Enterprise modernization has often been treated as a long-running technology agenda. Organizations knew they needed to address legacy applications, fragmented data and technical debt, but many were able to defer the problem by building around it. Interfaces, integrations and temporary workarounds kept business systems running—until AI.
For AI to move from experimentation to industrial-scale deployment, modernization and AI implementation need to run in parallel and be treated as interdependent priorities. AI needs access to data, and applications and processes that are agile and connected to support new ways of working at speed.
AI-native companies are changing expectations for how quickly new capabilities can be built and released. This matters because if a business in any sector can introduce game changing functionality in weeks, their competitor does not have the luxury of time to deliver a new feature. Businesses in highly competitive markets cannot choose to let core applications, data architecture or delivery models hold them back.
This is why modernization is fundamental to an enterprise leverage the impact of AI.
Legacy ignored is an obstacle to AI readiness
HCLTech’s latest research, The AI Impact Imperatives, 2026, found that 51% of enterprise applications are still legacy. Many organizations have inherited complex application estates accumulated over many years through acquisitions, regional expansion, custom development and representing layers of change to the business.
The issue is not simply that these applications are old, slow and difficult to maintain. It is that they often reflect the history of the business rather than the future the business needs to build.
Legacy applications contain years of accumulated features, duplicated functions, unused code and embedded processes that do not reflect how the organization should or needs to operate in today’s AI era. In many modernization assessments, enterprises find they are maintaining far more capability than they actively use. The cost is technical debt and business drag.
Modernization cannot be oversimplified to a lift-and-shift of legacy code to minimally reduce technical debt. Moving an old system to a new platform may reduce some infrastructure burden, but it does not automatically create a more agile business. The greater opportunity is to understand what the application does today, identify what the business needs to be successful in the present and the future and reimagine the system accordingly.
The imperative is to leverage modernization as a business redesign opportunity, not a technology refresh.
Data modernization enables the AI ready applications
The research revealed that only 21% of respondents feel their data estate is modernized and operational to support AI. Considering that 60% of respondents report issues with data visibility and automating data orchestration, 58% with intelligent data management, 55% with consolidating data platforms and 51% with building consumable data products—data modernization is an obstacle to achieving success with implementing AI.
AI depends on data that can be accessed, understood, governed and used in context. If data is locked inside legacy systems, scattered across platforms or difficult to orchestrate, AI cannot deliver the level of real-time decisioning, automation or intelligence that business leaders expect.
The report also illustrates that data modernization is central to AI outcomes, with 87% saying that improving data quality is critical or important to increasing the accuracy and reliability of AI models, while 84% say implementing real-time or streaming data is critical or important to enabling AI-driven, in-the-moment decisions.
In practice, this means AI programs cannot sit separately from the teams responsible for core applications and data foundations. Collaboration and cooperation between AI teams and legacy teams are two important AI imperatives. Modernization, data readiness and AI implementation need to move as one agenda engaging multiple interdependent teams.
Reimagine rather than rebuild
AI-led modernization changes the economics and pace of transformation because it allows organizations to analyze, understand and rebuild applications that reflect new ways of working.
The starting point is to reverse engineer what exists today: the code, documentation, data flows, business rules and dependencies. From there, enterprises can engineer a more agile, cloud-native and AI-ready application. In many cases, that means decomposing monoliths into microservices, redesigning data architecture, removing unused code and simplifying the business process behind the system.
This is where leaders need to make practical choices. Not every application should be rebuilt. In one case, the better answer may be to retire a custom system and replace with existing, modern SaaS capability. In another case, it may be to extend an enterprise platform that already contains the functionality the business needs. The goal is to create a simpler, faster and more intelligent operating environment.
The research also shows that AI is a necessary part of that journey. 75% of respondents agree their organization will rely on AI tools to unlock the productivity and efficiency required to achieve modernization goals and another 69% agree that manual work required by their current approach can be automated using AI.
This story doesn’t end with greater productivity. If AI is used only to write code faster, the enterprise misses out on the greater opportunity. Code generation is one part of the equation; but AI-enabled engineering of the target architecture redesigns process, improves data access and ensures the new system will continuously evolve with the needs of the business.
The changing economics of transformation
The research quantifies the impact of modernization on the enterprise. The median application modernization project involves 8.5 developer FTEs and takes around 39.5 weeks to complete. Assuming a burdened labor rate of $125,000 per developer resource, the modeled labor cost of this application modernization project stands at approximately $807,000.
This example illustrates two areas of dissatisfaction:
- That modernization takes too long and is too expensive.
- AI-enabled modernization can change the business case.
If the goal is simply to replace one database with another or move existing logic into a new environment, the value will be limited. If the goal is to reimagine the business process, simplify the application estate and create a more AI-ready foundation, the economics look very different.
Modernized applications and data support higher transaction volumes, accelerate feature releases, enable a better customer experiences and more agile operations. In areas such as order-to-cash, for example, the value is not only reducing the cost of maintaining the application, rather redesigning the process enables the business to operate faster, reduce friction and improve responsiveness.
To adequately measure modernization programs, leaders should look beyond time and material inputs and focus on business outcomes: faster release cycles, higher transaction capacity, improved user experience, lower operational cost and the ability to respond to market change.
AI changes the operating discipline
Modernizing with AI changes how applications will be maintained.
If an application is generated, refactored or modernized using AI, it cannot be maintained by relying on traditional development practices that created the original technical debt. The enterprise must embrace a new engineering discipline. The new operating model must include AI-assisted development, testing, documentation, vulnerability scanning and quality controls.
Ignoring this change introduces the risk of recreating the same problems in a new environment. Poor code quality, inconsistent maintenance, duplicated functions and manual workarounds can return quickly if teams do not adopt new ways of working.
Ultimately, “AI at the core” is not a slogan. It means that AI must be embedded into an organization’s foundation represented by the people, processes, technology and delivery culture. The teams responsible for legacy applications and the teams driving AI implementation need a shared modernization agenda, shared expectations and a common understanding of how AI-enabled delivery changes the work itself.
The research responses reflect this need for coordination, with 82% of organizations starting to collaborate cross-functionally to determine how to best utilize AI technologies. 80% have sought external consultation from AI experts, systems integrators or consultants and report a greater likelihood of positive outcomes.
Prioritizing business agility
One of the biggest mistakes leaders make is assessing the value of modernization by quantifying the reduction of technical debt. Technical debt is real, but it is the starting point for a broader conversation and strategy.
Questions like these are equally important. They move the discussion beyond technical debt alone and toward whether the business is becoming agile enough to compete, adapt and deliver the AI-enabled experiences that customers and employees now expect.
Is the business agile enough to compete in an AI-driven market? Can it launch new capabilities quickly? Can it adapt business processes without months of rework? Can it connect data across systems? Can it give employees and customers the intelligent experiences they now expect? Can it maintain AI-enabled applications with the right quality, security and discipline?
The research reveals that 52% of organizations report losing competitive advantage because their modernization timeline cannot keep pace with market demands. Another 76% agree they need a third-party system integrator or consultant to help get the organization to its desired modernization end state.
Enterprises that modernize only to reduce cost may achieve short-term savings, but enterprises that modernize to be AI-ready are creating a more durable advantage—long term dominance in their market.
The next phase of AI impact will be shaped by the organizations with foundations that put AI to work across the business at speed, scale and quality. This means modernization and AI deployment must move in parallel. Organizations that understand this will prioritize modernization as fundamental to realizing the full impact of AI.
