The key to the evolution of monitoring lies in shifting from detecting incidents to anticipating them.
Traditional Monitoring: A Point-in-Time Snapshot, Not a Continuous View
For years, SAP system monitoring has relied primarily on the expertise of consultants and periodic review activities. Traditional monitoring involves reviewing alerts, running monitoring transactions (ST22, SM21, DBACOCKPIT, among others), validating logs, checking memory usage, verifying jobs, analyzing database growth, reviewing queues, and validating availability.
All of this provides a snapshot of the system’s state at that specific moment. The problem is that five minutes later, the situation may have changed; the consultant isn’t monitoring the system 24/7; and some issues occurring in the system can turn into incidents without anyone seeing them coming. According to Gartner, unplanned downtime now costs organizations an average of $5,600 per minute.
The New Rule of the Game: Preventing Incidents Rather Than Resolving Them Quickly
Expectations have changed. Today, organizations need near-100% availability, continuous operation, reduced downtime, continuous coverage built on the combination of human expertise and intelligent monitoring, data-driven decisions, and, above all, the ability to anticipate issues.
It is no longer enough to resolve an incident quickly. The real value lies in preventing incidents from occurring in the first place.
This requires monitoring capable of analyzing historical patterns, correlating system signals, and detecting deviations from normal behavior in real time—in other words, predictive monitoring that identifies trends foreshadowing a failure, such as anomalous table growth or progressively degrading response times, before they become visible problems. Gartner estimates that organizations integrating AIOps capabilities into their monitoring approach can reduce mean time to resolution (MTTR) by up to 50%.
Beyond the Data: The Power of AI to Predict System Behavior
The next evolution is monitoring powered by artificial intelligence. AI doesn’t just observe; it does something more valuable. For example, instead of reporting an isolated data point such as “CPU at 85%,” AI-based predictive monitoring provides context and probability: “The sustained increase in CPU usage, combined with the rise in queued dialog processes and longer response times, indicates a high probability of system saturation over the next two hours.”
That’s a significant shift—from simply reporting usage data to explaining what’s happening and what’s likely to happen. This direction is consistent with the evolution of the underlying platform. SAP has recently enhanced SAP Cloud ALM and SAP Focused Run with machine-learning-based System Anomaly Prediction, shifting from static thresholds to dynamic, self-learning thresholds.
AI can correlate many variables simultaneously, such as CPU utilization, memory usage, log growth, crashes, RFC queues, jobs, connected users, table growth, dumps, and pending updates, among others.
It does not analyze a single metric in isolation but rather examines the system’s overall behavior.
AI suggests, the expert decides: The new model for efficient support
The goal isn't to replace human talent — it's to give it a partner: a tireless colleague that watches over the system around the clock, flags what matters, and explains why, so the expert can lead with more context, not less. AI is an additional layer of intelligence, not a replacement for the Basis consultant’s expert judgment; while AI continuously monitors, analyzes trends, identifies patterns, and suggests actions, the human team decides and executes, and the consultant validates, interprets, and makes decisions. Technology accelerates operational visibility and frees up time for strategic thinking, but it does not replace judgment, accountability, or the final decision — those remain firmly with the expert, working alongside AI rather than being replaced by it.
The combination of these two capabilities results in better service.
Looking ahead, the next level of maturity will be the automation of low-risk actions: restarting services, cleaning up files, freeing up space, running scripts, redistributing processes, or automatically generating tickets—always in accordance with clear governance policies and architectural guardrails, because automation without transparency scales risk faster than value.
This level still requires appropriate rules, validations, and controls before any action is automated and trusted to run on its own.
Evolution, Not Just Support: Avvale’s Commitment to the Future of Your Operations
It’s not just about incorporating AI; it’s about evolving monitoring toward a model where attention is continuous, analysis is intelligent, alerts are contextual, actions are recommended, risks are identified before an incident occurs, and consultants spend more time creating value than manually reviewing metrics.
At Avvale, we don’t just provide support: we evolve our service alongside technology, so our clients have the tools to anticipate and prevent incidents rather than react to them. This is how we put this philosophy into practice in Application Managed Services: AI extends what our teams can see, and our experts remain in control of what happens next.
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