It’s not just harder to deal with incidents in modern hybrid IT setups; it’s a whole new problem.
When you have on-prem systems, multiple clouds, edge services, and everything else, things break in more places and for more reasons.
During my time working with AIOps and observability, I’ve seen teams get buried in alerts from dozens of tools, spending more time chasing symptoms than fixing the real problems.
How AI and machine learning are used in real life is changing right now
AI-powered platforms can do more than just point out that something is wrong.
They can link logs, metrics, and traces, highlight critical information, and identify patterns that indicate impending failures.
I’ve seen this change cut down on alert noise a lot, speed up the time it takes to fix problems, and, just as importantly, help IT teams explain how their work affects the business in terms that matter to them.
Andrew Lovell
What incident response looks like in hybrid IT
Yes, hybrid IT environments have made things bigger, but more importantly, they’ve made things more chaotic. Many teams aren’t looking at one clean system anymore.
They have to deal with micro-services running in AWS, old workloads sitting in on-prem data centers, third-party SaaS tools, and edge devices that are all over the place. The end result is that monitoring and visibility data are spread out across different tools and dashboards.
It was only a matter of time before alert fatigue became common. There were times when global banks got millions of events every month, and there was no easy way to tell which ones were important.
Teams wasted hours sorting through duplicates and chasing false positives, only to find out what the real problem was after customers had already felt the effects.
In setups like this, incidents almost never stayed small
Without meaningful, real-time correlation, serious problems were often only found after users reported an outage. Because of that delay, both detection and resolution times went up, and the business effects were quick: lost sales, unhappy customers, and broken trust.
Finding the real root cause was even harder because hybrid systems are always changing – new deployments, autoscaling, patches, and configuration changes.
At that point, it wasn’t that the teams were doing a terrible job; the old incident response playbooks just weren’t made for the complexity of hybrid-scale systems.
AI and AIOps: Making incident response less painful
This field is where AIOps actually earns its keep. At its core, it’s about using machine learning to take some of the manual grind out of incident response, cutting through noise, finding the real cause faster, and helping teams decide what to do next.
In my work, I’ve seen how AIOps platforms process millions of events from various monitoring tools and analyze them in ways that are simply inaccessible to humans at scale.
They learn to recognize “normal” states across various systems and display only those alerts that merit attention. Royal Bank of Canada decommissioned its legacy event management system and migrated to an AIOps engine.
More importantly, their teams detected issues approximately one-third faster, and recovery time decreased by over 40%. This directly translated into fewer sleepless nights and fewer customer-facing incidents.
A big part of that improvement comes from correlation. Instead of bombarding engineers with ten separate alerts, one from the load balancer, another from the database, and another from the app, the system rolls them into a single incident and often points straight to the underlying cause.
On top of that, anomaly detection helps catch trouble early
I’ve seen models flag slow-burn issues, like a storage queue quietly backing up, well before any hard thresholds were breached. That early warning gives teams time to act while the system is still technically “green”.
What’s also changed is how remediation works. Modern platforms don’t just say something is wrong; they suggest what to do about it. Based on past incidents, the system might recommend restarting a service, scaling a cluster, or applying a known configuration fix.
It’s not fully autonomous ops, but it does feel like having a seasoned engineer whispering the next best move in your ear, only much faster.
AI is also beginning to assist with tasks beyond technical triage.
Generative tools can now summarize incidents, draft postmortems, and update knowledge bases using data pulled straight from tickets and logs.
Industry data backs these findings up: teams that’ve embedded generative AI into their ITSM workflows are seeing noticeable reductions in resolution time, and the most mature adopters are cutting that time in half.
From what I’ve seen, the real win is freeing engineers from repetitive admin work so they can focus on higher-value problems.
Of course, none of this occurs magically.
AI doesn’t fix broken processes.
When those pieces come together, incident response stops being constant firefighting and starts looking a lot more predictive and preventative.
What actually helps during incidents?
Cutting through alert noise
One of the biggest wins in incident response is simply seeing less junk. In real environments, I’ve enabled correlation features and watched massive alert storms shrink into a few clear incidents. Instead of hundreds of near-identical warnings, teams get a small set of issues that actually need attention.
The UK Retail Bank is into this exact problem. Once they replaced their old event manager with a smarter correlation layer, the volume of alerts dropped by about half.
That alone changed how the team worked. Analysts stopped bouncing between false positives and started spending their time on real problems. Productivity went up without adding headcount.
Spotting problems early
Good systems learn what “normal” looks like over time. When something drifts away from that baseline, it shows up before users feel it. I’ve seen this catch slow memory leaks, creeping storage backlogs, and backup jobs that quietly stopped running weeks earlier.
In healthcare and finance, those kinds of silent failures are dangerous. Bon Secours Mercy Health system improved visibility across its communications platform and stopped waiting for doctors or nurses to report outages.
The result was a 30% drop in critical, patient-impacting incidents and noticeably faster recovery when things did go wrong. That change alone reduced stress across both IT and clinical teams.
Faster triage, fewer manual fixes
Many incidents follow the same patterns. Once those patterns are clear, the response doesn’t always need a human at the keyboard.
In several environments I’ve worked in, routine fixes, restarting stuck services, clearing caches, and re-syncing jobs – were handled automatically.
A well-documented example comes from Vodafone, one of the world’s largest telecommunications providers operating across hybrid cloud and legacy infrastructure environments.
The company was managing extremely high event volumes across distributed network infrastructure and enterprise IT systems. Manual triage was consuming engineering time, and repetitive incidents were slowing response.
Vodafone implemented ServiceNow IT Operations Management (ITOM), including event management and automated remediation workflows integrated into its hybrid infrastructure stack.
Systems that improve over time
Unlike static rules, effective detection setups get better as the environment changes. New services come online, usage patterns shift, and seasonal traffic spikes become normal instead of alarming.
Improvements have been demonstrated clearly, with multinational retailers focused on stores and warehouses seeing rapid gains. Over just a few months, their recovery times dropped dramatically as the system adapted to new data and automated more fixes.
Store systems reached near-perfect availability because problems were handled before opening hours. That kind of reliability shows up directly in revenue and customer trust.
Andrew Lovell
How does this looks in real-life?
The examples below are taken from publicly available case studies and data; they are used for illustrative purposes only and do not constitute an endorsement of any specific company or platform.
1. Chipotle Mexican Grill: BigPanda AIOps cuts MTTR in half
Context: Chipotle, a U.S. restaurant chain with thousands of locations and a rapidly growing digital ordering platform, was getting too many alerts on its hybrid infrastructure (multiple monitoring systems like Datadog, CloudWatch, and AppDynamics).
IT teams were taking hours to manually sort through alerts, which slowed down the response to incidents.
AI Solution: Chipotle used the BigPanda AIOps platform to bring all of their alerts together, turn them into meaningful incidents, add more context, and automatically create tickets for triage.
Results: AIOps-driven correlation and context enrichment cut the MTTR in half, which means that the time it takes to fix a problem is cut in half. Also, automated ticket generation with full context made it easier to send alerts to the right teams, getting rid of the need to manually thread them.
This case is one of the best examples of how hybrid IT incident response automation can cut MTTR in a high-volume production environment.
2. KFin Technologies: 90% reduction in resolution time with AIOps
Context: KFin Technologies, a data-intensive financial services operations company in India, needed to enhance incident responses for its critical database and application services across hybrid environments.
AI Solution: Deploying an AIOps-capable monitoring solution (Manage Engine Applications Manager with smart alerting) equipped with AI-driven correlation and prioritization.
Results: 90% reduction in Mean Time to Resolution (MTTR) after using intelligent alerts and actionable insights. This resulted in a significant decrease in Severity-1 incidents, allowing IT teams to concentrate on genuine issues instead of mere noise.
This example shows how AI works in a highly transactional, regulated financial services environment to deliver drastic improvements in incident response performance.
3. Cisco: 60% alert reduction and 40% faster resolution
Cisco’s massive global networking infrastructure was also creating enormous numbers of alerts. These came from all over the place, including their own network equipment, cloud systems, and services that were spread out across different places.
AI solution: The business added Moogsoft AIOps to their ITSM tools and linked them up. This lets them use machine learning to automatically link alerts and find patterns.
Results: They cut their total number of incidents by 60% just by getting rid of duplicate alerts and noise that wasn’t important. Following that, the average time to resolution went down by about 40% because teams could finally focus on what really mattered.
This is a vendor-documented implementation that actually happened, with real numbers showing how well AIOps can work when you’re dealing with hybrid infrastructure.
4. Tecsys: Datadog AIOps cuts down on alert incidents by 69%
Context: Tecsys is a global supply-chain software company that had a big problem with too many alerts. They had a lot of extra tickets and noise because they were using a lot of monitoring tools on their cloud and hybrid systems.
AI solution: They used Datadog’s event management platform, which uses AI to connect alerts and group multiple notifications into single, meaningful events.
Results: Alerts went down by 69%, which meant that SRE teams could finally work on real problems instead of being buried in alerts. It was much easier to find and respond to real incidents quickly with the cleaner, correlated alerts.
This one really shows how AI can change how alerts are sorted, and when you do that, incidents get solved faster.
Important things for business leaders to remember
One thing I’ve learnt from working with AI in incident response is that tools alone won’t save you. The companies that really benefit from AI are the ones that see it as a tool to use, not a product to install.
That usually means getting serious about observability by putting all the telemetry together, cleaning up the metrics, and making sure that logs and traces tell a clear story.
Without that base, AI just makes things more confusing more quickly. I also tell leaders to be very strict about how they set priorities.
Don’t try to make everything work automatically at once.
When teams see that things are getting better faster and that there are fewer late-night problems in those important areas, they naturally want to help.
How people use these systems is just as important
AI works best when teams trust it, know what it can and can’t do, and know when to step in.
That means spending time updating runbooks, writing down tribal knowledge, and teaching engineers how to understand AI suggestions instead of just following them.
AI takes care of correlation, early detection, and routine fixes in the best teams I’ve worked with. People make decisions and make systemic improvements. Teams also change how they review incidents.
Instead of asking, “Who missed the alert?” they start asking, “Why didn’t the system catch this sooner, and how can we teach it to do so next time?” That change in thinking is what really gives you long-term strength.
Final thoughts
It’s not getting easier to use hybrid IT.
If anything, things are changing faster; there are more services, more dependencies and more things that can go wrong without anyone noticing.
What has changed is how well we can handle that complexity. AI-driven incident response doesn’t stop failures from happening, but it changes the way organizations deal with them.
- Incidents happen less often, are shorter, and are less obvious to customers.
- Engineers spend less time fixing problems and more time making the system better. You stop chasing reliability and start designing for it over time.
- From what I’ve seen, companies that use AI to respond to incidents successfully don’t do it just to be independent. They want things to be clear, quick, and sure.
- Trust that the right problems will come up, teams will have the information they need to address it and set up the right set of processes, governing committees, and guardrails to enable that.
The vision that issues can be fixed before they cause outages: that’s the real deal here. Not systems that heal themselves overnight, but operations that are calmer, decisions that are better, and systems that learn from their mistakes.
