Most AIOps evaluations go wrong in the same way. A shortlist is assembled from analyst lists and peer recommendations, each vendor demonstrates their platform against their own scenario, everything looks impressive, and the decision comes down to price and a feeling. Six months later the platform is deployed against a fraction of the estate and nobody can say whether it worked.
The problem is not the vendors. It is that the evaluation never established what was being evaluated. This guide is a framework for doing that: what to establish about your own operation before you look at products, nine criteria that separate platforms in ways that matter, how to test each one rather than take it on trust, and what to measure afterward so you know.
It applies whichever vendors make your shortlist, including ours.
Before you look at products — assess your own operation
Four questions, answered honestly, will narrow a shortlist faster than any feature comparison.
How specialized are your teams, and how well do they collaborate? Platforms that assume a single unified operations team behave differently in an organization where network, application and infrastructure teams each own their own tooling and escalate to each other. Be honest about which you are.
Where do your systems fail to connect? The incidents that cost the most are usually the ones that cross a boundary — between domains, between vendors, between the monitoring tool and the ticketing system. Write down the three worst incidents of the past year and mark where the boundary was. That list is your real requirements document.
How much of your work is reactive? If most of the day is spent responding to alerts that a customer has already noticed, your first requirement is detection before impact. If detection is largely solved and the pain is diagnosis time, your requirement is root cause. These lead to different products.
Is the pace of change outrunning your operations capability? Continuous deployment, network builds and elastic scaling all change the environment faster than a manually maintained model can track. If that describes you, discovery matters more than configuration, and it moves up your criteria list.
The nine criteria
1. Scope of assurance
What to look for: whether the platform covers fault, performance and change management, or addresses events and incidents with performance handled by a separate product.
Why it matters: this is the first question, because it determines how many of the others matter. Fault management can be performed from alerts — an alert is a report that something already crossed a line. Performance management cannot, because a baseline, a trend and a slow degradation are statements about the shape of data over time and cannot be reconstructed from another tool’s threshold verdicts. A platform covering only one is not a worse platform; it is a smaller part of the problem, and the rest becomes an integration project you own.
How to test it: ask the vendor to show a single incident analysis containing a performance degradation, the change that caused it, and the fault it eventually produced. Note whether that happens in one product or across several.
2. Ingestion from your existing monitoring estate
What to look for: whether the platform consumes alerts and events from the tools you already run, and how much work each integration takes.
Why it matters: no operator replaces a decade of accumulated monitoring to adopt an AIOps platform, and any evaluation implying otherwise is not realistic. This is table stakes — but the difference between an out-of-the-box connector and a services engagement is measured in months.
How to test it: name your five most important monitoring tools and ask what each integration requires, who does the work, and how long it took at the last three comparable customers.
3. Direct telemetry ingestion
What to look for: whether the platform also ingests metrics, events, logs and traces directly from infrastructure, or depends entirely on upstream tools to detect conditions and forward alerts.
Why it matters: a platform reasoning only over alerts can detect only what existing tools were configured to catch. Novel failure modes — the ones behind the worst outages — are precisely those nobody wrote a threshold for. Direct ingestion is also the prerequisite for performance management.
How to test it: ask the vendor to ingest raw telemetry from a domain you have not instrumented with alerting, and see whether anything meaningful surfaces.
4. Automated topology discovery
What to look for: whether topology is discovered and continuously maintained, or configured and manually updated.
Why it matters: topology accuracy determines correlation accuracy. A drifted model degrades silently — the model says one thing, the infrastructure does another, and nobody notices until an incident is misdiagnosed.
How to test it: make a change during the evaluation and measure how long the platform takes to reflect it without anyone intervening.
5. Cross-domain correlation
What to look for: whether correlation genuinely spans transport, core, access, cloud and application domains, or operates within each separately.
Why it matters: the most expensive incidents are those where the symptom appears in one domain and the cause sits in another. Per-domain correlation cannot find them by construction.
How to test it: present a historical incident that crossed domains and ask the vendor to walk through how their platform would have correlated it.
6. Root cause explainability
What to look for: whether the platform explains its reasoning or presents a conclusion — and whether a confidence score is being offered in place of an explanation.
Why it matters: an engineer will not act on an unexplained recommendation during a major incident, which means an unexplainable system does not get used at the moment it was bought for. In regulated industries, an auditor will ask how a determination was reached. This criterion has become more important, not less, as more of the reasoning moves into models.
How to test it: have an engineer — not an evaluator — review a sample of the platform’s conclusions and say whether they would act on them.
7. Remediation capability
What to look for: whether the platform recommends fixes, executes them, or only routes incidents — and what guardrails govern execution.
Why it matters: detection improvements plateau; resolution improvements compound. The gap between knowing and fixing is where most of the remaining time sits. The distinction between a platform that assists with runbooks and one that executes them is the single largest capability gap in this market, and vendor marketing rarely makes it visible.
How to test it: ask which classes of action can be automated, what approval gates exist, and what happens when an automated remediation fails. Then ask to watch one execute end to end.
8. Scale evidence, not scale claims
What to look for: production deployments at comparable scale — element counts, data volumes, subscriber numbers — rather than architectural assertions.
Why it matters: every vendor claims scalability. Few can point to a production deployment the size of yours.
How to test it: ask for a reference customer at or above your scale, and ask that customer what broke first.
9. Time to production
What to look for: realistic deployment timelines with evidence, and clarity on what drives them.
Why it matters: AIOps programs fail more often from stalled deployment than from technical inadequacy. A platform delivering value in a quarter beats a better platform delivering in a year.
How to test it: ask for the deployment timeline of the three most recent comparable customers — including any that ran long, and why.
Map your current tooling
Before evaluating what a platform ingests, write down what you already run. Most operations discover in this exercise that they own more overlapping tools than they thought, and that some of the cost case for AIOps is consolidation.
| Layer | Examples |
|---|---|
| Application and digital experience | Datadog, New Relic, Dynatrace, AppDynamics |
| Infrastructure and cloud | Datadog, Azure Monitor, Amazon CloudWatch, Google Cloud Operations |
| Network monitoring | SolarWinds, Riverbed, NetScout, ThousandEyes |
| On-premises infrastructure | ScienceLogic, Zenoss, Nagios |
| Logs and telemetry | Splunk, Sumo Logic, Elastic, OpenTelemetry collectors, Prometheus |
| Event intelligence and incident management | BigPanda, Dell AIOps Incident Management (formerly Moogsoft), Splunk ITSI |
| Deployment and configuration | Chef, Puppet, Ansible, Terraform |
| ITSM | ServiceNow, Jira Service Management, BMC Helix |
Product names and categories as of August 2026. Vendor portfolios change; verify current naming and packaging with each vendor.
For each tool, note three things: what it detects that nothing else does, what it duplicates, and whether its data can leave it. The third is the one that surprises people.
Decide how you will know it worked
Agree the measures before the evaluation, not after. A program without a baseline cannot demonstrate a result, and AIOps programs are unusually prone to being judged on the one number that is easiest to move.
Operational measures
- Mean time to detect, acknowledge and resolve — tracked separately, because they improve at different rates and for different reasons
- Ticket volume by severity, not in total
- Repeat incidents per month
- Customers affected per incident
- No-fault-found tickets
- On-site technical visits and truck rolls avoided
Business measures
- Customer support contact volume
- Net Promoter Score and churn in affected segments
- Headcount growth avoided as the estate grows
- Licensing eliminated through tool consolidation
- Incidents resolved before customer impact — the number the program was actually funded for
A caution on alert reduction. It is the easiest metric to move and the least meaningful on its own. A platform can suppress ninety percent of alerts and make your operation worse. Measure it if you like, but never alone.
Run a proof of concept that tells you something
Three things separate a useful PoC from an expensive demonstration.
Use your own historical incidents. Select five or six real incidents from the past year — including one nobody diagnosed quickly, and one that turned out to be change-induced — and ask each vendor to show how their platform would have handled them. Vendor-supplied scenarios show the product at its best. Yours show it at yours.
Include a domain you have not instrumented well. The value of direct ingestion and automated discovery is only visible where existing tooling is weakest. A PoC confined to your best-monitored domain measures your monitoring, not the platform.
Have the operations team evaluate it, not only the architects. A platform that is architecturally sound but not trusted by the people on shift at three in the morning will not change outcomes. Ask engineers whether they would act on what it tells them.
Before you start, write down: where the PoC runs, which data sources are in scope, what outputs you expect to see, who participates from both sides, and the timeframe. Ambiguity on any of these is how a four-week PoC becomes a four-month one.
Common ways this goes wrong
- Treating AIOps as a monitoring replacement. It sits across existing tooling. Programs framed as rip-and-replace stall on migration instead of delivering value.
- Comparing across different scopes. Scoring an event correlation product and a full service assurance platform on one feature grid produces a misleading result in both directions. Establish scope first, then compare within it.
- Automating remediation too early. Trust is earned. Start with recommendations, measure how often they are right, then automate the classes where accuracy is proven.
- Under-resourcing knowledge capture. Platforms that accumulate environment-specific knowledge need it fed in early. Skip that and you are running a general model, and will conclude the platform is unremarkable.
- Letting the shortlist come only from analyst lists. Analyst coverage is a useful filter, not an evaluation. The criteria above are what tell you whether a platform fits your operation.
Where VIA AIOps fits
Vitria develops VIA AIOps, a knowledge-driven AIOps platform built for telecom and service-provider environments. It is designed against the criteria above: fault, performance and change management in one platform; ingestion both from existing monitoring tools and directly from source systems; automated topology discovery; cross-domain correlation; knowledge-based root cause analysis with explained reasoning; and Likely Fix recommendations with agentic remediation inside configurable guardrails.
Vitria has been named a Sample Vendor for Event Intelligence Solutions in eight 2026 Gartner Hype Cycle reports and is included in the 2026 ISG Buyers Guide for AIOps Platforms.
It is one of several platforms worth evaluating. The criteria in this guide apply regardless of which vendors make your shortlist, and we would rather you evaluate rigorously than take our word for it.
Next step
Work through the criteria against your own operation, then bring the results to a conversation rather than a demo. Talk to a VIA AIOps expert — or if you want the vendor-by-vendor view first, see our platform comparisons.
Frequently Asked Questions and Answers
What should you look for when evaluating an AIOps platform?
Nine criteria matter most: scope of assurance — whether fault, performance and change are covered in one platform; ingestion from your existing monitoring estate; direct telemetry ingestion rather than dependence on upstream alerts alone; automated topology discovery; genuine cross-domain correlation; explainable root cause analysis; remediation capability with clear guardrails; production deployments at comparable scale; and realistic, evidenced time to production.
What is the difference between AIOps and observability?
Observability platforms provide visibility — metrics, logs and traces that let an engineer investigate. AIOps adds interpretation and action: correlating signals into incidents, determining cause, and recommending or executing remediation. Observability tells you what is happening; AIOps tells you why, and increasingly does something about it.
How long should an AIOps evaluation take?
A focused proof of concept runs four to six weeks when the scope, data sources, participants and success measures are agreed in writing beforehand. Evaluations that run longer usually do so because those were left open, not because the technology needed more time.
Which metrics prove an AIOps program worked?
Track mean time to detect, acknowledge and resolve separately; ticket volume by severity; repeat incidents per month; customers affected per incident; and incidents resolved before customer impact. Be wary of judging the program on alert reduction alone — it is the easiest number to move and the least meaningful in isolation.
Do you have to replace existing monitoring tools to adopt AIOps?
No, and any evaluation that assumes so is not realistic. AIOps sits across an existing monitoring estate and consumes what those tools emit. Programs framed as rip-and-replace tend to stall on migration rather than deliver value. What varies between platforms is how much work each integration takes and whether the platform can also ingest telemetry directly.