Artificial intelligence applied to IT infrastructure operations promises to transform enterprise management, but according to the latest Gartner report, the path to full automation will be fraught with obstacles. The analyst predicts that by 2030, a quarter of the work performed by infrastructure and operations teams will be handled by AI, although this advancement will bring an increase in failures and a proliferation of control tools that will complicate the current landscape. This scenario, far from simplifying the lives of technical managers, will require a careful strategy and investment in robust solutions that allow navigating the transition without compromising business continuity.
The report, published in July 2025 as part of Gartner's hype cycle for AI in IT operations, points out that current narratives promise tool consolidation, but the short-term reality will be the opposite. 'Many AI-for-IT-operations narratives promise tool consolidation,' the document explains. 'Agents will query multiple systems, reason across silos, and reduce dependence on specialized tools.' However, Gartner anticipates that for at least two years organizations will have to deal with 'more layers, more control points, and more specialized observability, orchestration, and management capabilities.' The pain will only lessen when future market and vendor consolidation reduces the overall tooling footprint. Until then, the recommendation for operations teams is to include in their strategic plans the increasing likelihood of AI being involved in service disruptions.
In fact, the projections are stark: by 2028, 40% of organizations using agentic AI at scale in production will experience a business-critical service disruption, up from less than 1% in 2026. Despite this trend, adoption will not stop. Gartner estimates that by 2029, 60% of enterprises will have deployed agentic AI as part of their IT infrastructure operations, up from fewer than 10% today. In the same year, only 20% of actions suggested by AI will require explicit human approval, down from 80% in 2025. This shift will occur thanks to the growing use of 'deterministic guardrails,' policy-driven rules that define what AI is allowed to do without supervision.
By 2030, executives are expected to have restructured half of all infrastructure and ops teams after investing in agents capable of handling complex management tasks. In that year, professionals still working in the field will use AI for every task that humans perform today: 75% of the work will be done by humans augmented with AI, and 25% will be executed entirely by AI. This new paradigm requires companies to prepare now by adopting a digital transformation strategy that integrates custom software capable of connecting with these agents and managing the emerging complexity.
At Q2BSTUDIO, as a software development and technology company, we understand that implementing AI agents in IT infrastructure cannot be done improvisationally. The proliferation of consoles and the increased likelihood of failures demand a structured approach that combines robust cloud solutions, integrated cybersecurity, and business intelligence tools to monitor and optimize performance. The key is to design systems that not only automate processes but also incorporate observability mechanisms to detect deviations and prevent disruptions. For example, integrating services on cloud AWS/Azure allows flexible infrastructure scaling, while implementing AI agents requires additional security layers to prevent unauthorized access or unexpected behaviors.
Cybersecurity becomes a fundamental pillar in this new ecosystem. If AI agents can make autonomous decisions, the risk of a malicious or erroneous action causing a critical disruption increases significantly. Therefore, organizations must invest in cybersecurity solutions that include penetration testing, continuous audits, and granular access policies. Furthermore, observability of the agents themselves (Agentic AI Observability) will be an essential technology to govern AI properly and keep operational costs under control. Gartner classifies this technology as one of the four that will reach maturity in the next two to five years, along with Agentic NetOps, Augmented FinOps, and multi-agent systems.
Cloud financial management will also benefit from AI. The concept of Augmented FinOps uses artificial intelligence to offer algorithmic budget planning and automatically optimize cloud resources, reducing inefficient usage and misaligned spending. For companies already using BI / Power BI tools, integrating data generated by AI agents can provide real-time dashboards that alert on cost or performance deviations. At Q2BSTUDIO we help organizations implement these solutions in a customized way, combining the power of the cloud with business intelligence to make more informed decisions.
Another area that will see profound transformation is autonomous endpoint management. With the increase in patches and updates generated by AI, IT teams need tools that automate machine configuration and software deployment according to user profiles. This, together with network automation through conversational interfaces, will allow administrators to focus on strategic tasks rather than routine maintenance. However, as Gartner warns, incorporating more agents without a solid architecture can multiply points of failure. Therefore, we recommend a gradual approach, starting with automation of critical processes and scaling to multi-agent systems only when the necessary organizational maturity is available.
Looking toward 2030, Gartner's vision paints a future where AI not only executes tasks but also makes complex decisions autonomously. Companies that anticipate this reality, investing in AI responsibly and with governance, will gain a significant competitive advantage. At Q2BSTUDIO we work with our clients to design and implement custom software solutions that integrate AI agents securely and efficiently, minimizing the risk of disruptions and maximizing return on investment. The future of IT operations is full of possibilities, but also challenges that require careful planning and collaboration with experienced technology partners.
