What should telecom operators look for in an AIOps platform?

Telecom operators evaluating AIOps platforms face a market where most products were designed for enterprise IT operations and adapted for networks afterwards. The requirements differ enough that the distinction matters: network operations involve multi-vendor, multi-domain environments with element counts in the tens of thousands, topology that changes continuously, and incidents whose impact is measured in affected subscribers rather than affected users. This guide sets out nine criteria that separate platforms built for that context from those adapted to it, and how to test each one during evaluation.

Why telecom AIOps differs from IT AIOps

Four differences drive most of the requirements gap.

• Scale of a different order. A large operator generates petabytes of operational telemetry daily across transport, core, access, cloud infrastructure and applications. Approaches that work at enterprise volumes may not survive it.
• Continuous topological change. Network builds, cloud-native functions scaling elastically, ongoing plant work. A service model accurate at deployment is inaccurate within weeks unless it is rediscovered rather than maintained.
• Multi-vendor and multi-domain by default. No operator runs a single-vendor network. Correlation must span domains and vendors that describe the same conditions differently.
• Subscriber impact as the unit of severity. Severity is a function of how many subscribers are affected and how — which requires understanding the path from an element to a service to a customer, not just that an element is down.

AIOps, observability and service assurance are not the same category

Three categories are routinely compared as though they were alternatives, and a shortlist built without separating them will compare products that do different jobs.

Observability provides visibility. Metrics, logs and traces are collected and made explorable so that an engineer can investigate. The engineer supplies the interpretation.

Event intelligence consumes alerts from monitoring tools and reduces them — deduplicating, grouping and correlating a high-volume alert stream into a smaller set of actionable incidents. It reasons over what other tools have already decided is worth reporting.

Service assurance covers fault, performance and change management across the service path, from network element to subscriber experience. It requires the underlying data rather than another tool’s verdicts about it, because performance is a statement about the shape of data over time.

All three are legitimate, and an operator may end up buying more than one. The mistake is to evaluate them against a single scorecard and conclude that the cheapest wins. Establish which category a product occupies before comparing capabilities, because that determines what remains your integration project.

The nine criteria

1. Scope of assurance

What to look for: whether the platform covers fault, performance and change management, or handles events and incidents with performance left to a separate product.

Why it matters: this is the first question, because it determines how many of the others matter. In a network, most subscriber-affecting degradation does not begin as a fault. It begins as a slow performance change, frequently after a change window, and only becomes a fault once a threshold is finally crossed. Fault management can be performed from alerts, because an alert is a report that something already crossed a line. Performance management cannot, because a baseline, a trend and a gradual degradation are statements about the shape of data over time, and they 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 systems you already run — element and network management systems, probe and assurance systems, cloud monitoring — and how much work each integration takes.

Why it matters: no operator replaces a decade of accumulated domain monitoring in order to adopt an AIOps platform, and any evaluation implying otherwise is not realistic. This is table stakes. But in a multi-vendor network you are integrating more sources than an enterprise IT buyer, and the difference between an off-the-shelf connector and a services engagement is measured in months.

How to test it: name your five most important monitoring and element management systems, and ask what each integration requires, who does the work, and how long it took at the three most recent comparable operators.

3. Direct telemetry ingestion

What to look for: whether the platform also ingests metrics, events, logs and traces directly from network elements and infrastructure, or depends entirely on upstream monitoring 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 that cause the worst outages — are precisely those nobody configured a threshold for. Direct ingestion is also the prerequisite for performance management, which is why it follows criterion 1.

How to test it: ask the vendor to ingest raw telemetry from a domain you have not instrumented with alerting, and see whether the platform surfaces anything meaningful.

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. In a network under continuous build and elastic scaling, a model that is accurate at deployment is inaccurate within weeks. A drifted model degrades silently — the model says one thing, the network 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 manual intervention.

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. In a multi-vendor network the problem is harder still, because different vendors describe the same condition in different terms.

How to test it: present a historical incident that crossed domains and vendor boundaries, 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 operations 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. This criterion has become more important, not less, as more of the reasoning moves into models.

How to test it: ask an engineer, not an evaluator, to 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 took longer than planned, and why.

How to run a proof of concept

Three things make the difference between a useful PoC and an expensive demonstration.

Use your own historical incidents. Select five or six real incidents from the past year — ideally including one nobody diagnosed quickly — 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 only becomes 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 architecturally sound but not trusted by the people on shift at 3am will not change outcomes. Ask engineers whether they would act on what it tells them.

Common deployment pitfalls

• Treating it as a monitoring replacement. AIOps sits across existing tooling. programs framed as rip-and-replace stall on migration rather than delivering value.
• Automating remediation too early. Trust is earned. Start with recommendation, measure accuracy, then automate the classes where accuracy is proven.
• Under-resourcing knowledge capture. Platforms that accumulate environment-specific knowledge need that knowledge fed in early. Skipping it means running a general model and concluding the platform is unremarkable.
• Measuring alert reduction alone. Alert volume is easy to measure and easy to improve. Mean time to resolution and incidents resolved before customer impact are what the program was funded for.

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: direct MELT ingestion, automated topology discovery, cross-domain correlation, knowledge-based root cause analysis with explained reasoning, Likely Fix recommendations and agentic remediation, with fault, performance and change management in one platform. Production deployments include the largest US ISP processing petabytes daily, a Global 100 CSP serving 30 million subscribers, and a Fortune 200 mobile operator’s greenfield 5G network — typically reaching production in 90 to 120 days.

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.