4 Ways Agentic AI Could Transform IT Operations

Eric Johnson
PagerDuty
Learn more about PagerDuty

The next generation of AI is already here. It may have been mere months since organizations adopted generative AI (GenAI), but now there's a new kid on the block and it promises to offer even greater benefits to businesses and IT operations teams in particular. In fact, research reveals that more than half of companies in the US, UK, Australia and Japan have already adopted agentic AI, with most expecting an ROI of over 100%.

The key to success will be to avoid repeating the adoption mistakes of the past and to start small with manageable projects.

A New Era of Productivity

Agentic AI promises another great leap forward.

The machine-based intelligence is capable of working autonomously to achieve pre-determined goals. In the process, it is capable of adjusting to any unseen bumps in the road through reasoning, iterative planning and adaptive problem-solving. While GenAI focuses on creating content and requires human input in the form of prompts, its agentic cousin acts independently with little or no intervention, making decisions based on data and objectives. Unlike GenAI, it continuously learns and adapts.

It's not difficult to see the huge potential here for enhancing business performance, upskilling workers and relieving them from manual toil. Gartner believes that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028. Last year, the figure was zero.

Use cases are almost limitless in scope.

Take financial services. An AI agent could be set to work continuously, monitoring transactions for anomalies indicative of fraud. It would leverage adaptive learning to tell the difference between legitimate and criminal activity and take actions to remediate the problem, such as blocking a transaction and notifying the customer. All of this minimizes the delays and customer frustration that come with traditional manual reviews, not to mention the risk of fraudulent transactions sneaking through. From the bank's perspective, it frees staff to work on more satisfying and higher-value tasks, mitigating fraud losses and keeping customers happy.

Companies Are Bullish

There are many more examples like this. In healthcare, agentic AI could automate patient scheduling based on doctor availability, patient history and urgency, sending appointment reminders and even predicting potential cancellations. In local government, it could automate the time-consuming, paperwork-heavy process of granting business or construction permits by analyzing submitted documents and referencing regulatory requirements. The impact on citizens, businesses and under-staffed, under-funded local authorities could be immense.

It's no surprise that organizations are so optimistic about the technology. By 2027, 86% of companies expect to have deployed AI agents operationally, with early GenAI adopters leading the way.

4 Ways to Change ITOps

On a more granular level, agentic AI promises to transform IT operations (ITOps). We've already seen how GenAI has helped by intelligently automating alert correlation, root cause analysis, ticketing and reporting, as well as how it can empower and upskill incident responders as they struggle to deal with manual toil and alert overload. Agentic AI offers more, working through problems even if there are multiple steps and disparate tools involved.

Here are four specific use cases:

1. Site Reliability Engineering (SRE): SRE expertise is in short supply, but it's still much needed as application complexity and customer expectations increase. Agentic AI could help engineers fix problems faster by identifying and classifying operational issues, flagging important historical context and suggesting recommended actions, allowing engineers to focus on innovation.

2. Operations insight: Complexity is the enemy of effective IT operations. ITOps teams sometimes struggle to make sense of their environment given the number of tools they have to manage across distributed, hybrid cloud and on-premises systems. An AI agent could analyze data from across this potentially large ecosystem of tools, uncover trends, surface insights and recommend actions for improved decision making.

3. Scheduling: Today's customers expect seamless digital experiences, and if they don't get them, they are more likely than ever to move to a competitor. That makes it especially critical to ensure seamless responder coverage. Agentic AI can help by taking on a painful manual process, pre-empting scheduling and availability conflicts by dynamically adjusting on-call shifts. Not only will this help drive faster incident resolution, it could also reduce operational costs.

4. Incident response: Incidents are a fact of life in a modern, digital-first organization. But as the business impact of such issues increases, the pressure mounts on ITOps to get ahead of system failures. Agentic AI can reduce response times and human error by stepping in to help and proactively identifying anomalies, taking action to resolve them and continuously learning from past incidents. As with all of the above examples, this is about freeing up human talent to work on higher-value tasks, which also means happier employees.

Some Lessons Learned

As much as organizations are keen to harness the benefits of agentic AI, they're also aware of repeating the mistakes of the past. Many know they didn't train employees enough on GenAI to truly optimize their use of the technology. That's why nearly two-thirds (61%) are planning organization-wide seminars or training initiatives. Others cite lessons learned, such as insufficient planning, not having well-defined ROI expectations and a failure to put in place the right data infrastructure first.

Operational guidelines and guardrails will also be critically important as organizations rush to embrace a technology that operates autonomously.

Agentic AI promises to help ITOps do critical work better, faster and smarter, but success requires careful planning.

Eric Johnson is Chief Information Officer at PagerDuty

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4 Ways Agentic AI Could Transform IT Operations

Eric Johnson
PagerDuty
Learn more about PagerDuty

The next generation of AI is already here. It may have been mere months since organizations adopted generative AI (GenAI), but now there's a new kid on the block and it promises to offer even greater benefits to businesses and IT operations teams in particular. In fact, research reveals that more than half of companies in the US, UK, Australia and Japan have already adopted agentic AI, with most expecting an ROI of over 100%.

The key to success will be to avoid repeating the adoption mistakes of the past and to start small with manageable projects.

A New Era of Productivity

Agentic AI promises another great leap forward.

The machine-based intelligence is capable of working autonomously to achieve pre-determined goals. In the process, it is capable of adjusting to any unseen bumps in the road through reasoning, iterative planning and adaptive problem-solving. While GenAI focuses on creating content and requires human input in the form of prompts, its agentic cousin acts independently with little or no intervention, making decisions based on data and objectives. Unlike GenAI, it continuously learns and adapts.

It's not difficult to see the huge potential here for enhancing business performance, upskilling workers and relieving them from manual toil. Gartner believes that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028. Last year, the figure was zero.

Use cases are almost limitless in scope.

Take financial services. An AI agent could be set to work continuously, monitoring transactions for anomalies indicative of fraud. It would leverage adaptive learning to tell the difference between legitimate and criminal activity and take actions to remediate the problem, such as blocking a transaction and notifying the customer. All of this minimizes the delays and customer frustration that come with traditional manual reviews, not to mention the risk of fraudulent transactions sneaking through. From the bank's perspective, it frees staff to work on more satisfying and higher-value tasks, mitigating fraud losses and keeping customers happy.

Companies Are Bullish

There are many more examples like this. In healthcare, agentic AI could automate patient scheduling based on doctor availability, patient history and urgency, sending appointment reminders and even predicting potential cancellations. In local government, it could automate the time-consuming, paperwork-heavy process of granting business or construction permits by analyzing submitted documents and referencing regulatory requirements. The impact on citizens, businesses and under-staffed, under-funded local authorities could be immense.

It's no surprise that organizations are so optimistic about the technology. By 2027, 86% of companies expect to have deployed AI agents operationally, with early GenAI adopters leading the way.

4 Ways to Change ITOps

On a more granular level, agentic AI promises to transform IT operations (ITOps). We've already seen how GenAI has helped by intelligently automating alert correlation, root cause analysis, ticketing and reporting, as well as how it can empower and upskill incident responders as they struggle to deal with manual toil and alert overload. Agentic AI offers more, working through problems even if there are multiple steps and disparate tools involved.

Here are four specific use cases:

1. Site Reliability Engineering (SRE): SRE expertise is in short supply, but it's still much needed as application complexity and customer expectations increase. Agentic AI could help engineers fix problems faster by identifying and classifying operational issues, flagging important historical context and suggesting recommended actions, allowing engineers to focus on innovation.

2. Operations insight: Complexity is the enemy of effective IT operations. ITOps teams sometimes struggle to make sense of their environment given the number of tools they have to manage across distributed, hybrid cloud and on-premises systems. An AI agent could analyze data from across this potentially large ecosystem of tools, uncover trends, surface insights and recommend actions for improved decision making.

3. Scheduling: Today's customers expect seamless digital experiences, and if they don't get them, they are more likely than ever to move to a competitor. That makes it especially critical to ensure seamless responder coverage. Agentic AI can help by taking on a painful manual process, pre-empting scheduling and availability conflicts by dynamically adjusting on-call shifts. Not only will this help drive faster incident resolution, it could also reduce operational costs.

4. Incident response: Incidents are a fact of life in a modern, digital-first organization. But as the business impact of such issues increases, the pressure mounts on ITOps to get ahead of system failures. Agentic AI can reduce response times and human error by stepping in to help and proactively identifying anomalies, taking action to resolve them and continuously learning from past incidents. As with all of the above examples, this is about freeing up human talent to work on higher-value tasks, which also means happier employees.

Some Lessons Learned

As much as organizations are keen to harness the benefits of agentic AI, they're also aware of repeating the mistakes of the past. Many know they didn't train employees enough on GenAI to truly optimize their use of the technology. That's why nearly two-thirds (61%) are planning organization-wide seminars or training initiatives. Others cite lessons learned, such as insufficient planning, not having well-defined ROI expectations and a failure to put in place the right data infrastructure first.

Operational guidelines and guardrails will also be critically important as organizations rush to embrace a technology that operates autonomously.

Agentic AI promises to help ITOps do critical work better, faster and smarter, but success requires careful planning.

Eric Johnson is Chief Information Officer at PagerDuty

Related Links

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For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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