Using AI to Transform Technical Operations in Your Organization

In today’s fast-paced and highly complex technology landscape, operations teams face the challenge of managing vast amounts of data, ensuring high availability, and responding quickly to incidents while maintaining service reliability. Artificial Intelligence is emerging as a powerful tool to transform how operations teams work, offering smarter, more efficient ways to address these challenges. From AIOps (Artificial Intelligence for IT Operations) to ITOA (IT Operations Analytics), AI is revolutionizing the operations and support model, whether in a traditional support structure, Site Reliability Engineering (SRE) model, or a DevOps framework.

AI is reshaping key areas of operations, providing tools and capabilities that streamline workflows, enhance decision-making, and improve system reliability. Let’s explore how AI can transform an operations organization and where it can deliver the most value.

Reducing Noise with AI-Powered Monitoring and Correlation

One of the biggest challenges in operations is managing the massive amounts of data generated by telemetry, observability systems, monitoring tools, and alerts. As the volume of data increases, teams often struggle to differentiate between critical signals and background noise. Traditional methods involve manually tuning alert thresholds or creating complex rules to manage alerts, but these can be time-consuming, prone to error, and ineffective in rapidly evolving systems.

AI significantly enhances this process by automatically analyzing and correlating large datasets to identify patterns and anomalies. By leveraging machine learning models, AI can intelligently group related alerts and incidents into a single "situation," greatly reducing alert fatigue and the number of false positives. This AI-powered correlation not only improves efficiency but also accelerates response times, allowing teams to react more swiftly and effectively.

Out-of-the-box AI models are maturing and have started to demonstrate near-immediate time-to-value, eliminating the long implementation times typically associated with rules-based correlation solutions. With these ready-to-deploy models, organizations can start seeing significant improvements in their operations right away. The need for complex, time-intensive rule writing has diminished, enabling faster adoption and reducing the burden on operations teams.

Real-world case studies underscore the effectiveness of this approach. For example, a global Managed Service Provider that implemented GrokStream’s AIOps platform saw an 80% reduction in incidents, saving 40,000 Network Operations Center (NOC) hours annually, which translated to a savings of $1.2 million in operational costs. By leveraging AI to intelligently correlate and group incidents, the provider dramatically improved operational efficiency and reduced manual intervention.

AI’s ability to correlate and group alerts into a single "situation" also ensures that the right team members are alerted based on their expertise. This prevents unnecessary escalations where multiple engineers are pulled into a single incident bridge call. As a result, AI-driven alert correlation leads to more focused and efficient use of resources, reduced mean time to resolution (MTTR), and ultimately, a more agile and responsive operations team.

Another case study from GrokSteam highlights a Fortune 500 enterprise using AIOps to achieve a 72% reduction in incidents, saving 36,000 Level 1 and Level 2 support hours annually, or $1.08 million in support costs. By automating the event correlation process, these organizations not only improved the speed of issue resolution but also maximized the impact of their human resources, ensuring that the right teams addressed the right problems faster.

Leveraging Historical Data for Root Cause Analysis

AI can also mine your historical knowledge base, including runbooks and past incidents, to assist in real-time root cause analysis. When a new issue arises, AI systems can analyze previous incident records and suggest possible causes and recommended actions for triage. This data-driven approach helps engineers address incidents more effectively by leveraging prior experiences and known resolutions.

Rather than starting from scratch each time an issue occurs, operations teams can benefit from AI’s ability to quickly reference existing knowledge, improving decision-making and accelerating incident resolution. By integrating AI with your knowledge base, teams can optimize their response times and maintain consistency in addressing recurring issues.

AI in Customer Communication

Customer communication during incidents can often be slow, impersonal, or overly technical, leading to frustration. AI presents a clear opportunity for improving the way organizations communicate with customers during outages or service disruptions.

AI can automate incident notifications, providing customers with real-time updates in plain language, including the current status of the issue, expected resolution times, and any mitigation steps being taken. Additionally, AI can generate post-incident reports and Root Cause Analyses (RCAs), maintaining transparency with customers while ensuring the report is clear, concise, and understandable.

FICO has implemented Microsoft Copilot to improve our post-incident reporting. While FICO operators still provide review and governance of these customer comms, AI-assisted communication has reduced the manual burden on engineers, enhanced our customer trust, and improved our overall customer satisfaction.

Creating and Managing Knowledge with AI

A robust knowledge base is essential for ensuring that operational teams can respond quickly to incidents and challenges. Traditionally, knowledge management within IT operations involves manually creating and updating documentation, runbooks and application flow diagrams. This process can be labor-intensive, prone to inconsistencies, and often ends up stale.

AI can automate much of this work by analyzing source code, configuration management data and system behaviors to generate release notes, application flow diagrams and runbooks. By continuously analyzing the software, infrastructure, and configurations, AI ensures that the knowledge base remains up-to-date and relevant. Engineers shift to being content editors rather than content creators on critical knowledge artifacts. Additionally, AI facilitates knowledge transfer across teams, breaking down silos and making critical information more accessible to engineers, regardless of their role or function. This results in faster onboarding, improved incident response and better cross-functional collaboration.

FICO has utilized AI around incident knowledge management in two ways:

• We use chatbot AI technology to help support engineers find relevant operations procedures through conversational interaction to extract necessary details to link to the most appropriate knowledge article,  
• We use AI to create new knowledge by dynamically enhancing those procedures into change plans based on the specifics of the current action. These updated change procedures provided further training for the chatbot model and improved the quality of our knowledge base.

Turning Operations Engineers into SREs

The shift toward Site Reliability Engineering (SRE) as a model for operations teams presents an opportunity to evolve traditional operations engineers into more specialized, value-added roles. Many operations engineers possess deep technical knowledge but lack experience with development practices. AI can help bridge this gap, allowing these engineers to take on more development-oriented tasks.

One example of this transformation is in hotfix creation, where operations engineers can use AI to quickly identify and implement fixes without needing to escalate the issue to software engineering teams. Traditionally, when incidents occur that require a hotfix, operations teams must escalate the issue to software engineering teams. However, with AI’s support, operations engineers can leverage their deep understanding of the environment, technology stack and incident response practices to quickly develop hotfixes themselves. This reduces reliance on software engineers, improves MTTR and decreases recurrence of incidents.

Skytells, a company employing AI-assisted development tools, has leveraged AI to accelerate their software development processes. By integrating AI-driven tools, such as DeepCoder and Eve AI Assistant, Skytells has achieved remarkable improvements in software quality and efficiency. Specifically, the AI-assisted tools have resulted in a 70% reduction in bugs per 1,000 lines of code, which directly impacts their ability to address issues faster.

AI-assisted development results in more rapidly and accurately deployed hotfixes by operations, reducing the need for urgent, resource-draining fixes and improving overall system reliability. These AI-powered tools allow operations engineers to implement fixes swiftly and effectively without waiting for software engineers to intervene.

Once the hotfix is developed, it can be passed on to the engineering team for review and inclusion in future releases. This approach not only accelerates the resolution of incidents but also enables operations teams to focus more on ensuring system stability and resilience, while development teams continue their focus on feature development. This evolution of traditional operations roles into SREs helps streamline operations and increases the overall effectiveness of IT teams.

The Challenges and Considerations in Implementing AI in Operations

While the benefits of AI are significant, it is not without risks and hurdles. One of the primary challenges is ensuring the quality and accuracy of the data that AI is analyzing. AI models are only as good as the data they are trained on, so it’s crucial to ensure data is clean, accurate, and comprehensive. In addition, out-of-the-box models assume certain data is available that either may not be currently captured or may have quality issues. Projects to incorporate AI into technical operations often run in parallel with projects to improve the coverage or quality of key telemetry, monitoring, or logging data.

Furthermore, the adoption of AI may require a cultural shift within the operations team. Engineers see the use of AI as a threat to their roles. Formal organizational change management programs that not only help educate the engineer on the use of the tools, but emphasize how incorporation of this technology into daily operations frees the engineering staff up for more high-value activities, are required. Organizations should focus on upskilling their teams to fully leverage the power of AI and help foster collaboration across traditionally siloed departments.

Finally, data security needs to be a focus of any effort to apply AI to technical operations. The data sets in use often contain sensitive system data and potentially customer data. Ensuring the data remains governed by your organizational policies around security and compliance and is not inadvertently being exposed through inclusion in training of AI models is essential.

Cost Considerations for AI Adoption

Implementing AI solutions can be a significant investment, particularly for smaller organizations. However, it's important to recognize the potential ROI. Part of the Total Cost of Ownership (TCO) analysis for AI should include the opportunity cost of allowing high-cost software engineers to focus on value-added activities, such as innovation and the development of new features and functions, rather than spending cycles on operational tasks now covered by AI-assisted operations staff.

Additionally, upskilling operations engineers into SRE engineers through AI integration helps reduce the average cost of traditionally high-salaried SRE engineers. As AI enables operations teams to handle more complex tasks traditionally reserved for software engineers, organizations can lower the reliance on higher-cost engineering resources for day-to-day operational tasks.

The average salary of an SRE engineer in the United States is around $135,000 to $160,000 per year, depending on location and experience. This can vary significantly by region, with SRE salaries in cities like San Francisco and New York often exceeding $180,000 (Glassdoor, 2025). This compares to a traditional operations engineer, whose average salary in the U.S. ranges from $75,000 to $100,000 per year, depending on experience and company (PayScale, 2025). These factors, along with the efficiency and reliability improvements driven by AI, often lead to a net positive ROI.

Strategic Steps for AI Adoption in Operations

Transforming technical operations through implementing AI-based tools and processes tools requires careful assessment of existing infrastructure, data maturity and the skillsets of the operations team. A roadmap for AI adoption can help guide this process, starting with smaller pilot projects where there are large operations staff but limited technical skills (e.g., Level 1 support or customer communications) and scaling gradually as the team gains experience. It’s also crucial to select AI vendors who can provide out-of-the-box models that meet your specific needs, while also allowing for customizations that address gaps in coverage.

AI adoption is not just a technical change but a cultural transformation. Operations teams must be trained to focus on data quality, training of the models through reviewing and providing feedback on AI-based outcomes, and governing the management of the AI tools themselves. Clear communication with staff about the benefits of AI, such as reducing the burden of routine tasks and enabling them to focus on more strategic activities, is vital for smooth adoption.

Lastly, data privacy and security must be top priorities when integrating AI in operations. With sensitive customer and system data being analyzed, it is essential to have robust governance and compliance measures in place to protect data and ensure privacy regulations are met.

The Future of Technical Operations is AI-Driven

AI is no longer a futuristic concept — it’s already playing a critical role in transforming technical operations teams across industries. From reducing noise in monitoring and alerting systems to automating customer communications and creating intelligent knowledge bases, AI is enabling organizations to work smarter, not harder. It empowers operations teams to focus on high value tasks, improves collaboration and enhances the reliability and stability of critical systems.

As AI continues to evolve, its potential to streamline workflows, improve incident response, and create more efficient operations models will only increase. For IT organizations looking to stay ahead of the curve, embracing AI-driven solutions is not just an option — it’s a strategic necessity to transform operations to be more agile, responsive and cost-effective. Starting with small, strategic AI initiatives can help organizations scale over time, maximizing the impact of AI and ensuring continued success in the rapidly changing IT landscape.