Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?

If your organization is looking to improve its IT service management (ITSM) and/or IT Operations Management (ITOM) capabilities, then it’s probably considering Artificial Intelligence for IT Operations, which is commonly called “AIOps.” But what is AIOps, and how will it help your IT organization’s IT management capabilities and, ultimately, business operations and outcomes? Let’s start with an AIOps definition.

Every year there is a surprise in a Radar report. While it won’t be a surprise to our thousands of customers who are seeing tremendous benefits with us, PagerDuty is excited to be named a Leader in the 2022 GigaOm Radar for AIOps Solutions. GigaOm uses extensive criteria to evaluate vendors in their Radar.

Multi-cloud is inevitable. With AIOps, struggling in its complexity doesn’t need to be. Business technology stacks don’t appear out of a vacuum. For the modern cloud-enabled, cloud-dependent company (that is to say, most of them), the look from the inside looks more like an ongoing evolution than a monolithic choice.

Operations staff get a hard time. The lowly systems administrator (sysadmin), database administrator (DBA) and all the other operations engineering team members from cyber penetration specialists to user acceptance testing (UAT) and so on are generally unloved.

In June, the research firm GigaOm, published the 2022 edition of their annual Radar for AIOps Solutions, having had time to digest the contents, it seems a good time to summarize the key takeaways from the Moogsoft perspective. Firstly, in case you are not familiar with GigaOm, here’s a brief introduction.