From reactive to predictive: Problem Management with AIOps + JSM

IT organizations are spending more time firefighting than anything else. Most incidents are reactive, with service desks simply trying to restore services quickly rather than preventing issues from happening in the first place. Although intervention is sometimes most likely required at that moment, the mechanism itself neither works to lessen nor prevent them from reoccurring.

Problem Management was designed to fill that gap, but it still is underutilized in many organizations due to lack of visibility, lack of automation, and too much of manual effort. With the advent of AIOps (Artificial Intelligence for IT Operations) alongside IT Service Management tools such as Jira Service Management (JSM), organizations now have a practical means to actually do predictive problem management instead of merely reacting.

Why Problem Management falls short

Problem Management ensures that repeating incidents are observed, the root cause is identified, and a solution with a fix is made permanent. However, in practice, teams have some difficulties:

Data is scattered among monitoring tools, logs, and service desk tickets.
Root cause analysis is time-consuming and manual.
Problem records are reactive and created only after multiple incidents pile up.
There is no continuous feedback loop between incidents, changes, and problem records.

This results in Problem Management becoming a backlog of unresolved issues rather than a proactive discipline.

How AIOps Adds Value

AIOps is a technique that injects a machine learning layer and analytics into the IT operations data. Instead of manually combing through logs and alerts, AIOps platforms can:

Correlate signals and pattern across thousands of incidents and monitoring.
Spot anomalies at an early stage before they would cause a major-scale incident.
Highlight probable root causes by studying dependencies in between systems.
Predict issues in the future by learning from the historical incident and change data.

Meanwhile, problem candidates are suggested and prioritized automatically before incidents can even really start accumulating.

How JSM supports AIOps

While AIOps gives the intelligence, JSM provides workflow and structure. Insights uncovered through AIOps generate value only once the information has been realized through processes that people currently matter in. JSM lets teams:

Auto-create problem records from correlated incidents identified by AIOps.
Seamlessly link incidents to problems so ongoing disruptions can be associated with work on their root cause.
Connect problems to changes to keep track of whether permanent fixes have already been applied successfully.
Maintain a knowledge base so that lessons learned from previous problems aid progress towards avoiding repetitions.

AIOps + JSM will bridge the gap between raw operational data and actual problem resolution.

The Practical Workflow

The combined AIOps + JSM workflow for predictive problem management looks like this:

Incident monitoring: AIOps ingests logs, alerts, and performance metrics.
Correlation and analysis: Related incidents are grouped, and patterns pointing to deeper issues are flagged.
Problem record creation: JSM automatically generates a problem ticket with enriched data (impacted services, likely root cause, recent changes).
Prioritization: Problems are ranked based on business impact, frequency, and predicted risk.
Root cause validation: Engineers use the enriched context to validate or refine the suggested cause.
Permanent fix: A change request is raised in JSM to implement a long-term resolution.
Feedback loop: Knowledge articles are updated, and AIOps learns from resolution outcomes to improve future predictions.

This workflow ensures that Problem Management is no longer an afterthought but a continuous, data-driven process.

Practical Benefits

By following the path, clear, measurable results are made possible:

Incident cuts: The frequency with which incidents are repeated decreases because of the underlying resolution of recurring issues.
Faster resolution: Problem records, which have been pre-enriched, ensure that responders have all relevant data readily available.
Better prioritization: Instead of responding to alerts, resources focus on actually working on things of consequence.
Improved user experience: End users will enjoy the reduced time of disruption and faster recovery whenever disruption happens.
Knowledge enhancement: Every solved problem adds to the knowledge base and will help to lessen tribal knowledge.

From Reactive to Predictive

Setting aside the reactive firefighting incident for the predictive problem management is not instantaneous. It takes:

AIOps integration with the ITSM.
Beginning in a small way, maybe by automatically creating problem records for the top recurring incidents.
Building up trust in the predictions by validating the results with actual teams.
Continuously feeding learnings back into the knowledge base and automation rules.

Over so time, teams evolve and move farther up the maturity curve-from reacting to incidents, preventing known problems, to predicting issues and avoiding impacts on the business.

Problem Management has long promised stability, yet it has been deemed manual and reactive for most period. Using AIOps with Jira Service Management means that organizations can finally realize the promise of proactive problem-solving. AIOps intelligence surfaces the ‘right’ problems, and JSM structure ensures they will be resolved, tracked, and worked upon.

This practical combination will give back IT teams precious time from firefighting to focus on the delivery of stable, predictable services. This yields, fewer outages, happier users, and the service desk truly driving business value. Take the first step towards smoother and smarter collaboration with Atlassian JSM. Connect with us if you would like to learn more about the benefits of working with CRG Solutions experts.