If you’ve been in ops long enough, you’ve seen the story of incident management cycles play out with decades old challenges of incident management. A critical service fails, someone from the team gets the alert, your monitoring dashboards scream red, and your slack messages start to blow up.

Half of the team jumps in and everyone’s trying to find what’s breaking without any context with the alert. Eventually, the root cause gets scribbled into a ticket and the whole thing is just memory-holed until this happens again.

If you’re still managing incidents like this in 2025, it is high time you tackle these challenges of incident management with better tooling, and a culture that fosters reliability than just panicking your teams and patch fixing the issues. 

This blog dives into what’s actually broken in the incident management process today and what needs fixing. After talking to more than 1000+ teams using Zenduty, we’ve collated all the issues they were facing across people, process, rolling, and org structure and how high-performance teams have closed this gap.

What are some of the challenges of incident management?

Most teams are dealing with at least five or six different problems at a time and it is rooted in how teams coordinate, how systems are monitored, how P0,P1,P2…. are defined and how post-incident learnings are retained. 

Despite the rise of AI in 2025, incidents are still rising while teams are sitting in their think tank to understand how to implement AI-enabled incident management. Don’t worry, here’s a guide we’ve written to help you imagine how it all can work out.

In the sections below, we’ll break down the biggest failure points that consistently slow down incident response. Let’s dive in further.

The Volume Problem

Incidents were supposed to be exceptions but that’s not the case anymore. Incidents have been on the rise with the recent ones including Google’s GCP incident, and even a company like Sony facing frequent outages; something or the other is broken. 

Systems today are made of services talking to other services, often across clouds, APIs, and vendor integrations. A single change is enough to ripple through the fragile dependencies that aren’t properly documented.

The reaction in most orgs is to throw more monitoring, dashboards, tools, and hence, more alerts. Too many alerts result in alert fatigue and it would eventually turn into a cultural problem again. If the number of incidents is going up and your team is reacting the same way it did three years ago, it’s time to rethink the whole pipeline.

Alert Noise | The Hidden MTTR Tax

“You can’t fix what you can’t see, but seeing everything doesn’t help either.”

Teams are buried under alert fatigue and it’s because their monitoring and alerting tools fire off every threshold breach, anomaly and this results in alerts that aren’t actionable.

When every other alert is just noise, your engineers would start muting, snoozing these alerts and when something urgent fires off, nothing feels urgent anymore to them. High-functioning orgs don’t focus on getting more alerts, but cutting down on them and filtering them to surface the 5% of events that really require your attention.

To bring in such a culture, you need to start merging signals, context, impact radius, service ownership, and relevant playbooks. That’s how you lower MTTR without even touching the remediation logic. Let’s jump to ensuring how to get the right people in the room when the 5% of events happen.

Team Engagement is the Real Bottleneck

In most orgs, it is reported that they take 30 minutes to an hour just to diagnose and identify the right teams to route the alert to. This is where most of the MTTR gets eaten alive and affects your SLAs. You assume that the incident is in progress but it is just sitting in the queue, waiting for a human to route it. 

The diversity of the teams needed to resolve modern incidents make this even worse. Imagine a scenario with a database outage that may require infra teams, SREs and a platform team. Or a payments degradation would pull in the backend, observability, and third-party vendors into play. The more the number of teams involved, the slower incidents move in real time. 

Until you solve team engagement, every other improvement is just fighting uphill because nothing slows down a resolution like waiting for people to show up. Let’s understand what happens when you have the right people but even they’re flying blind without any context to even start triaging. 

You Can’t Fix What You Can’t See

Once everyone’s in the incident channel, the first question is usually the same.

Where is it failing?

More often than not, no one really knows what’s breaking with teams looking at slices of the stack. CPU on one node, latency on one endpoint, and logs from one container but they can’t see how it all connects. 

Engineers need to know the context when a service fails, they need to know what depends on it, what else is failing because of it and which users are feeling the impact else it’s just poking around dashboards hoping to find a correlation.

Teams with modern stack are switching to service dependency mapping to close this gap so that engineers can understand what each system touches and who owns each part of it. Without a connected view of your system health, you’re just guessing without triaging effectively. 

Let’s jump to the next section and talk about understanding the missing business context.

Context Is Missing From Response

A service goes down. Teams jump in. But no one knows what part of the business is taking the hit.

That’s still the norm. Most orgs don’t map technical components to customer impact. They define incidents by what broke, not what it broke. That’s why response efforts often waste time chasing logs without knowing if users are even affected.

A spike in error rates might not mean much or it might be blocking payments for your top customers. Without that context, teams either overreact or miss the real issue.

The fix is simple. Alerts and dashboards need to show business impact. Is it affecting revenue? Enterprise users? SLAs? If the answer isn’t obvious, your team is guessing.

Postmortems Are Skipped

Once the incident’s resolved, everyone moves on. No write-up. No timeline. No follow-ups.

That’s the default for most teams. It’s because they’re already buried under planned work and the next fire. But skipping postmortems means skipping the chance to actually fix what caused the incident.

Closure and post-incident reporting is the most ignored phase of the process. Even when teams do it, it’s often rushed or just a copy-paste from the ticket.

Good teams automate this. The incident timeline, who joined, what actions were taken, what alerts fired, it’s all there. With GenAI or even basic automation, you can generate a usable draft postmortem in minutes.

And with ZenAI, here's how you can write it in seconds:

Skip it, and you’ll keep solving the same problem again next quarter.

Process Isn’t the Problem, Consistency Is

Most orgs say they have incident processes including detection, triage, resolution, postmortem in a doc somewhere.

Some teams report having well-documented processes, only a few say they’re actually effective. The rest admit their workflows either “meet needs” or “need improvement.” That’s a red flag.

Having a playbook doesn’t mean people follow it. And even when they do, if it’s not updated, aligned across teams, or integrated with tools, it won’t deliver results.

The teams that consistently respond well aren’t relying on process documents. They bake processes into their tools, automate handoffs, and run the same play the same way.

Automation Is Half-Done

Everyone says they’ve automated incident response. Most haven’t. What they’ve done is automate fragments including routing alerts, restarting services, tagging tickets, etc.

That’s not bad, but it doesn’t move the needle on MTTR if the rest of the flow is still manual. Most orgs still rely on humans to engage teams, chase context, and write the postmortem. That’s where the time gets lost.

Orgs that treat automation as a strategic, full-stack investment outperform across every metric. Fewer alerts come from users, MTTR drops, and post-incident learning actually happens.

If your automation starts and ends with "restart the pod," you’re not using it right.

ZenAI: Bridging the Gaps Left by Automation

While automation has streamlined many aspects of incident response, critical gaps remain, particularly in areas requiring rapid context synthesis and decision-making. Tasks like triaging complex incidents, identifying root causes, and generating comprehensive postmortems often still rely heavily on manual effort.

ZenAI addresses these challenges by providing AI-driven tools that enhance each stage of the incident lifecycle:

• Instant Incident Summaries: ZenAI delivers concise, actionable summaries within Slack, offering stakeholders immediate insights into customer impact and operational status.
• Contextual Root Cause Analysis: By analyzing complex payloads, ZenAI helps teams quickly identify affected services, clusters, and components, facilitating faster root cause identification.
• Automated Postmortem Reports: ZenAI generates detailed post-incident reports by aggregating logs, metrics, and relevant communications, reducing the manual burden on engineers.
• Optimized On-Call Scheduling: Through conversational interactions, ZenAI assists in creating efficient on-call schedules, ensuring seamless shift management across distributed teams.

Organizations leveraging ZenAI have reported significant improvements, including up to 50% faster Mean Time to Resolution (MTTR) and increased developer productivity.

To explore how ZenAI can enhance your incident management processes, visit Zenduty’s AI Incident Management page.

Wrapping Up

We’ve seen it firsthand across hundreds of teams: too much noise, slow engagement, no visibility, skipped postmortems, and siloed ownership. The good news is every one of these can be fixed.

Start by cutting alert noise and automating the boring parts. Use ZenAI to fill the gaps where human context and speed matter most. Build incident workflows around services, not stacks. And most importantly, build a culture that values reliability over reaction.

The teams that do this are already seeing lower MTTR, fewer repeat incidents, and happier on-call engineers.

If you’re serious about fixing incident management in your org, now’s the time.