Most AIOps job descriptions list the same things: observability stacks, scripting, cloud platforms, some exposure to machine learning, and a calm head during an outage. Very few mention the skill that quietly separates the engineers people trust from the ones people work around. That skill is getting a room to agree on what its words mean. If you want to grow an AIOps career, learning to pin down terms like “incident”, “anomaly” and “resolved” before anyone builds automation on top of them is worth more than another tool certificate. This article explains why, and how to practise it without becoming the person who derails every meeting with semantics.

Why vague words cost AIOps teams more than other teams

In a lot of office work, a fuzzy word causes a misunderstanding that someone fixes over coffee. In AIOps, fuzzy words get encoded. A correlation rule, a ticket routing policy, a model that scores events, a weekly report for leadership: each one turns a human word into logic that runs thousands of times without asking anyone what they meant.

Take the word “incident”. The service desk may use it for anything a user reports. The SRE group may reserve it for customer-facing impact. The security team has its own meaning entirely. If an AIOps platform is trained or tuned on historical “incidents”, the model learns whichever meaning happened to dominate the data, and nobody notices until its output looks strange. The same goes for “anomaly” (statistically unusual, or actually harmful?), “root cause” (the first failing component, or the change that triggered it?) and “resolved” (service restored, or underlying fault fixed?).

None of these are trick questions. Each has a reasonable answer. The trouble is that different people hold different reasonable answers at the same time, and the automation can only follow one.

The career case: this is a visible, promotable skill

Engineers sometimes treat definitions as admin work that belongs to managers. That is a mistake, for three practical reasons.

• It shows up in outcomes people care about. When a team finally agrees that “resolved” means the customer-facing symptom is gone and the follow-up ticket is open, mean-time figures stop jumping around and leadership stops asking why the numbers changed. The engineer who drove that agreement gets associated with the calmer reporting.
• It is rare. Plenty of people can write a Python script that parses logs. Fewer can sit between an operations lead, a data scientist and a service owner and get them to sign off on one sentence. Scarce skills get noticed.
• It scales your technical work. A detection rule built on a clear term survives handovers. A rule built on tribal meaning breaks the first time someone new touches it. Reliable work is what gets you trusted with bigger systems.

If you are aiming for a senior, staff or lead role, notice how much of that job is alignment. Being good at definitions is alignment made concrete.

Where shared definitions matter most in AIOps work

You do not need a glossary of two hundred entries. Most of the pain comes from a short list of words that feed automation or reporting. A practical starting set:

1. Incident versus event versus alert. Decide which one a human must look at, which one is just a signal, and which one opens a record. Write the rule as a sentence a new hire could apply.
2. Severity levels. “Sev 2” should mean the same thing to the person on call at 3 a.m. and to the manager reading the report on Monday. Tie each level to observable impact, not to how stressed someone felt.
3. Anomaly. Separate “unusual compared with the baseline” from “unusual and harmful”. Models are good at the first and need human context for the second.
4. Service and dependency. If one team counts a database cluster as a service and another counts it as infrastructure, topology-based correlation will group events inconsistently.
5. Resolved and closed. These are often treated as synonyms. They are usually two different moments, and reports get much more honest once they are split.

For background on how event handling fits into day-to-day troubleshooting, the walkthrough on solving IT problems in real time as a beginner is a useful companion to this list.

How to practise defining terms without annoying everyone

There is a version of this skill that makes you tiresome: interrupting every discussion to ask what someone “really means”. The version that builds a career is quieter and more useful.

Pick the moment

Raise definitions when something is about to be built or measured: a new correlation policy, a dashboard request, a model retraining, a change to on-call rules. That is when a vague word is cheapest to fix. Raising it in the middle of an outage helps nobody.

Bring a draft, not a question

“What does incident mean?” invites a long debate. “Here is a one-line definition I am proposing for incident, plus two examples that are in and one that is out. Does anyone disagree?” invites a quick yes or a specific objection. Drafts move faster than open questions.

Use real records as test cases

Pull ten historical tickets and ask the group to label them against the draft definition. Where people disagree, you have found the real fault line. This is far more persuasive than arguing in the abstract, and it gives you evidence when someone later says the definition does not work.

Name an owner

Every definition that matters needs one person or role who can change it. Without an owner, the term drifts back to tribal meaning within a few months. Often the natural owner is the service owner or the head of operations, not the AIOps engineer, and that is fine. Your job is to make sure the owner exists and knows they own it.

Put it where the work happens

A definition living in a forgotten wiki page has little effect. Link it from the runbook, the ticket template, the rule description in the AIOps platform and the report footer. When the word appears, the meaning should be one click away.

Borrowing from the data world

AIOps sits close to data engineering, and data teams have spent years wrestling with exactly this problem. A metric called “active users” or “revenue” can mean five things across five dashboards, and the fix has always been agreed definitions with clear ownership, enforced through data quality checks and governance. If you want structured exposure to those habits, the DataOps courses and consulting from DataOpsSchool cover data quality and governance practices that depend on everyone working from the same agreed terms. The habits transfer directly to event data and operational telemetry.

The connection is worth stating in interviews, too. Being able to say “I treat operational signals like a data product, with defined fields and an owner for each meaning” signals maturity that hiring managers in AIOps tend to value.

A short example from an on-call rotation

Picture a mid-sized platform team rolling out automated event correlation. Within two weeks, the on-call engineers complain that the tool is “merging unrelated incidents”. The vendor is blamed, then the configuration, then the model.

An engineer on the team pulls the grouped records and notices something simpler. The networking group logs every link flap as an incident. The application group logs only user-visible degradations. The correlation engine is doing its job; it just has two incompatible meanings of the same word in its input.

She drafts a single sentence: an incident is any condition that degrades a customer-facing service beyond its agreed threshold; everything else is an event. She tests it against twenty old records with both groups in the room, adjusts one edge case about internal tools, and gets the head of operations to own the definition. Ticket templates and routing rules are updated to match. The complaints fade, and the change is mentioned in her next review as an example of systems thinking. Nothing about it required new software.

Teams thinking about how this kind of groundwork fits into a wider rollout may find the guide on introducing AIOps to an existing IT group helpful.

Building the skill into your AIOps career plan

If you want this to count, treat it like any other skill you are developing.

• Keep a personal list of terms you have helped define, the before-and-after effect, and who owns each one now. That list becomes concrete material for reviews and interviews.
• Read your own reports critically. For every number you publish, ask whether a reader from another team would interpret its label the same way. If not, fix the label or the definition.
• Learn the vocabulary of neighbouring teams. Security, service management and data engineering each use overlapping words with different meanings. Knowing their versions lets you translate instead of argue.
• Write short. A definition that needs a paragraph is usually two definitions. Practise getting each one into a sentence plus examples.
• Revisit after change. New services, reorganisations and tool migrations all shift meanings. Make a habit of checking the key terms whenever one of those happens.

The bottom line

AIOps promises automation that reasons about operations at a scale humans cannot match. That promise depends on input that means something consistent. The engineers who make sure it does, by writing down a shared definition for the handful of words that drive rules, models and reports, and by making sure each one has an owner, produce work that lasts. It is an unglamorous skill, yet it ranks among the surest routes to becoming the person others ask for when something important is being designed.