Outdated tech slows AI progress for most firms

Tech debt rarely announces itself. For decades, businesses have tolerated disjointed systems, outdated security measures, and inefficient resource use, assuming fixes could be made later. Now, with AI initiatives demanding immediate results, those assumptions are proving unsustainable.

Recent examples highlight the consequences. A core banking migration project recently left customers locked out for weeks after integration testing failed to account for the complexity of merging legacy systems with new workloads. In another case, GPU servers arrived as isolated units for AI tasks, each with separate storage, networking, and management systems—effectively creating duplicate infrastructure.

Organizations now operate two parallel environments: one for daily operations and another for AI experiments. Data must be transferred between them before and after use, adding maintenance burdens and security vulnerabilities. IBM’s 2025 survey of 1,300 AI leaders found that 81% report technical debt already limits AI success, with performance drops of 18% to 29% and project delays of 15% to 22% when debt is overlooked.

First, the time needed to deliver value extends as systems become more complex. Deploying AI alongside outdated infrastructure requires approvals across multiple systems, delaying outcomes by months. Second, system stability declines. Research from Google’s DevOps team shows AI adoption increases code commits but reduces delivery reliability, raises failure rates, and requires more rework. Third, security weaknesses grow. Agentic AI systems often use inherited permissions from legacy accounts, which are frequently excessive and unmonitored. Finally, infrastructure management breaks down under manual processes. Overwhelmed IT teams dump logs into public AI tools without tracking—a clear sign of losing control.

Most companies recognize the problem but struggle with where to begin. The solution focuses on prioritizing the AI initiative with the greatest business impact. A 2025 survey by a data platform vendor found only 7% of enterprises had data fully prepared for AI, with 56% citing fragmented data as the main barrier. The fix starts with mapping data sources, integrations, and security requirements for that key project. Two or three targeted improvements, such as eliminating data silos or tightening access controls, can drive progress without a full system overhaul. IBM’s findings show that including debt costs in AI planning boosts returns by 29%.

Security requires particular focus. Unlike human users, compromised AI agents cannot be removed. Overly broad permissions turn them into attack vectors. The answer is to treat AI agents as restricted systems: track their activity, limit permissions, and audit access regularly. Pilot programs help test solutions before full deployment, reducing unnecessary infrastructure changes. This approach validates needs and ensures compliance before scaling.

The next critical step for most organizations is not a complete system rewrite but a targeted review. What is blocking the most important AI project? Is it data fragmentation, security flaws, or integration delays? The answers will determine where to invest first. Companies frequently underestimate how much existing infrastructure will hinder AI integration.