The Challenge
TotalEnergies operates large on-premise infrastructure environments supporting critical applications and business services across thousands of servers.
Like most large enterprise environments, these platforms were already heavily monitored. Operations teams had access to large volumes of telemetry, alerts and operational data.
The challenge was not collecting more data.
It was identifying which combination of signals indicated that an infrastructure component was actually moving toward failure.
Traditional monitoring is particularly effective once a known threshold has been crossed. CPU utilization becomes too high, storage approaches capacity, latency increases or a service stops responding.
But by that point, the degradation may already be affecting production.
For operations teams managing thousands of servers, manually identifying the weak signals that precede those incidents becomes increasingly difficult.
The objective was therefore to move beyond monitoring infrastructure health and answer a more useful operational question:
Is this infrastructure likely to fail, and should the operations team intervene now?
Moving from Reactive Monitoring to Predictive Operations
The project introduced an AIOps capability on top of the existing operational environment.
Rather than replacing the monitoring systems already in place, the platform combined multiple types of operational data and used machine learning to identify patterns associated with infrastructure degradation and failure.
The precise internal data sources and models remain confidential, but the principle was straightforward.
Different infrastructure signals were evaluated together rather than as isolated alerts.
The AIOps system continuously assessed the state of the monitored infrastructure and estimated whether a failure was likely to occur.
When the detected behavior indicated sufficient risk, an alert was generated so that the operations team could investigate before the infrastructure actually failed.
This changed the operational sequence.
Instead of:
Failure → Alert → Investigation → Remediation
the objective became:
Early signals → Failure risk detected → Alert → Investigation → Preventive action
Not every infrastructure failure can be predicted, and a predictive model is never perfect. The goal was therefore not to promise autonomous or failure-free infrastructure.
It was to give operations teams an additional window in which they could act.
Running Real AIOps in Production
A major part of the challenge was taking AIOps beyond experimentation.
Predictive infrastructure projects can perform well in a laboratory while providing little operational value once exposed to real production environments.
For TotalEnergies, the system had to operate continuously across infrastructure representing thousands of servers and generate alerts that operations teams could realistically use.
That meant balancing two competing risks.
A model that is too conservative misses potential incidents.
A model that is too sensitive overwhelms operations teams with alerts and eventually becomes another source of monitoring noise.
The production system reached an approximate 30% false-positive rate.
In other words, the system was deliberately designed around a real operational trade-off: accepting a manageable level of unnecessary investigation in exchange for earlier warning of infrastructure conditions associated with potential failures.
The important measure was not whether every prediction was correct.
It was whether acting on those predictions reduced the number of incidents that ultimately reached production.
Helping Teams Investigate Before Failure
Prediction alone was not enough.
An alert saying that an infrastructure component may fail is only useful if an operations team can investigate it in time.
The AIOps approach therefore helped surface abnormal infrastructure behavior earlier in the incident lifecycle.
This gave engineers the opportunity to validate what the system had detected, examine the surrounding operational context and decide whether preventive action was required.
For some situations, this meant addressing a degradation before it became a production incident.
For others, the early warning gave operations teams additional context before the failure occurred, reducing the time spent discovering where to begin the investigation.
AIOps therefore complemented existing monitoring rather than replacing it.
Monitoring continued to provide visibility into infrastructure health.
AIOps added a predictive layer designed to identify when the combined behavior of the infrastructure suggested that something was moving in the wrong direction.
Reducing Infrastructure Incidents by Approximately 60%
Once deployed in production, the AIOps capability contributed to an approximately 60% reduction in infrastructure incidents across the monitored platforms.
The result came from changing when operations teams could intervene.
Instead of depending exclusively on alerts generated after known conditions had been breached, teams could investigate selected infrastructure risks while the underlying systems were still running.
The project also demonstrated an important distinction between AIOps and conventional alert automation.
The objective was not simply to create more sophisticated rules.
It was to use patterns across heterogeneous operational data to estimate the likelihood of infrastructure failure and turn that prediction into an actionable signal for operations teams.
At the scale of thousands of servers, that capability allowed the organization to focus human attention on infrastructure showing meaningful signs of degradation rather than expecting engineers to manually correlate every available signal.
The Outcome
The production deployment delivered a different operational model for the monitored on-premise infrastructure:
- Thousands of servers covered by the AIOps capability
- ~60% reduction in infrastructure incidents
- Predictive alerts generated before selected infrastructure failures
- ~30% false-positive rate in production
- Earlier investigation and preventive intervention by operations teams
- Existing monitoring retained and augmented rather than replaced
The project showed that the value of AIOps is not simply adding artificial intelligence to monitoring.
It is creating enough reliable predictive context for operations teams to act before an infrastructure problem becomes a production incident.
For TotalEnergies, that meant moving part of infrastructure operations from reactive incident response toward a more predictive operating model — at enterprise scale and in production.