that requires heavy maintenance.

Algorithms should be responsive to environmental changes – such as through incorporating neural net technology that can adapt to the shifting sands of the IT landscape.

A survey conducted in April by OpsRamp found that nearly 70% of IT operations leaders plan to invest in AIOps to improve incident diagnosis, troubleshooting and resolution. By all accounts, the year 2020 has raised the bar for IT expectations around resiliency and user experience. AIOps promises to be a pivotal strategy for IT operations success, but it all depends upon user satisfaction and adoption of the technology. Vendors that incorporate customer adoption and value tactics into their development processes will be instrumental in pushing this market forward in a positive way.

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