From managing fragmented systems to maintaining 24/7 uptime at cost, IT teams are facing insurmountable expectations. To meet these demands, businesses are turning to AI copilots to strengthen system resiliency and make more informed business decisions.
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AI Copilots in IT Operations: Key Findings
How AI Copilots Drive Business Value in IT Operations
AI copilots are quickly becoming must-have tools for maintaining stability and efficiency in complex IT environments, shifting operations from reactive, manual ops to proactive and autonomous IT management. In this article, we tackle the role of AI copilots in IT operations.
1. Driving Scalable 24/7 Operations
AI copilots can work 24/7 without fatigue, monitoring systems, and catching issues instantly at any hour.
This always-on vigilance lets organizations support global users and strict uptime SLAs without requiring proportional increases in overnight staff. Additionally, it helps IT teams rapidly resolve issues before they turn into major crises.
As a result, businesses can expect higher uptime and fewer after-hours emergencies, which benefits both the IT team and customers.
2. Correlating Events Across Environments
According to Zylo’s latest SaaS Management Index, businesses use 275 apps on average.
With so many tools, technologies, and systems to manage, it's easy for issues to fall through the cracks.
However, an AI copilot can prevent this scenario altogether. Once integrated into the business IT ecosystem, the AI assistant unifies monitoring across silos. It also aggregates events from diverse tools and uses topology awareness to see relationships.
For example, an AIOps platform can correlate an AWS EC2 CPU alarm with a database error from an on-prem Oracle system if it knows those components are part of one service.
By linking related events, the AI presents operators with a single incident that reflects the full scope of the issue instead of dozens of separate alerts. This cross-domain insight is especially invaluable in hybrid multi-cloud environments where manual correlation is extremely challenging.
ServiceNow: Consolidating Nationwide IT Operations
ServiceNow’s collaboration with Canadian telco company Bell is a succinct demonstration of AI copilot’s capabilities. Its AI platform unified 26 applications and 8,800 data silos, enabling Bell to streamline operations nationwide.
As a result, Bell's team delivers more personalized services to millions of customers 24/7. More importantly, ServiceNow’s AI platform has automated 90% of dispatch-related tasks and saved over $1 million annually on in-house support calls.
3. Automating Ticket Triage and Alert Escalation
Much like a human agent, an AI copilot can analyze each ticket’s content and context and determine its issue category, priority, and appropriate resolver group. It can also assess the business impact of incidents and notify teams accordingly.
For instance, the AI copilot can automatically send an alert message to the engineer regarding a high-impact outage. On the other hand, it will send an email for less urgent cases.
However, unlike humans, the AI copilot can do all these tasks in a matter of minutes.
In doing so, it ensures that high-priority incidents get immediate attention, improving the mean time to acknowledge and respond.
Already, businesses implementing AI-powered customer solutions are slashing response times by 40% and elevating customer satisfaction by 25%.
BigPanda: Transforming Alerts into Action
One example of AI-driven ticketing is BigPanda’s Incident Intelligence and Automation. The solution dedupped, filtered, and correlated tickets into a consistent format, adding relevant information to the ticket. It also highlighted alerts that needed immediate attention, reducing the ticket identification process from 30 minutes to 30 seconds.
As a result, BigPanda helped its client automate processes by 83% and meet 95% of SLAs and 91% of critical alert SLAs.
4. Identifying Root Causes
In addition to automatically triaging tickets and escalating issues, AI copilots can also diagnose the root cause of issues faster than humans.
They can ingest historical incident data and real-time telemetry, correlate symptomatic alerts across infrastructure layers (network, server, application), and apply pattern recognition to suggest the most likely cause.
To demonstrate: if multiple microservices degrade due to a single database failure, the AI groups those alerts and pinpoints the database as the root cause. This automated root-cause analysis can shrink troubleshooting time from hours (or war-room calls combing through logs) to minutes.
IBM: Automating Issue Resolution
Previously hindered by tools that couldn’t isolate customer-specific issues, ExaVault adopted IBM’s Instana for its robust AI-powered root cause analysis and real-time alerting.
Instana's ability to filter metrics down to individual accounts enabled the team to quickly identify and resolve edge-case issues that standard monitoring missed. As a result, ExaVault reduced its mean time to repair (MTTR) by 56.6% and improved uptime from 99.51% to 99.99%, significantly minimizing customer-impacting bugs and improving reliability.
5. Streamlining Knowledge Management
After an incident is resolved — whether by AI or a human — the AI copilot can automatically produce a remediation runbook, Standard Operating Procedure (SOP), or a postmortem report for future reference.
It will include timelines of events, log extracts, root causes, and actions taken. It can also highlight incident trends, like “this is the third similar outage this month,” and suggest preventative measures.
By automating parts of the post-incident review, copilots ensure valuable lessons and data aren’t lost while freeing engineers from tedious documentation work.
In fact, 84% of developers are already using or plan to use AI in their documentation process.
6. Monitoring Continuous Deployment
AI copilots play a crucial role in maintaining system stability by scanning environments for vulnerabilities or outdated software. It then correlates this data with business outcomes, providing decision-makers with real-time insights into system performance.
For instance, an AI copilot can identify a dip in website traffic and correlate it with server performance issues, helping business leaders understand how IT disruptions impact customer engagement in real time.
Based on this information, the AI copilot suggests when to best schedule patches, so it won’t interrupt user experiences and business operations during peak periods.
GitHub Copilot: Enhancing Software Configuration and Deployment
GitHub Copilot has been the go-to tool for 40% of developers to accelerate CI/CD workflows, automate code configurations, and troubleshoot DevOps workflows.
It can evaluate infrastructure configs, propose improvements, and even diagnose deployment issues. Moreover, the AIOps tool can detect errors in CI/CD pipelines or suggest a fix for a failed rollout by analyzing logs and system state. These features boost developer velocity and reduce deployment failures by acting as an AI partner for ops teams.
A study by Harness observed that developers using GitHub Copilot improved cycle time by 2.4%, reducing it by 3.5 hours on average. These developers also experienced 10.6% higher pull requests, showcasing better collaboration and productivity among team members.
7. Rollback Readiness
During software releases or configuration changes, AI copilots track key metrics to detect signs of regression. If error rates or latency spikes, the AI tool can automatically pause the rollout or initiate a rollback workflow.
Advanced AIOps solutions, like those used at Microsoft Azure, analyze thousands of deployment signals to determine whether a deployment should be halted due to potential issues. This proactive approach minimizes the risk of failed rollouts, ensuring that new updates don't negatively impact customers.
As a result, DevOps teams gain confidence knowing that if an issue arises in production, the AI copilot will promptly identify and address it, ensuring minimal disruption.
8. Providing Predictive Analytics
By analyzing past incidents, system performance metrics, resource utilization, and traffic patterns, AI-powered predictive analytics can detect potential issues before they arise. Examples include server overloads, application slowdowns, or infrastructure bottlenecks.
AI copilots can also forecast upcoming IT resource demands, like increased processing power or bandwidth during peak usage periods, enabling IT operations to proactively scale resources and ensure smooth performance.
In a real-world scenario, an AI copilot might analyze historical network traffic patterns and predict a spike in demand on an eCommerce platform during a holiday sale, allowing IT teams to adjust server capacity and optimize network performance ahead of time to avoid service disruptions.
Addressing Challenges in Integrating AI Copilot in IT Operations
While AI copilots offer numerous benefits, their integration into existing IT environments can pose challenges, especially in organizations with legacy systems. The key to success, according to Malay Parekh, CEO at Unico Connect, is to foster a business culture that understands the value of AI:
“Start with clear operational goals, ensure data infrastructure is enterprise-ready, and most importantly, empower cross-functional teams to make AI part of day-to-day decision-making.”
Here are additional ways to implement AI into your IT operations:
1. Start Small, Scale Smart
Begin by applying AI to well-defined, lower-risk areas where AI can deliver quick wins without endangering critical systems. Good starters include log monitoring, alert noise reduction, and automatic ticket sorting. This approach allows teams to build confidence in AI capabilities before moving to more complex processes, including automated remediation or root cause analysis.
2. Implement Explainable AI
When selecting AI copilot solutions, prioritize those with explainability and transparency features. Some AIOps platforms now offer natural language summaries of root causes or visual correlations that make it clear why the AI made a recommendation. Utilizing these capabilities helps demystify the AI’s “thought process” for stakeholders and team members.
3. Ensure Human Oversight
AI copilots work best with humans in the loop and a set of well-defined guardrails. For instance, requiring human approval before the AI executes a data-destructive action or widespread change. This oversight ensures that if the AI misfires or encounters an unknown scenario, humans can intervene.
AI Copilots in IT Operations: FAQs
1. Can AI copilots work with legacy systems?
Yes, AI copilots can be used even if you have legacy or mixed environments through APIs, connectors, plugins, or middleware. The key is to feed the copilot relevant data (logs, events, etc.) from those systems.
2. Are AI copilots secure?
Reputable AI Ops platforms are designed with enterprise security in mind, implementing cybersecurity solutions like data encryption in transit and at rest, role-based access control, audit logs for all automated actions, and options to run on-premises. Many also go through certifications (ISO, SOC 2, etc.) especially if they’re serving finance or healthcare clients. That said, it’s crucial to configure and use them properly and maintain human oversight on changes the AI is making.
