The trillion-dollar question: When should legacy applications make way for AI?

If you just read the headlines, it would seem as if AI is now writing all of the world’s code and powering every application businesses run on.

That’s far from true. Just 4 of 33 AI pilots reach production, according to IDC Research — leaving legacy applications still fueling the wheels of commerce. This “silent majority” represents trillions of dollars spent each year on building, maintaining, testing, validating and monitoring legacy applications.

These applications won’t be replaced overnight. Companies and organizations depend on their predictability. The 60-plus-year-old COBOL programming language remains the backbone of banking software for good reason: it is extraordinarily efficient at processing massive transaction volumes with precision. Furthermore, do you want your bank revolutionizing how they manage your money? Probably not.

So, while AI investment continues to build inside the software development lifecycle (SDLC), it isn’t instantly rendering older software obsolete. What it will do is steadily enable easier tweaking, updating and testing of legacy applications — and in some cases, full migrations to modern platforms. And really, this isn’t a new phenomenon. Businesses have always looked to wring more efficiency and profit from existing products through intelligent prioritization.

The argument then is that CIOs and CTOs can take a proactive look at their legacy application portfolios to determine which ones, if any, should migrate sooner. Five considerations can help guide that decision.

Before replacing legacy apps with AI, ask these 5 important questions

1. Does the legacy application still work?

Is its utility still there? Customers often appreciate the consistency of legacy applications. They’re reliable, predictable and well understood. Don’t fix what isn’t broken. Another way to think about this is the degree to which the technical approach of your legacy application is still viable. It’s pretty much a guarantee nowadays in software that an application built one way, with some set of technologies, would be built a totally different way just two to three years later. There is no avoiding that, but what you want to avoid is investing further into a technical approach powering a legacy application that has been completely replaced with new software or a technical approach, especially if it is 10x better across the vectors of software development (latency, cost, accuracy).

2. Does it still make financial sense?

Running a system over a long period amortizes costs significantly. Even as growth rates slow or plateau, it can still be less expensive to let legacy applications run than to overhaul them. Another way to think about this is: how viable is my customer base in the near-term and the long-term? If you anticipate modest—or even flat—earnings growth for your product, then that’s an indicator that it’s possibly worth optimizing your development processes with AI. Where it’s probably not worth investing is when you have no confidence in your future earnings, whether that’s due to the customer base shrinking or commoditization or something else.

3. Can you integrate AI into existing workflows?

A significant portion of upcoming software development lifecycle work will focus on refactoring applications to be more AI-native. Some legacy applications may be strong candidates for a full AI rebuild, while others are better positioned for an AI add-on. Gartner research from 2025 found that only 28% of AI use cases in infrastructure and operations fully succeeded.

Among those that did, success was attributed primarily to integrating AI into existing workflows and systems. “As AI becomes part of day‑to‑day operations, it boosts adoption and creates visible impact within the organization,” Gartner states.

It’s important to keep in mind the distinction between using AI to optimize an existing process or workflow within your application, versus powering a workflow or feature with AI. The former approach is more palatable for legacy applications because it generally doesn’t change the cost profile of running that application. In the latter case, if you’re introducing an AI-powered module into the application, you’re generally going to incur inference costs at runtime, and they are an order of magnitude more expensive for today’s frontier models than base compute.

4. Do you have documented processes for maintaining legacy applications?

If so, you’ll more quickly identify where AI can optimize. The more coherent, organized and detailed processes are, the faster AI can find its footing and drive tangible efficiency gains. If documentation is lacking, start there. Keep detailed instructions and workflows for how you do things. Consistency matters. Don’t do things by heart. Don’t approach tasks casually, and don’t do things differently each time. The more uniform your process, the more easily you can insert AI into discrete steps and achieve efficiencies without disrupting the broader software development lifecycle. The organization in the most precarious position is the one managing legacy applications with no documented process for doing so.

5. Can you prioritize?

Making a change to a piece of legacy software might involve 20 or more steps. Only one or two of those steps may be clear candidates for AI-driven optimization. Identifying and prioritizing those opportunities will help you realize early wins and build the case for broader return on investment. Also, not all candidates for optimization make sense in light of broader financial and operational constraints. As always, prioritize ruthlessly in favor of ROI—bang for your buck. If your team has been struggling to operate a particular part of your system due to a lack of expertise or time, you might consider using AI to buttress the maintenance of that component. Having AI own that part of the workflow might unlock big time savings—or it might erode crucial domain knowledge that your team used to possess through repetition. There is no one-size-fits-all; think through the second-order effects.

Adding AI in testing in the SDLC

Beyond coding and application development, AI is opening new possibilities in how we test software. As leaders examine processes and look for places to insert AI, testing is often a natural entry point. There has been substantial innovation here, including new autonomous AI-driven testing solutions, those that have been enhanced with AI, and hybrid approaches that blend both. Each organization will be at a different place in its AI journey. Testing solutions exist to meet everyone where they are. Also, the state of applications will help determine which approach fits best—and when it fits as you evolve applications.

Of course, there is some substance to the AI hype around how much code AI will write and how many applications it is already creating faster than ever. But one school of thought is that AI’s biggest economic impact will be in the creation of massive new markets and industries rather than in the complete displacement of existing industries. Regardless of how far AI takes us through the universe, it’ll take some time and it’ll be bankrolled by the trillions of dollars of existing products and industries that we depend on every day.

That’s all good news for legacy players, but no one can afford to stay still. AI capabilities are advancing rapidly. Make it a habit to revisit legacy applications and workflows regularly. The right moment to introduce AI will keep shifting, and staying ahead of it is a competitive advantage.

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