What challenges should you expect in application modernization?

• Why legacy systems are hard to modernize
• Common application modernization challenges
  • Technical and architectural risks in app modernization
  • App modernization skill and talent gaps
  • Data migration and integration challenges during app modernization
  • Budget constraints and ROI pressure
  • Avoiding downtime and business disruption
• Most common reasons why application modernization efforts fail

Legacy debt is a lot like the tattoo you got 20 years ago (or 24 hours ago). The system that created the debt seemed like a great idea at the time, but now? Not so much.  

When your organization operates with legacy debt, just like when you’re living with a regrettable tattoo, you have three basic choices:

1. Leave it alone and just live with it. (But you’ve tried that. No dice.)
2. Cover it with something new. (Been there, done that, too. The proverbial modernization can cannot be kicked down the road. Again.)
3. Remove it entirely. (Which can be complicated and sets up its own set of risks.)

Most enterprises have spent years—sometimes decades—weaving applications into the business. Along the way, teams added custom integrations, workarounds, undocumented business rules, and dependencies that few people fully understand anymore.

• A customer service application may support hundreds of workflows.
• A core business application may exchange data with dozens of internal and external systems.
• An application that looks outdated on the surface may still be running critical business processes behind the scenes.

Legacy applications often rely on aging architectures, outdated technologies, and shrinking pools of specialized talent. That makes even small changes more expensive, time-consuming and slower to implement, and riskier than they should be. That's why application modernization requires more than replacing old technology. Organizations must understand how applications support the business, what they connect to, and what could break if changes are made.

Applications designed before modern APIs, event architectures, and machine-accessible workflows become barriers to AI readiness and adoption.

Most application modernization efforts run into the same set of obstacles:

• Legacy architectures that are difficult to change
• Hidden integrations and dependencies
• Limited documentation
• Technical debt accumulated over years of enhancements
• Aging technology skills
• Data migration and synchronization challenges
• Security and compliance requirements
• Budget pressure and competing priorities
• Resistance to changing established processes  

While these challenges seem technical on the surface, they turn into business problems when they slow delivery, increase costs, or create operational risk. Modernization is about creating an environment where humans and AI can work together safely and effectively.

Technical risk comes from hidden complexity. Because many legacy applications were built for a different era, they are rife with hidden complexity. They certainly weren’t designed for cloud-native architectures, API-driven integration, real-time data sharing, modern cybersecurity requirements, or AI-enabled workflows.  

Common architectural constraints include:

• Monolithic application designs
• Point-to-point integrations
• Proprietary technologies
• Vendor lock-in
• Batch-based processing
• Hard-coded business rules
• Limited scalability
• Aging infrastructure dependencies

These constraints create risk because changing one component can create unexpected impacts elsewhere. What’s sold as a simple application upgrade can quickly become an enterprise-wide dependency management exercise.

Application modernization often suffers from both a legacy skills gap and a modern skills gap. And yes, these are two sides of the same coin.

Many organizations depend on a small number of employees who:

1. Understand legacy applications, but who lack expertise in cloud, APIs, DevOps, security, and modern application architecture
2. And vice versa

If institutional knowledge isn't documented before modernization begins, teams risk delays, costly rework, and migration errors. So, enterprises struggle to preserve institutional knowledge (or the budget for maintaining it) while building new capabilities.

Data and integration work often carries more risk than the application migration itself.  

Why?  

Most enterprise applications exchange data with other systems, support downstream reporting, and power critical business processes. When those applications are modernized, data and integrations become some of the highest-risk components of the effort.

Common risks include:

• Data quality issues
• Missing or incomplete records
• Integration failures
• Reporting disruptions
• Data synchronization problems
• Compliance and privacy concerns
• Business process interruptions

Many modernization projects discover that moving the application is easier than moving the ecosystem around it. That's why data and integration assessments should happen early, not after development begins.

Budget constraints force tradeoffs, and that’s not a bad thing. Leaders must decide which applications to modernize first, how to fund the work, and what outcomes matter most.  

Application modernization often competes with cybersecurity investments, AI initiatives, regulatory requirements, and other strategic priorities. As a result, executives expect modernization programs to demonstrate measurable business value right away.  

Technical improvements are table stakes.

The strongest business cases typically connect modernization to outcomes such as:

• Increased revenue
• Lower operating costs
• Faster delivery cycles
• Improved customer experiences
• Reduced security risk
• Greater application reliability
• Increased developer productivity
• AI readiness  

Without a clear business outcome, modernization work can stall with budgetary push-and-pull. And without clear outcomes and demonstrable ROI, leaders can struggle to secure long-term funding.

The safest modernization programs avoid big-bang migrations. Most organizations cannot afford extended outages for customer-facing or mission-critical applications. So, successful teams reduce risk through incremental change.

Common approaches to avoiding downtime and disruption include:

• Phased implementation
• Wave-based migrations
• Automated testing
• Parallel environments
• Incremental releases
• Dependency mapping
• Business continuity planning  

As nice as it might be to modernize every application and its ecosystem at once, it’s more reasonable to modernize continuously and incrementally while keeping the business running.

Most application modernization failures stem from execution mistakes, not technology limitations. The tools, platforms, and cloud architectures for modernization are mature. What's harder—and where projects often go wrong—is understanding dependencies, preserving business logic, sequencing change correctly, and bringing people along through the transformation.

Here are a few of the most common mistakes we see organizations make (or the common mistakes previous vendors have made and that Slalom consultants correct):

• Reason 1: Treating modernization as a tech project instead of a business initiative
• Reason 2: Underestimating the complexity hidden inside legacy systems
• Reason 3: Ignoring the people who keep the system running
• Reason 4: Trying to modernize everything, everywhere, all at once
• Reason 5: Trusting AI blindly—or refusing to use AI at all

Most application modernization programs fail to deliver value because they measure technical progress instead of business outcomes, preserve old ways of working, and lack business ownership from the start.

Many modernization programs start with technical goals like “move to the cloud, upgrade the platform, and refactor the application.” But they overlook the need to define what success will look like after the change.

• How much cost will we remove?
• How much faster will we release software?
• How much downtime will we eliminate?
• How much productivity will we gain?

It’s questions like these that help leaders fund, defend, and prioritize modernization—and keep modernization momentum going.

Many organizations spend millions modernizing applications only to preserve the same inefficient workflows, approvals, handoffs, and operating models. In effect, the technology changes, but the work doesn’t. That's why effective executives focus on workflow redesign, not just application replacement.

Application modernization often begins in IT because IT owns the application. But the outcomes belong to the business. When modernization lacks business sponsorship, business ownership, and business accountability, it becomes easier for stakeholders to view it as "just another technology project.” The most successful programs have business leaders helping define priorities, outcomes, funding, and success metrics from the beginning.

Slalom consultants start with outcomes. Before discussing platforms, architectures, or migration strategies, we work with business and technology leaders to define and align on what success looks like. That means identifying the business metrics that matter most—whether that's reducing operating costs, accelerating product delivery, improving customer experiences, increasing resilience, or creating a foundation for AI adoption.

From there, we look beyond the application into process, operating model, and people challenges. It’s our job to help organizations evaluate how work gets done, find where friction exists, and what needs to change alongside the technology to unlock value and growth.

Most importantly, we bring business and technology stakeholders together from the beginning. By aligning priorities, outcomes, funding, and accountability early, modernization elevates its posture as an IT project competing for attention into a shared, enterprise-wide business initiative.

Many organizations jump straight to code conversion, cloud migration, or platform replacement before aligning on business outcomes. Teams are busy with expensive migrations untied to clear ROI. There’s also a lot of friction around integrations, because there are usually many unknown, undocumented, or infrequently used integrations that show up once transformation is underway. Sadly, modernized technology doesn't solve the original problem, and stakeholder support erodes.  

We start with vision, strategy, and measurable business outcomes before discussing implementation approaches. Modernization succeeds when it's tied to agility, cost reduction, compliance, customer experience, or AI readiness—not because a system is "old."  

Modernization projects regularly fail because organizations focus on technology and ignore workforce anxiety, adoption, and organizational change. One of the biggest causes of failure is failing to engage the people closest to the legacy environment and closest to operating with the new systems once change happens. Even technically successful modernization efforts can struggle if employees continue using legacy processes or resist new ways of working. So, leaders face delicate but critical considerations about upskilling or potentially hiring new talent.

Slalom puts change management alongside engineering. We believe training, communication, stakeholder alignment, and adoption planning are just as important as the technical work. So, we modernize with teams, not to them. The engineers, operators, architects, and business users often hold critical institutional knowledge. They don’t fear change. Usually, they just need to develop trust in their modernization partners. After all, many teams have seen early modernizations fail. It’s only when they're excluded, that modernization becomes a threat rather than an opportunity.  

Many failed programs start with an all-or-nothing vision. This approach is a gamble. Organizations attempt massive migrations, complete rewrites, and multi-year transformations before proving incremental value. With funding challenges, organizations can face high up-front costs, stringing themselves along the long and slow path to value realization. As the world and markets continue evolving, the “final” system can be outdated again by the time it's ready.  

Our AI-enabled tooling allows a different approach. Instead of betting the company on one giant transformation, we help customers move through a series of smart, compounding bets.  

Here’s what that can look like:

• Assess first
• Prioritize high-value workloads
• Validate assumptions
• Deliver value in waves
• Expand from proven success

When you modernize the right things in the right order and align the right people along the way it’s easier to do two things. First, you create measurable business value at every step. And second, you earn trust, buy-in, and funding for “the next right thing.”

Organizations make one of three mistakes:

1. Treating AI-generated outputs as automatically correct
2. Avoiding AI and relying entirely on manual analysis
3. Assuming AI can do everything automatically with no oversight

Modernization is too important for either approach. We don’t believe in extremes, and we operate from a more considered, tempered middle ground.

We combine AI agents with deterministic validation methods. AI helps accelerate discovery, documentation, dependency mapping, and refactoring. Deterministic tooling helps verify critical logic and reduce uncertainty. This combination gives customers speed without sacrificing confidence.

Yes, AI is making application modernization faster by helping teams analyze code, document applications, identify dependencies, and accelerate testing and development. But AI does not eliminate the need for architecture reviews, testing, security controls, governance, or human oversight.  

Organizations that treat AI as a replacement for disciplined modernization practices often introduce new risks while trying to reduce old ones. And organizations that fail to operationalize around AI are already left behind.