Legacy IT is the topic nobody wants to address in strategic meetings, and that then inevitably shows up as an obstacle in every digital transformation project. ERP systems implemented twenty years ago, databases without APIs, custom applications written with technologies nobody knows anymore, processes that live inside Excel spreadsheets shared on internal servers. When an organization decides to introduce AI agents into its workflows, sooner or later it reaches the point where the agent must interact with one of these systems. That's where things get complicated.
Gartner has analyzed the problem directly, separating what GenAI can actually do for legacy modernization from what vendors promise but don't deliver.
The inflated promise and operational reality
Gartner published an explicit analysis on the topic: vendor claims that GenAI will eliminate legacy IT and automatically resolve technical debt have inflated stakeholder expectations. CIOs must use this research to identify where and how much GenAI can realistically transform modernization practices.
The point isn't that GenAI is useless for legacy modernization: it's that it works differently from how it's often sold. It's not a tool that automatically transforms obsolete systems into modern applications. It's a tool that can accelerate specific phases of the modernization process: documenting existing code, generating tests, guided migration between languages, and technical debt analysis. But the architecture work, the decision on what to keep and what to replace, and managing operational risk during the transition remain human responsibilities.
Hybrid computing as a long-term strategy
In the December 2025 press release on trends that will impact infrastructure and operations in 2026, Gartner identifies hybrid computing as the structural answer to the problem of technological heterogeneity. Hybrid computing is an emerging architectural style that orchestrates diverse and sometimes incompatible compute, storage, and networking environments. It allows I&O leaders to protect infrastructure investments over time with a composable and extensible architecture, maximizing the value of emerging technologies by combining their strengths.
Gartner is direct on this point: hybrid computing will force I&O leaders to adopt a composable business and technology architecture as part of a long-term strategy for building systems and applications. This isn't a temporary choice while waiting for legacy to disappear: it's the recognition that heterogeneous environments are the permanent condition of any organization with a history, and that the winning strategy isn't wholesale replacement but intelligent orchestration.
AI agents and systems without APIs
One of the most frequent practical problems when integrating AI agents with legacy systems is the absence of modern APIs. Many enterprise systems were built before APIs became a standard, and they don't expose interfaces that an agent can query programmatically. Gartner points to the "computer use" approach as one of the emerging responses to this problem: agents that interact with legacy system user interfaces just as a human operator would, without requiring changes to the underlying system.
This solution has advantages and limitations. The advantage is that it can work on any system with a graphical interface, regardless of age and technology. The limitation is that it's slower, more fragile to interface changes, and harder to monitor than an API integration. Gartner classifies it as part of the "Predicts 2026: The New Era of Agentic Automation Begins" trends, signaling that agentic automation through direct computer use is one of the technological discontinuities reshaping the enterprise automation market.
Inference spending surpasses training spending in 2026
A data point that measures AI market maturity comes from Gartner's analysis of AI-optimized IaaS infrastructure, published in October 2025. Gartner estimates that in 2026, 55% of AI-optimized IaaS spending will support inference workloads, surpassing training spending for the first time. In absolute terms, inference spending is projected at $20.6 billion in 2026, up from $9.2 billion in 2025.
For those managing legacy systems, this data point is relevant for a practical reason: inference happens in production, on real data, in real time. It means the pressure to integrate AI models with existing operational systems grows at the same pace as inference spending. The more organizations bring AI into production, the more urgent and less deferrable the legacy integration problem becomes.
Intelligent applications: five characteristics driving modernization
Gartner has defined the characteristics of intelligent applications, identifying them as the target for legacy application modernization. The five characteristics are: continuous learning from operational data, the ability to explain decisions in an understandable way, real-time integration with multiple data sources, adaptability to user and environment context, and the ability to orchestrate autonomous actions within defined parameters.
Not every legacy system needs to become an intelligent application, and it doesn't need to happen all at once. Gartner indicates that the most effective modernization starts by identifying the processes where learning, explainability, and automation capabilities produce the highest business value, and concentrating the transformation investment there. Legacy that doesn't block high-value processes can wait: legacy that sits on the critical path of AI projects has immediate priority.
Technical debt as a strategic decision
Gartner positions technical debt not as a technical problem to solve, but as a strategic decision to manage. Every dollar invested in maintaining a legacy system is a dollar not going toward building the capabilities the organization will need in the next competitive cycle. The question isn't "how do we modernize everything?" but "which legacy systems are consuming disproportionate resources relative to the value they produce, and in what order should we address them?"
In this context, AI isn't the solution to legacy: it's a tool that changes the cost-benefit equation of modernization in specific cases. Accelerating code documentation, automating regression tests, supporting guided migration between technologies: all activities where GenAI produces a measurable benefit in the modernization process. But the strategy remains a human choice, and the choice matters more than the tool.
