We’re just two months into 2025, and the technology landscape is already experiencing significant shifts. With Generative AI (GenAI) adoption at an all time high, renewed interest in private cloud, and increasing regulatory scrutiny, 2025 is shaping up to be a pivotal year for enterprise operations. However, as organisations integrate increasingly complex technologies into their IT infrastructures, the risk of system overload grows. How can companies harness these advancements to drive innovation while ensuring resilience and reliability in their systems?
Automation: The key to comprehensive IT monitoring
As AI is embedded across business operations, observability is no longer optional—it’s a necessity. Emerging technologies like agentic AI are reshaping everyday IT Operations workflows, making observability the foundation of enterprise IT strategy in 2025. We are reaching a pivotal moment in digital transformation and, while this is a positive and innovative time for the tech industry, organisations must recognise observability as a cornerstone that enables companies to unlock the full potential of AI.
Agentic AI is a prime example of an emerging technology already reshaping and disrupting enterprise tech stacks. AI agents are able to analyse, learn, and act on business data autonomously, so it is no surprise they are largely welcomed by tech giants across the globe. Despite their growing adoption by leading tech companies, their role in addressing IT infrastructure complexity remains underappreciated. Using AI agents, enterprises can enhance operational efficiency, minimize downtime, and optimize resource allocation with unprecedented precision.
GenAI transforming operations
The convergence of AI and observability is also set to transform how customers are managing their technology stack, from automation to chatbots. GenAI adoption has skyrocketed—nearly 70% of Fortune 500 companies reported using Microsoft Copilot in 2024, a figure expected to rise this year. As GenAI becomes an operational standard, its benefits extend far beyond efficiency, enabling advanced troubleshooting, code generation, and system optimization.
To maximize the impact of GenAI, organisations must adopt more integrated and customizable AI observability solutions. Through the technology’s ability to analyse data and ‘think’ both slow and fast, organisations are able to quickly jump on and act on any issues that arise. An effective strategy involves first pre-training AI models with agentic capabilities. This approach helps in resolving issues more quickly while also improving explainability. As a result, AI becomes more transparent and easier to understand for both employees and customers.
The cruciality of collaboration
The observability sector is expected to grow at an annual rate of 19.28% between 2025 and 2034. One major reason for this growth is the increasing use of AI, which helps businesses monitor their IT systems and keep them running smoothly.
However, AI is not a perfect solution on its own. When different AI tools are not properly integrated and don’t have guardrails, they can create new risks, such as miscommunication between systems or unexpected technical issues. This is why it’s important for different teams within an organisation to work together to ensure AI tools function properly across all systems.
Artificial Intelligence for IT Operations (AIOps) is one way to solve this challenge. AIOps uses machine learning and data analysis to automate repetitive tasks, detect problems before they become major issues, and improve overall efficiency. But its impact goes beyond just automation—it also helps IT teams communicate more effectively, making it easier to manage complex IT environments.
When AIOps is used across teams like DevOps, ITOps, and Network Ops, it creates a strong foundation for AI adoption. By reducing the need for manual work, sending real-time alerts, and integrating smoothly with existing IT systems, AIOps allows teams to react faster and work more efficiently. More importantly, it shifts IT teams from reacting to problems after they occur to preventing them in the first place. By improving collaboration and breaking down communication barriers, AIOps helps businesses stay ahead of IT challenges and respond quickly when issues arise.
AI and observability: what’s next?
In 2025, AI adoption will move beyond surface-level innovation, driving measurable business outcomes. By automating routine tasks, IT teams can spend less time on repetitive work and more time on strategic projects that drive innovation. At the same time, agentic AI is transforming observability by using intelligent automation to monitor and manage systems more effectively.
But technology alone isn’t enough. Without empowering teams to effectively integrate and leverage agentic AIOps, adoption risks becoming performative rather than transformative.
True success lies in fostering collaboration across teams, breaking down silos, and embedding AI into the fabric of modern data center operations. By taking this approach, AI doesn’t just optimize systems—it empowers employees, uplevels the customer experience, and future-proofs IT infrastructure.
