For years, technical debt has been treated as something organizations could manage over time and chip away at between larger priorities. That’s no longer the case.
Today, technical debt is actively working against transformation. It’s slowing down cloud adoption, increasing costs, and limiting how quickly organizations can respond to change. And in many cases, it’s becoming the biggest barrier to modernization itself.
At Perficient, we’re seeing this challenge play out across industries as organizations face increasing pressure to modernize infrastructure, applications, and development practices without disrupting the business.
What has changed is the scale, but also the urgency.
Infrastructure, Applications, and Legacy Systems Are Breaking in Different Ways
Across enterprise environments, multiple pressure points are converging.
The first is happening at the infrastructure layer. Broadcom’s acquisition of VMware has forced organizations to re-evaluate long-standing virtualization strategies almost overnight. Pricing models have shifted, licensing costs have increased dramatically, and systems that once felt stable are now under scrutiny.
For many teams, this isn’t theoretical anymore. The cost implications are real with approaching renewal cycles.
The second pressure point is happening within application portfolios. Organizations still running large volumes of .NET Framework applications are facing a different kind of constraint. These applications are tied to Windows Server, which brings ongoing licensing costs, aging frameworks, and limited ability to take advantage of modern, cloud-native services.
For some organizations, the challenge goes even deeper. Mission-critical mainframe systems continue to consume large portions of IT budgets while relying on shrinking pools of specialized expertise. Others are struggling with custom-built applications that have accumulated years of technical debt and complexity, making modernization difficult to scale.
Individually, these challenges are difficult but manageable. Together, they create a compounding effect:
- Higher costs at the infrastructure level
- Slower innovation at the application layer
- Limited ability to move forward without addressing both
This is where many modernization efforts stall.
Why Traditional Approaches Can’t Keep Up
The problem isn’t a lack of tools or ambition, but rather the model itself. For years, modernization has followed a familiar pattern: Assess the environment –> Build a plan –> Execute in phases –> Repeat
It works but it doesn’t scale.
Large VMware estates can take 18 to 36 months to migrate using traditional approaches. Application modernization often takes even longer, because it happens one system at a time.
By the time organizations complete one phase, the environment has already evolved, costs have increased, priorities have shifted, and technical debt continues to grow.
Most organizations don’t struggle to define a modernization strategy; they struggle to execute it at scale.
—Larry Cusick, Senior Solutions Architect
What’s been missing is a way to compress timelines and remove the manual bottlenecks that slow everything down.
What AI Changes
This is where Perficient and AWS start to shift the equation.
Rather than treating modernization as a manual, step-by-step process, AWS Transform introduces an AI-driven model that automates many of the most time-intensive tasks:
- Discovering infrastructure and application dependencies
- Analyzing environments and identifying risks
- Planning migration waves based on real relationships between systems
- Translating complex environments into AWS-native architectures
Instead of large teams manually coordinating every step, the AI handles the repetitive work while teams focus on decision-making and direction.
This distinction doesn’t eliminate the need for expertise. It changes where that expertise is applied. At Perficient, we see organizations getting the greatest value when AI-driven automation is paired with experienced architects, engineers, and modernization teams who can guide strategy, governance, sequencing, and implementation.
AI enables modernization teams to move at the speed the business now requires.
—Steve Holstad, VP AWS Practice
Modernization itself is no longer one-size-fits-all. Organizations are evaluating everything from VMware estates and .NET portfolios to custom applications and broader transformation initiatives. The common challenge remains the same: finding a scalable path from strategy to execution.
Organizations Are Putting Theory Into Action
What we’re seeing in practice is that organizations are starting where the pressure is greatest and where Perficient can create immediate impact. While modernization needs vary by organization, a few common entry points are emerging across infrastructure, applications, and legacy platforms.
- VMware Modernization
For infrastructure teams facing rising VMware costs, the priority is speed. Perficient and AWS enable organizations to move from analysis to execution much faster than before. Instead of spending months planning migration waves, teams can begin moving workloads to AWS in a matter of weeks.
For many organizations, VMware licensing changes have become the forcing function that finally moved modernization from a future initiative to an immediate business priority.
The goal is momentum, not just migration. Early wins create the foundation for scaling the approach across the rest of the environment.
- Windows and .NET Modernization
On the application side, the challenge has always been scale. Modernizing a single application is achievable, but modernizing hundreds with traditional methods is not.
Perficient and AWS change that by automating large portions of the code analysis and transformation process. What once required deep, specialized expertise for every application can now be executed more consistently across a portfolio.
At the same time, organizations are looking to reduce Windows and SQL Server licensing costs while creating a foundation for cloud-native development, containers, and modern runtimes.
The result is a shift from sequential modernization to parallel execution and individual apps to portfolio-level transformation.
- Mainframe Modernization
Organizations running mainframe environments face a unique challenge: balancing decades of business-critical functionality with growing cost and talent pressures. Traditional modernization approaches can take years and carry significant risk.
AI-driven modernization is helping teams accelerate code analysis, documentation, business logic extraction, and transformation planning, making mainframe modernization more achievable than it has been in the past.
- Custom Application Transformation
Many organizations are also finding that their biggest modernization roadblocks are not commercial platforms, but the custom applications built over years of growth, acquisitions, and changing business requirements.
By applying AI-assisted transformation patterns to application portfolios, teams can modernize APIs, frameworks, runtimes, and architectures more consistently and at greater scale than traditional manual approaches allow.
The Bigger Shift: From Planning to Execution
The most important change is an organization’s mindset. Those who are making progress aren’t waiting to fully de-risk modernization before they begin. They’re using approaches like AI to:
- Prove value quickly
- Establish repeatable models
- Build confidence with real outcomes
Instead of treating modernization as a multi-year program, they’re treating it as a series of focused, accelerated initiatives. AI is accelerating modernization, but technology alone doesn’t guarantee outcomes. Organizations still need experienced partners to align business priorities, define modernization paths, and translate recommendations into production-ready solutions.
At Perficient, we combine AWS Transform capabilities with deep migration and modernization experience to help organizations move from planning to measurable progress faster.
Where This Leads Next
Technical debt isn’t going away, but the way organizations address it is starting to change.
AI-driven approaches are making it possible to move faster, scale more effectively, and close the gap between strategy and execution.
From accelerating VMware migrations and transforming .NET application portfolios to modernizing mainframe systems and custom applications, organizations are already using Perficient to turn strategy into execution.
Whether you’re evaluating VMware migration, Windows and .NET modernization, mainframe transformation, or custom application modernization, the first step is understanding where AI-driven transformation can create the greatest impact.
Click here to explore how Perficient and AWS are helping organizations modernize faster.
