AIOps Consulting — Intelligent Automation for Enterprise IT Operations

The Challenge: Alert Fatigue and Reactive Operations

Most enterprise IT operations teams are drowning in alerts, not insights. Monitoring tools like Dynatrace and Splunk generate thousands of signals a day, but without intelligent correlation and automation layered on top, operations teams spend more time triaging noise than resolving real problems.

The result is familiar to any Head of IT Operations or Service Delivery Director: rising MTTR, escalation fatigue, inconsistent on-call experiences, and SLA risk that creeps up quietly until it becomes a customer-facing incident. Traditional AIOps tooling helps, but only when it's implemented with real operational context — not bolted on as a generic dashboard.

There's also an organizational dimension that's easy to underestimate. AIOps platforms are often purchased by a tooling or architecture team and handed to an operations team that wasn't part of the design — so the automation reflects what the platform can technically do, not how incidents actually get escalated, who needs to be informed, or what 'resolved' really means for that business. Closing that gap is usually the difference between an AIOps rollout that gets adopted and one that quietly gets ignored after the first quarter.

How SD Advisory Implements AIOps

SD Advisory works from the operations side first, not the tooling side. Our principal consultant has owned P1/P0 escalations directly and led multi-region operations teams of 50+ resources, so implementations are designed around how incidents actually move through an organization — detection, correlation, triage, escalation, and resolution — rather than treating automation as a bolt-on dashboard layer.

In practice, this means integrating observability platforms like Dynatrace and Splunk with AI-driven triage and escalation workflows, so anomalies are correlated and prioritized automatically before they reach a human responder. Where it adds value, I extend this further with GenAI — for example, building an AI Incident Co-Pilot that assists operations teams with real-time incident analysis, resolution guidance, and auto-drafted stakeholder communications, reducing the manual overhead around every incident, not just the diagnosis.

Throughout, the goal is augmentation rather than replacement. AIOps and AI Incident Co-Pilots are designed to sit alongside your existing on-call rotation and escalation matrix, surfacing the right context faster rather than asking teams to trust a black box. That's also why the engagement always starts with how your team currently triages an incident — not with which platform to buy.

Tools & Technologies

Engagement Process

1. Operational Discovery

Map how incidents currently move through your organization — detection sources, correlation gaps, escalation paths, and where time is actually being lost between alert and resolution.

2. Observability Integration

Connect or tune Dynatrace, Splunk, or your existing monitoring stack so signals are correlated and prioritized before they reach a human, reducing alert volume and noise.

3. AI-Assisted Triage & Escalation

Layer in AI-driven workflows — and, where it fits, an AI Incident Co-Pilot — for real-time incident analysis, resolution guidance, and automated stakeholder communication.

4. Measure & Iterate

Track MTTR, SLA adherence, and escalation accuracy against your baseline, and tune correlation rules and automation thresholds as real incident data comes in.

Built For Enterprise Decision-Makers

Operational outcomes

Through AI-driven operational monitoring and workflow automation, I've helped improve SLA adherence from 96% to 99.8% on an enterprise IT operations engagement, while AI-enabled triage and escalation workflows integrated with Dynatrace and Splunk reduced incident resolution time by 20%. The 20% MTTR figure is a benchmark from the AI Incident Co-Pilot prototype; the SLA and availability figures come from enterprise delivery engagements.

Why This Works

Most AIOps engagements fail not because the technology doesn't work, but because the team implementing it doesn't understand both the platform and the operational reality it's meant to serve. Two decades of owning P1/P0 escalations directly — not just consulting on them — is what closes that gap, and it's why the automation that comes out of this process tends to get adopted rather than quietly ignored after the first quarter.

Frequently Asked Questions