After working with enterprise teams on modernization initiatives throughout 2025, we’re seeing a clear shift in how organizations approach legacy transformation. The hype cycles have matured. The budgets have tightened. And the focus has moved from technology experimentation to practical execution.
Here are seven application modernization trends defining enterprise IT strategy in 2026, based on what we’re seeing across customer engagements.
1. Modernizing for AI readiness
A driver of application modernization that didn’t exist a few years ago but is now a core CIO concern: AI enablement. Organizations are modernizing legacy systems specifically to unlock AI use cases that their current architecture can’t support.
What’s actually happening:
Legacy systems weren’t designed for AI integration. They lack the APIs needed to feed data to AI models, the real-time data access that AI applications require, and the modular architecture that allows AI components to be added incrementally. Organizations are discovering that their AI ambitions are blocked by legacy constraints.
What’s gaining traction:
The conversation has shifted from “should we modernize?” to “we need to modernize to enable AI.” This creates urgency that previous modernization drivers lacked. When modernization is framed as a prerequisite for competitive AI capabilities, it moves from a technical initiative to a strategic imperative.
Organizations leading this shift are taking inventory of AI use cases they want to enable, mapping those use cases to the legacy systems blocking them, and prioritizing modernization based on AI enablement potential rather than technical debt alone.
The pattern we’re seeing: Teams that start with “what AI capabilities do we need?” and work backward to “what must we modernize to get there?” are moving faster than teams still framing modernization as infrastructure improvement.
2. AI-assisted modernization workflows
AI isn’t just the destination for modernization. It’s becoming the tool that makes modernization possible at scale. The most significant shift in application modernization for 2026 is using AI to accelerate the modernization process itself.
What’s actually happening:
AI tools are automating the most time-consuming parts of the modernization workflow: planning, understanding, building, testing, and monitoring. Stages that historically stretched modernization projects into multi-year efforts are being compressed dramatically. The promise is that AI handles the labor-intensive work, reducing the human effort required at each stage of the pipeline.
What’s gaining traction:
Hybrid AI approaches that combine deterministic and generative processes are emerging as the enterprise standard. Deterministic analysis provides verifiable, traceable insights grounded in actual code. Generative AI accelerates the creative work of designing solutions and producing artifacts. The combination delivers both accuracy and speed across the modernization workflow.
This shift reflects hard-won lessons. Early AI tools that promised to “automatically modernize legacy code” produced output requiring extensive human review and correction. Organizations burned by inconsistent results now prioritize approaches with verifiable, repeatable outputs that can be validated before full deployment.
The pattern we’re seeing: Organizations report significant reductions in initial assessment timelines when using AI-assisted analysis tools compared to manual code review. The teams succeeding aren’t replacing human judgment with AI. They’re using AI to surface the information humans need to make better decisions faster.
3. The skills gap isn’t going away
This isn’t a new trend – it’s a persistent reality that keeps getting worse. Organizations running 60-year-old technology face a shrinking pool of people who are experts in it, and nothing has fundamentally changed that equation.
What’s actually happening:
The engineers who built and maintained mainframe systems are retiring. The next generation wasn’t trained on COBOL, JCL, or legacy architectures – and they don’t want to be. You can’t hire your way out of this problem. The expertise is leaving, and it’s not being replaced.
This creates a modernization forcing function that has nothing to do with digital transformation ambitions. Some organizations are modernizing not because they want to, but because they literally cannot staff the maintenance of what they have.
What’s gaining traction:
AI was supposed to help here – and to some extent it does. AI tools can assist with code comprehension, generate documentation, and help newer engineers work with legacy systems faster. But AI hasn’t fundamentally solved the problem. The deep institutional knowledge of why systems were built the way they were, the undocumented business rules, the edge cases that only a 30-year veteran knows – that knowledge is still walking out the door.
Organizations are responding with knowledge capture initiatives, trying to extract and preserve understanding before experts leave. But this is a race against time, and many organizations started too late.
The pattern we’re seeing: The skills gap will remain a driver of modernization in 2026, 2027, and beyond. Until something fundamentally changes – whether AI reaches a new capability threshold or organizations find new ways to transfer institutional knowledge – this pressure only intensifies.
4. Understanding as the foundation for all modernization
Every modernization approach, whether rehost, replatform, refactor, or rebuild, shares a common prerequisite: understanding what you have. In 2026, this understanding phase is being recognized as the core element of modernization success.
What’s actually happening:
Organizations that don’t successfully build an understanding of their existing application consistently report lower satisfaction with modernization outcomes. They containerize applications without understanding dependencies. They design APIs without understanding legacy behavior. They decompose monoliths without understanding where the real boundaries should be.
What’s gaining traction:
The industry is converging on a truth that experienced practitioners have known for years: you can’t transform what you don’t understand. This is driving investment in automated code analysis that surfaces business logic, dependency mapping that reveals hidden connections, business rule extraction that documents what systems actually do, and knowledge capture that preserves institutional understanding.
For organizations modernizing mainframe applications, understanding COBOL business logic is particularly critical. These systems often contain decades of accumulated rules governing core business processes, from payment validation to regulatory compliance.
The pattern we’re seeing: The organizations that are succeeding invest in understanding their legacy systems deeply before deciding what to change.
5. Agentic AI is the new hype
Just as organizations developed realistic expectations about AI assistants, a new wave of hype has arrived: agentic AI. Vendors are positioning autonomous agents as the solution to modernization complexity. The pitch is compelling – and deserves the same scrutiny that earlier AI promises required.
What’s actually happening:
Agentic AI differs from AI assistants in a fundamental way. Assistants respond to prompts and require humans to orchestrate each step. Agents can plan, execute, and adapt across multi-step workflows – analyzing code, generating transformation artifacts, running tests, and iterating based on results. For modernization, this means potentially moving from “AI helps humans work faster” to “AI handles entire workflow stages autonomously.”
The marketing claims are already ahead of the reality. Vendors are describing agent capabilities that work in demos but struggle in production environments with legacy complexity, edge cases, and enterprise-scale codebases.
What’s gaining traction:
Smart organizations are applying the lessons they learned from the last AI hype cycle. They’re asking harder questions: Where can agents operate with verifiable, repeatable results today? What guardrails and human oversight do they need? How do we measure whether an agent is actually reducing effort versus creating new review burdens?
The most promising applications are bounded, well-defined tasks where agent autonomy can be validated: automated test generation, documentation maintenance, and specific code analysis workflows. Full end-to-end modernization agents remain largely aspirational.
The pattern we’re seeing: Organizations that navigated the first AI hype cycle successfully are better positioned to evaluate agentic claims. They’re neither dismissing agents nor buying the marketing – they’re running controlled evaluations to find where agents genuinely add value.
6. Tighter budgets, harder questions
The era of experimental modernization budgets is over. After years of funding pilots that never reached production, finance teams and executive leadership are applying new scrutiny to modernization investments.
What’s actually happening:
Organizations spent heavily on pilots – in both money and time – with few quantifiable modernization results to show for it. The proof-of-concept phase stretched indefinitely while legacy systems continued running in production. Now budget holders are asking why, and they’re not satisfied with “we learned a lot.”
What’s gaining traction:
Procurement conversations have fundamentally changed. Decision-makers are demanding answers to harder questions: What’s the total cost of ownership beyond the pilot phase? How does this integrate with our existing tools and workflows? Can we staff this internally, or are we creating vendor dependency? What happens when the vendor’s pricing changes?
This scrutiny is reshaping vendor relationships. Organizations are consolidating around fewer vendors rather than spreading bets across experimental tools. The application modernization patterns that succeed are the ones where organizations can clearly articulate ROI before investment, not after.
The pattern we’re seeing: Budget approval now requires demonstrating production readiness, not just proof-of-concept capability. The “let’s try this and see” era has ended.
7. The rise of the modernization factory
The most sophisticated organizations are making a strategic shift: recognizing that modernization isn’t a project with an end date. It’s a permanent organizational capability. This realization is driving the “modernization factory” model.
What’s actually happening:
Legacy systems don’t stop accumulating. Today’s modern architecture becomes tomorrow’s technical debt. Organizations that treated modernization as a one-time initiative are discovering they need to modernize again, and again, and again. The project mindset doesn’t survive this reality.
What’s gaining traction:
The factory model reframes modernization as an ongoing function, like security or infrastructure. Instead of asking “when will we be done modernizing?” organizations ask “how many applications can we modernize per quarter?” and “what’s our velocity through the modernization pipeline?”
This shifts the metrics entirely. Success isn’t measured by completing a single high-profile transformation. It’s measured by throughput: how many applications move through the assessment, planning, execution, and validation stages. How quickly can a new application enter the pipeline and emerge modernized? What’s the success rate?
The 7 approaches to mainframe modernization become lanes in the factory, not competing strategies. An application gets routed to the appropriate lane based on its characteristics, then moves through standardized stages with clear handoffs.
The pattern we’re seeing: Organizations building modernization factories are assembling best-in-class components rather than buying end-to-end solutions from a single vendor. They want flexibility to improve and customize each stage of the pipeline independently – whether by building in house or buying software tools to integrate into the process.
What these trends have in common
Seven different trends, one common thread: the industry is learning to separate signal from noise.
- AI readiness is driving modernization urgency with concrete business cases
- AI-assisted workflows are accelerating analysis without replacing human judgment
- The skills gap keeps getting worse – and nothing has fundamentally solved it
- Understanding is recognized as the foundation, not an optional phase
- Agentic AI is generating new hype – and organizations are applying hard-won skepticism
- Tighter budgets are forcing focus on demonstrable ROI
- Factory approaches are making modernization systematic
The organizations that will succeed at modernization in 2026 aren’t ignoring new technologies – they’re evaluating them rigorously. They’re building repeatable capabilities based on deep understanding of their legacy systems, then selectively adopting tools that genuinely accelerate those capabilities.
Where we’re headed
The trajectory is clear: application modernization is becoming a core organizational capability rather than a periodic initiative. The organizations building that capability systematically – with AI-assisted tooling, standardized processes, and a foundation of deep system understanding – will transform their portfolios faster and more reliably than those still treating each modernization as a unique project.
New hype cycles will keep arriving. The organizations that thrive will be the ones that learned how to evaluate them.
