3 Reasons Legacy IT May Thwart AI Success

<p><b>We’ve long known that legacy tech puts success and profitability at risk. But AI has raised the stakes even more; the speed and agility leaders demand isn’t achievable without modernization.</b></p>

<p>Eighty-five percent of Global 2000 companies <a href="https://www.cognizant.com/us/en/insights/insights-blog/legacy-modernization-mandate-ai-timeline" target="_blank" rel="noopener noreferrer">doubt their current technology can support AI</a>. That number should alarm every business leader—and it's driving a mandate that can no longer be deferred.</p> <p>The limitations of legacy IT were daunting for cloud and SaaS implementation—yet adding AI to the mix has tipped the scales. Now, IT modernization is at a breaking point. This is something we hear from business leaders often.&nbsp;</p> <p>In many enterprises, critical applications may reside on inflexible equipment that no longer meets the needs of the business, delights users or can be relied upon. Legacy systems have their place, but for many leaders, the artificial intelligence mandate has increased the urgency to modernize. IT modernization has morphed from a strategic discretionary initiative into a mandatory business requirement. You cannot move forward, though, unless you know what's holding you back. Here are three reasons legacy systems may not fully support AI goals.</p> <h4>Reason 1: The skills shortage is real</h4> <p>Programmers and other technology professionals coming into the market today have been trained on AI, cloud and modern software development languages like Python, cloud APIs and AI agents—not COBOL or mainframe commands. Few understand, or want to be trained to understand, how to manage legacy systems, in the era of AI. For enterprises looking to build AI talent, continuing to invest in legacy skills becomes a challenge.</p> <p>At the same time, there are fewer available IT professionals overall in the market, according to the Linux Foundation's 2025 State of Tech Talent report (among other sources). There are currently large and critical talent gaps in the market, and the need for technical skills has ramped up with the growing importance of AI.</p> <p>Our research points out that for technology implementations to succeed, &quot;investment in human capital must parallel, or even precede, investment in the technology itself.&quot; Leaders confirm this concern; half of all executives surveyed doubt they have or can afford the talent needed to meet their modernization goals to support AI.</p> <p>Employees devoted to maintaining legacy systems must be pulled away from more strategic work. This is significant because by spending money on maintenance rather than modernization, a business misses out on the AI multiplier effect—as our report notes, initial investments made to enable AI create ripple effects, generating larger returns and additional use cases with minimal additional cost.</p> <h4>Reason 2: The missing foundation: AI prerequisites are missing from legacy systems</h4> <p>Effective AI requires a modern, cloud-native foundation built for scale, resilience and continuous change. This includes elastic compute, unified and accessible data, real-time processing, modular applications and security embedded by design.</p> <p>Legacy environments were not built with these requirements in mind. They typically rely on rigid infrastructure that cannot scale dynamically, fragmented data that is difficult to access and batch-oriented processes that delay insight. Monolithic architectures slow updates and complicate integration with AI agents and modern services.</p> <p>Security is another concern. Outdated technologies introduce vulnerabilities that are amplified when legacy systems are exposed to modern, connected AI ecosystems. These limitations collectively hinder AI performance, responsiveness and reliability.</p> <h4>Reason 3: Customer connections: If your systems can't talk AI, you're losing the conversation</h4> <p>Customers want to be heard, catered to and supported—now as they always have. But their expectations have shifted dramatically.</p> <p>When someone attempts to make a business-to-business or business-to-consumer purchase, it's very likely that AI is part of the decision-making process. Our <a href="https://www.cognizant.com/us/en/aem-i/new-minds-new-markets-ai-customer-experience" target="_blank" rel="noopener noreferrer">New minds, new markets</a> research found that by 2030, AI-friendly consumers will power more than half of consumer purchasing. And a recent Interactive Advertising Bureau study found &quot;nearly 90% of shoppers say AI helps them discover products they wouldn't have found otherwise, and 64% had AI surface a new product in-session.&quot; Today's buyer journey is intelligent, conversational—and happening on demand.</p> <p>AI-driven engagement requires a shift from static experiences to conversational architectures that support contextualization and personalization. While legacy systems were masterpieces of reliability, they were built as Systems of Record—designed to store data, not to stream it in real time. To meet today's expectations, data must be &quot;unlocked&quot; via modern APIs so AI agents can actually use it as context to drive the conversation.</p> <p>If your systems cannot communicate and interact with AI channels, or your data cannot be harvested to power AI-native services—whether that means simple chatbots, predictive commerce platforms or voice assistants—your business risks becoming obsolete. Organizations that have migrated away from those legacy systems will leave their competition in the dust.</p> <h4>Making the case for legacy modernization</h4> <p>While modernization has always been sought after, execution has been impeded due to long gestation programs and an inability to accurately recreate legacy systems that have been updated over a period of time. But here, too, AI assistants are proving to be effective across the board—be it in terms of reverse-engineering legacy, forward-engineering to target state or data migration. AI code assistants compress the time and budget required for legacy modernization, thereby making it viable for enterprises to drive business value with speed to market.</p> <p>We see many forward-thinking clients using AI to fund and direct modernization. They are tapping the technology to help them find the way forward to the highest business impact. They're also using AI to create and track KPIs so they can be sure every project has a fast and significant ROI. In the end, legacy modernization isn't just about &quot;lifting and shifting&quot; data and equipment. It's a way to ensure an organization stays secure, agile and relevant in a world that prioritizes AI.</p> <p>To be sure, many business leaders have a grasp on both the challenge and the stakes. Nevertheless, execution remains a problem. Navigating budgets, shifting priorities and complex approval cycles can keep modernization always slightly out of reach. But make no mistake: It's time to speed up the process. Without modernization, enterprises can't meet business innovation goals, and no amount of patching or tooling can bring that to fruition.</p> <p><i>To learn more, visit our </i><a href="https://www.cognizant.com/us/en/services/core-application-modernization" target="_blank"><i>Core Modernization page</i></a><i>.</i></p>