Transform legacy systems into AI-fueled innovation engines

In a remarkably short time, progress in the development of generative and agentic AI has made powerful new capabilities available for enterprise technology. Like the wave of digital technology that preceded it, AI will have a profound impact on business operations, technology architectures, and consumer expectations. But to fully benefit from the technology’s potential, leaders need to overcome the technical debt that goes hand in hand with legacy systems.

There is no doubt that AI’s effects are already being felt. Our recent New minds, new markets research found that by 2030, “AI-friendly” consumers (those who prefer and actively seek AI-based experiences) could drive 55% of consumer spending. Today, AI is already helping businesses create more engaging applications, automate processes, boost agility, and enhance employee effectiveness.

AI opportunities are emerging against the context of broader economic uncertainty. A global tariff war has increased the cost of raw materials and components, impacting profit margins. To mitigate these effects, US businesses will need to focus on cost containment of current operations and on redirecting investments to account for new market realities. By focusing on cost reduction, technical debt management and legacy modernization, they can improve operational efficiency, reduce risks and position themselves for sustainable growth in a challenging economic environment. More importantly, they can re-invest the savings in innovation and growth.

• Operational efficiency. Companies will streamline operations and boost productivity, using AI and automation to mitigate the impacts of increased labor costs.
• Technical debt reduction. Organizations will prioritize refactoring code, modernizing infrastructure and leveraging automation to reduce maintenance costs and simplify operations.
• Legacy modernization. Companies will adopt strategies such as rehosting, re-platforming and refactoring legacy systems to react to market fluctuations with agility and scalability.

To win in this new age, businesses must harness AI in the most effective ways possible. We believe the key is domain specialization, in which the enterprise integrates AI models with its proprietary domain knowledge, process models and core systems—creating unique services that are both effective and efficient.

Technology innovation is a necessary step

This shift cannot happen without a new type of systems architecture. AI will be infused into applications, with modular AI agents operating alongside software services to dynamically respond to individual user needs. AI-infused systems will understand user objectives in real-time, gather information and take required actions through APIs. The result: user experiences that are much closer to interacting with an expert human.

For most enterprises, a tall hurdle stands in the way of this progress: legacy systems. These aging systems exist for a reason, of course. They typically meet the operational needs of the business reasonably well (or did until recently); besides, they are complex and risky to replace. But they lack modern capabilities and the flexibility to be fully integrated with AI. The reality is that modern alternatives are far superior—and the rapidly rising importance of AI makes legacy systems’ shortcomings impossible to ignore.

The scale of this problem is difficult to overstate. One 2024 report found tech debt has a crippling effect on innovation and costs the US economy alone $1.52 trillion—and other estimates are even higher. And this is before we consider that many of today’s legacy systems will fade into obsolescence as users demand AI-infused capabilities.

Leaders have already learned that building next generation, AI-infused applications in a legacy system landscape is impracticable—slow and expensive. Functionality is constrained, operational risks are elevated, and development is drastically slowed.

Things get worse when you consider the use cases that are most attractive. Gen AI is uniquely powerful when tasked with unpredictable requests, working across multiple problem areas—that is, exactly the use cases that wreak havoc on legacy systems and data sources.

For these reasons and more, legacy systems should no longer be kept on life support as a problem to be dealt with later. The time to modernize in order to best leverage AI is now.

Changing the legacy modernization calculus

There are two fundamental challenges that make legacy modernization difficult, and AI simplifies both:

• Understanding how the legacy system works. Documentation is often incomplete or non-existent, and understanding the system from its source code is nearly impossible—for humans. But gen AI excels at exactly this type of task, interpreting many obsolete coding languages, summarizing low-level documentation and identifying opportunities to simplify. Cognizant has already created repeatable business rule extraction accelerators for this purpose that can crawl through source code, translate it into natural language and aggregate it into specifications.
  
  
• The development work required to re-create a system can be immense. Generative AI can greatly reduce this effort, particularly helping with predictable and repeatable conversion at scale. For example, we helped a financial institution realize 70% better productivity in the creation of converted code using gen AI compared to the client’s previous approach.

With such significant advances in legacy modernization, previous assessments of this task’s difficulty should be revisited. We re-assessed an insurance client’s modernization plans and found the end-to-end program could be completed in 30% less time, for 30% less implementation cost compared to pre-AI estimates. With much lower cost, risk, and business disruption, modernization efforts that were previously seen as unattractive or low priority may now be viewed as essential.

The next step is deciding which systems to modernize, and in what order.

Opportunity identification and prioritization

AI has unlocked a wide range of modernization opportunities across the technology landscape. Without careful planning, these opportunities can become a fragmented collection of point implementations that don’t scale, interoperate or unlock transformational business value. The systemic approach shown in Figure 1 will identify the most attractive areas and address them in order, and Figure 2 illustrates how AI can accelerate the modernization process.

Industrializing to scale legacy modernization

Given its wide potential, it is likely that enterprises will elect to undergo progressive modernization across a significant proportion of their application portfolio. In such cases, efficiency and effectiveness can be enhanced with modernization factories that use an industrialized approach.

Two distinct modernization patterns exist, requiring different optimizations:

• Technology-led modernization. The desired outcome is an application similar to the legacy version it replaces, moved to modern code/environments. The objective is to maximize the throughput of development teams and minimize dependencies on, or impacts to, broader stakeholder groups. This approach can address AI readiness dependencies in the shortest time.
  
  
• Product-led modernization. The desired outcome is a next-generation experience and business process, transformed by new technology capabilities. The objective is to maximize product impact, forming cross-functional teams to fully understand how technology (and likely AI adoption) can create a better solution. This approach can address AI readiness and incorporate transformational AI (and other) features into modernized applications.

A single modernization center of excellence can support both these models, for multiple applications and modernization initiatives.

• Reverse engineering. In all scenarios, the business must understand what exists today. A single reverse engineering automation platform can use consistent accelerators, adapted to different legacy coding languages.
  
  
• Co-existence of legacy and modern applications can also be managed with a unified approach—handling release, cutover, transaction routing, integrated support, etc.
  
  
• Technology-led modernization will then specialize in approaches that focus on code remediation. Development is predictable, requires only limited stakeholder input and can lean heavily into AI code translation capabilities.
  
  
• On the other hand, product-led modernization typically focuses on business rule extraction followed by acceleration of new feature development using accelerated product engineering, working in cross-functional teams that collaborate deeply with stakeholders.

By combining all four of these elements, it’s possible to create a modernization factory that is highly efficient but also maximizes the business benefits possible from extended modernization initiatives.

Re-assess modernization now to set a foundation for innovation

Legacy modernization is a core pillar of AI readiness, and unaddressed legacy issues will slow down or even derail adoption of AI features into applications. Fortunately, modernization is more achievable now than it has ever been. By overcoming the complexity of legacy tech, a simple, agile, future-ready foundation for innovation can be reached.

Using a structured assessment approach will help leaders identify the right opportunities. Where modernization requirements are significant, a factory-based modernization approach can make use of gen-AI powered accelerators to execute efficiently.

With such an AI-led, automated and industrialized approach, the costs and risks of modernization are greatly reduced. Organizations can act now to overcome decades of technical debt, achieve a modern technology foundation, and position themselves for sustained innovation in the future.

Launch smartly with Cognizant’s Modernization Assessment Workshop. Visit our 'Legacy Modernization' page and register your interest.