{"slug": "beyond-automation-what-ai-is-changing-in-it-operations", "title": "Beyond Automation: What AI is Changing in IT Operations", "summary": "Marvin Ondong, who leads IT operations in Kenya, reported a 75% reduction in monitoring noise after incorporating AI-driven anomaly detection and noise suppression into his team's monitoring environment. Using an AI-enabled Remote Monitoring and Management platform for patch orchestration, health checks, scripted remediation and remote troubleshooting, patch compliance rose from routinely below 50% to the 95th percentile. Ondong argues AI works best when it gives engineers better information rather than removing them from the process.", "body_md": "AI is transforming IT support, but success lies not in replacing humans but in combining AI’s speed and consistency with human judgment, empathy, and experience. Drawing on his real-world work leading IT operations in Kenya, Marvin Ondong shows how organisations can deploy AI plus human support thoughtfully for better outcomes.\n\n### **Reducing the Noise in Monitoring**\n\nOne of the areas where we have seen a significant improvement is monitoring.\n\nLarge technology environments generate a considerable number of alerts, and not all of them require the same level of attention. Duplicate alerts, temporary anomalies and low-priority events can create unnecessary noise, making it harder for teams to identify issues that may have a genuine operational impact.\n\nBy incorporating AI-driven anomaly detection and noise suppression into our monitoring environment, we have achieved a 75% reduction in monitoring noise.\n\nFor the team, that means less time working through unnecessary alerts and more time investigating the issues that require attention. It also helps us identify unusual patterns earlier and gives engineers better information when they begin investigating an incident.\n\nThis is an area where AI is particularly useful because it can process large volumes of information quickly. But once something unusual has been identified, understanding the cause and the potential impact still often requires someone who knows the environment.\n\n### **Improving Routine IT Operations**\n\nWe are also using automation to manage routine endpoint activities through an AI-enabled Remote Monitoring and Management platform.\n\nThis includes areas such as patch orchestration, health checks, scripted remediation, and remote troubleshooting.\n\nPreviously, patch compliance routinely fell below 50%, largely because of the manual workload involved and the scale of the environment. With the AI-supported RMM framework in place, compliance now reaches the 95th percentile.\n\nThat improvement has helped us strengthen endpoint stability and reduce exposure to known vulnerabilities, while also reducing the amount of repetitive work that engineers need to manage manually.\n\nIt is a good example of where automation makes sense. The process is predictable; it needs to happen consistently, and it must work across thousands of endpoints.\n\nThe technology can manage much of that activity in the background, while the team focuses on exceptions, escalation, and issues that need deeper investigation.\n\n### **Where People Still Make the Difference**\n\nNot every IT issue follows a predictable pattern.\n\nOur teams work across different infrastructure environments, networks, systems and client requirements. An issue that appears straightforward at first can sometimes involve several dependencies or require coordination between different teams.\n\nAI can help identify anomalies, analyse information and surface possible causes, but the team still needs to understand what is happening in a wider environment.\n\nThat becomes particularly important when an issue affects multiple systems, or when the technically obvious solution may have consequences elsewhere.\n\nThis is where experience matters.\n\nAn engineer who understands the environment can look beyond an individual alert and consider what changed, what systems are connected, whether similar issues have happened before, and what impact a particular action could have on the wider operation.\n\nFor us, AI works best when it gives that engineer better information to work with rather than trying to remove them from the process altogether.\n\n### **Supporting a High-Demand Environment**\n\nThe scale of the environment means that we have to find ways of increasing capacity without simply increasing the amount of manual work.\n\nAutomation has helped with this.\n\nRoutine endpoint activities can happen consistently in the background. Monitoring tools can filter and consolidate signals before they reach engineers. Predictive capabilities can help identify potential problems earlier, while automated remediation can address known issues without waiting for someone to intervene manually.\n\nThis gives the team more capacity to focus on service continuity, escalations, and more complex technical issues.\n\nIt also changes the way we think about productivity in IT operations. The benefit isn’t simply that a task is completed faster. It is that engineers have more time available for work where their experience can have a greater impact.\n\n### **Governance Still Matters**\n\nAs AI and automation become more capable, governance becomes increasingly important.\n\nThere needs to be clarity around which activities can be automated, when an engineer needs to intervene and who is responsible when an automated process does not behave as expected.\n\nThis is especially important in large technology environments. An automated action applied across thousands of endpoints can have a much wider impact than an action taken on an individual machine.\n\nFor that reason, we still need clear escalation paths, defined levels of access, and appropriate controls around automated activity.\n\nThis approach is consistent with the National Institute of Standards and Technology’s AI Risk Management Framework, which emphasizes ongoing monitoring of AI systems, clearly defined responsibilities and processes for human oversight and intervention.\n\nFor us, governance is not about slowing down the use of AI. It is about making sure we can use it confidently and responsibly within a live operational environment.\n\n### **The Role of the IT Engineer Is Evolving**\n\nAs more routine activities become automated, the role of the engineer is changing as well.\n\nTechnical knowledge remains essential, but there is increasing value in being able to interpret information, understand dependencies, and investigate issues that don’t have an obvious answer.\n\nEngineers also need to understand the tools they are working with. If an AI-enabled system recommends an action, the person responsible needs enough knowledge of the environment to know whether that recommendation makes sense.\n\nThis makes training an important part of AI adoption.\n\nOur teams need to understand what the technology can do, where its limitations are and when something needs to be escalated or investigated differently.\n\nNIST similarly recommends clearly defining responsibilities for people operating and overseeing AI systems, alongside appropriate training and proficiency standards.\n\n### **What We Have Learned So Far**\n\nOur experience so far has shown us that some of the most useful applications of AI in IT operations are relatively practical.\n\nThe **75% reduction in monitoring noise** means engineers are spending less time working through unnecessary alerts. Moving patch compliance from below **50% to the 95th percentile** has strengthened the consistency of endpoint management. Automation across more than **10,000 endpoints** is helping us manage an environment that would otherwise require a significant amount of repetitive manual activity.\n\nAt the same time, the more complex parts of IT operations still rely heavily on people.\n\nTechnology can identify an unusual pattern or automate a known fix. An experienced engineer understands the wider environment, the dependencies involved, and the potential impact of the decision being made.\n\nThat balance is becoming increasingly important as AI becomes part of everyday IT operations.\n\nFor me, the value of AI is quite practical. It helps us manage scale, gives our teams better visibility and removes some of the repetitive work that can take time away from more important issues.\n\nTechnology is becoming more capable, but the experience of the people managing the environment remains just as important.\n\n### **Supporting Research & Links**\n\n- **NIST – Artificial Intelligence Risk Management Framework**\n [NIST AI Risk Management Framework](https://airc.nist.gov/airmf-resources/?utm_source=chatgpt.com)\n- **NIST – AI RMF: Human-AI Interaction**\n [NIST guidance on Human-AI Interaction](https://airc.nist.gov/airmf-resources/airmf/appendices/app-c-ai-risk-management-and-human-ai-interaction/?utm_source=chatgpt.com)\n- **NIST – AI RMF Playbook: Governance and Human Oversight**\n [NIST AI RMF Governance Playbook](https://airc.nist.gov/airmf-resources/playbook/govern/?utm_source=chatgpt.com)\n- **NIST – AI RMF Playbook: Measuring and Monitoring AI Systems**\n [NIST guidance on AI monitoring and measurement](https://airc.nist.gov/airmf-resources/playbook/measure/?utm_source=chatgpt.com)", "url": "https://wpnews.pro/news/beyond-automation-what-ai-is-changing-in-it-operations", "canonical_source": "https://techstrong.it/contributed-content/beyond-automation-what-ai-is-changing-in-it-operations/", "published_at": "2026-09-11 19:57:30+00:00", "updated_at": "2026-09-11 20:53:35.901781+00:00", "lang": "en", "topics": ["ai-products", "artificial-intelligence"], "entities": ["Marvin Ondong", "Kenya"], "alternates": {"html": "https://wpnews.pro/news/beyond-automation-what-ai-is-changing-in-it-operations", "markdown": "https://wpnews.pro/news/beyond-automation-what-ai-is-changing-in-it-operations.md", "text": "https://wpnews.pro/news/beyond-automation-what-ai-is-changing-in-it-operations.txt", "jsonld": "https://wpnews.pro/news/beyond-automation-what-ai-is-changing-in-it-operations.jsonld"}}