How U.S. Companies Can Cut Legacy System Costs in Half by 2026

Legacy systems have long been the unsung workhorses of enterprise operations. They carry institutional knowledge, automate core processes, and provide stability that newer systems often struggle to match. Yet beneath that stability lies a growing financial burden. For many U.S. companies, maintaining legacy systems has become an expense sinkhole—yet the surprising truth is that with the right strategy, those costs can be dramatically reduced.

To understand the scale of the problem, consider that many organizations report spending 60–80 percent of their IT budget just to keep legacy systems running.

CyberDB+3Forbes+3Velosio+3 In some heavily legacy-dependent sectors, like banking and insurance, that figure hovers around 70 percent. Bob's Guide+2Combined Ratio Solutions -+2 Even Deloitte surveys reveal that more than 55 percent of technology budgets go to sustaining existing operations, leaving only a sliver for innovation. Itbrew+1 When you factor in indirect costs—downtime, inefficiencies, security risks, and talent constraints—the true cost is far higher than the maintenance ledger suggests.

A transformation-minded U.S. company can realistically aim to cut legacy system costs in half by 2026. But doing so means moving beyond “patch-and-extend” tactics and embracing a thoughtful, phased approach—one that combines strategic modernization, AI-enabled tools, and operational discipline. The first shift is in mindset: modernization is rarely about wholesale replacement. Large-scale rip-and-replace efforts breed disruption and often overshoot budgets. A smarter path is incremental modernization, where teams refactor modules, introduce APIs, containerize components, and modernize parts of the architecture without tearing everything down at once. This lets value be delivered early, risk be controlled, and costs be smoothed.

One of the most powerful levers in cost reduction is AI and generative tools. According to a McKinsey report, organizations applying gen-AI to legacy modernization have seen 40–50 percent acceleration in project timelines and ~40 percent cost reductions in technology debt expenditures. McKinsey & Company EffectiveSoft (a tech provider) also notes that AI-assisted refactoring and migration deliver measurable declines in maintenance expense while improving code quality. EffectiveSoft AI can scan thousands or millions of lines of code to detect inefficiencies, dead paths, security issues, and duplication—tasks that would otherwise take huge engineering hours. Stefanini+1

But applying AI well isn’t plug-and-play. It requires data readiness, clean codebases, instrumentation, and governance. Legacy systems often harbor fragmented logs, unstructured documentation, and diverse coding styles. To harness AI, teams must invest early in data hygiene, establishing traceability, baseline metrics, and architectural maps. Without that foundation, AI becomes noise rather than insight. Another principle often overlooked is technical debt paydown. As modernization proceeds, part of each sprint should intentionally reduce ceiling debt—refactoring, simplifying, rewriting fragile modules. By embedding debt reduction into the modernization workflow, companies can prevent deferred problems from ballooning into systemic liabilities.

Attention to security and compliance must run in parallel. Legacy systems often contain hidden vulnerabilities, outdated protocols, or weak encryption. Modernizing code also offers the opportunity to strengthen defenses—enforcing stricter access controls, isolating modules, enabling real-time monitoring, and introducing anomaly detection. Ignoring security during modernization is a recipe for blowback. On the operational side, cost savings can emerge from reduced licensing, consolidated infrastructure, and lower support overhead. As systems shift to reusable components, cloud-native services, and modular stacks, redundant platforms can be retired. Vendor lock-in can be loosened. With fewer custom dependencies, hiring and training become easier, reducing premium costs for niche expertise.

One anecdote highlights what’s possible: a financial services company attempted a modernization of its core transaction engine. In prior estimates, a rewrite would have required hundreds of development weeks and a multi-million dollar budget. But by orchestrating gen-AI agents to assist with code discovery, refactoring, and testing, they reduced the project timeline by 40 percent while still improving code quality. McKinsey & Company Such approaches may remain emergent, but they foreshadow what lies ahead. By 2026, companies that treat legacy systems not as sunk cost liabilities but as platforms awaiting transformation will gain competitive advantage. The savings freed from modernization efforts can be reinvested in differentiation—customer experience, AI initiatives, cloud scale, or new product lines.

In practical terms, the path to halving legacy costs will vary by industry, scale, and technology stack. But the pillars remain: begin with foundational audits and data hygiene, adopt incremental modernization, leverage AI thoughtfully, and tie each change to measurable business value. The transformation isn’t instantaneous—but neither does it require a wholesale gamble. Legacy systems won’t disappear overnight, nor should they always be replaced wholesale. What’s required is a shift from reactive maintenance to proactive evolution. Between now and 2026 lies an inflection point. U.S. companies that act thoughtfully, combine automation with human insight, and prioritize modernization will reshape what was once a cost drain into a scalable, future-ready foundation.