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Learn about the latest application modernization trends and practical advice for achieving success with legacy optimization & transformation initiatives.

Application Modernization

Modernizing Legacy Applications Without Business Disruption

Article summary: Modernizing legacy applications without disrupting business operations is one of the most pressing challenges organizations face today. Successful modernization starts with thorough planning and stakeholder alignment — understanding system dependencies, documenting existing processes, and ensuring all parties agree on what to preserve, update, or remove. Rather than a risky “big-bang” approach, a phased, incremental strategy with pilot projects and intermediate checkpoints significantly reduces the chance of failure. It’s also critical to keep legacy systems synchronized with modernization work throughout the process, since codebases continue to evolve and any missed updates can result in outdated functionality or compliance violations. Ultimately, modernization should be viewed as a continuous journey rather than a one-time project, requiring ongoing attention, automation, and a commitment to business continuity at every stage.

A recent industry report revealed that 92% of businesses surveyed are still relying on outdated technologies. While 41% of these organizations plan to modernize their applications in the next 12 months, there are still quite a few challenges and questions that come up along the way. As your go-to partner for application transformation, we are here to help with our latest blog series – “The Modernization Playbook”, which will be a series of posts addressing some of the top modernization implementation challenges we’re hearing and practical tips for how to overcome them. For this inaugural post, we’ll start with one of the most frequently asked questions:

How can we modernize legacy applications without disrupting business operations?

Almost every core aspect of running an organization today depends on sophisticated software systems. From financial operations and resource planning to talent management and communication, organizational tech stacks continue to expand—enterprises with at least 5,000 employees now manage nearly 400 applications on average, with this number increasing proportionally to organization size. If these mission-critical systems succumb to legacy risks or failures, business operations can grind to a halt.

However, modernizing legacy applications remains challenging, and even with cutting-edge automation tools, these projects often span months or years depending on system complexity and scope. Adding evolving regulations and compliance requirements to the mix, organizations must consider how to keep modernization efforts aligned with rapidly changing production environments. As IT teams deploy updated systems, they may encounter resistance from team members who have optimized their workflows around existing environments. How, then, can IT departments create a successful modernization strategy and achieve their objectives while minimizing disruption to business stakeholders and end clients?

Communicate, Collaborate and Double Down on the Details

Taking the time to properly plan a transformation initiative and devise a robust modernization strategy is imperative. This begins with aligning with your business and technical stakeholders to determine the goals of the overall transformation effort, required deadlines, available resources, and other critical factors. The more comprehensive your understanding of business needs, the better equipped you’ll be to create a plan that meets those requirements both during the modernization process and after completion.

Equally vital is conducting a thorough assessment of the application(s) in question. Understanding system dependencies will help identify which other applications will be affected by the modernization initiative and which must integrate with the modernized solution. Additionally, comprehensively documenting the policies and processes implemented in the current system and communicating these to business stakeholders enables clear decisions about what functionality to preserve, update, or remove. This ensures that when new applications are deployed, they execute the policies that align with business requirements rather than causing disruptions through incorrect or outdated processes.

Master the Blueprint Before Building the Future

The failures associated with a “big-bang” approach to application modernization are well-documented. The risks of an “all or nothing” strategy—with no intermediate checkpoints or validation mechanisms—are substantial, particularly when it comes to system failures or disruptions. Despite the urgency around modernizing legacy applications, taking time to establish a foundation for success yields better outcomes. This is where a phased approach or incremental modernization strategy proves invaluable.

Using our proven 5-step methodology, organizations have successfully leveraged comprehensive assessments to divide large modernization initiatives into manageable segments that align with business requirements. Specific modernization paths can be selected for each component, with processes validated through pilot projects featuring intermediate deliverables. Business stakeholders should be kept in the loop during these pilot projects to ensure continued alignment. Once a successful modernization strategy emerges, incorporating lessons from these pilot implementations, organizations can confidently scale their modernization efforts with significantly reduced risk. Take for example a leading healthcare insurer that used the Intellisys platform to split their move from aging systems to modern platforms into multiple phases using the platform’s business rules extraction capabilities. They successfully extracted rules from over 1.5 million lines of COBOL code and exported the relevant rules into the Drools rule engine in 8 months.

Maintain Synchronization between Legacy Systems and Modernization Work

Legacy systems must remain operational while their modern counterparts are being developed and tested. These systems require ongoing maintenance and updates to address policy changes, security requirements, compliance mandates, and other essential modifications. Most organizations cannot simply freeze their codebase, especially during extensive modernization initiatives that may span months or years. However, without properly capturing, documenting, and sharing changes made to legacy code with the modernization team, organizations risk developing a technically modern but functionally outdated application that fails to incorporate critical updates. This oversight could lead to businesses executing obsolete processes or facing compliance violations.

The Intellisys platform stands alone in addressing this challenge through its agile documentation and business rules extraction capabilities for modernizing legacy applications. Using fully automated processes, the platform analyzes newer versions of application source code and identifies changes compared to previous iterations. It goes further by generating comprehensive reports highlighting where these modifications may impact existing modernization work. This functionality enables modernization teams to remain synchronized with changes made to production systems and incorporate these updates into modernized versions. The State of New York successfully leveraged these features during their initiative to extract rules from their legacy Integrated Eligibility System (IES) as part of a transformation effort. This project, spanning three years and encompassing 31 million lines of code across three programming languages, successfully incorporated all critical updates made during the modernization timeframe.

Embrace Continuous Modernization for Long-Term Success

Modernizing legacy applications without disrupting business operations requires thorough planning, incremental implementation, and synchronization between legacy system updates and modernization work being performed. By communicating effectively with stakeholders, thoroughly assessing the systems in question, breaking down large initiatives into manageable segments, and leveraging automation tools like the Intellisys platform, organizations can successfully navigate the complexities of these initiatives. Remember that modernization should not be looked at as a one-time event but rather an ongoing journey—one that requires attention to detail, strategic foresight, and a commitment to maintaining business continuity throughout the process. As you embark on your own modernization journey, focus on creating a sustainable approach that evolves alongside your business needs, ensuring that your technology stack remains a competitive advantage rather than a liability.

Application Assessment Application Modernization Documentation & Analysis

COBOL Code Red: 3 Strategies to Enable Government Modernization

2025 has emerged as the “efficiency and effectiveness year” for federal IT. Earlier this year, COBOL regained attention when the Department of Government Efficiency (DOGE) alleged inefficiencies in Social Security Administration (SSA) systems, claiming to find 150-year-old benefit recipients. These findings were later attributed to COBOL’s date representation format and known data entry errors already being addressed. The core issue remains the knowledge gap between newer talent and legacy systems. With COBOL no longer taught to recent engineering graduates and approximately 30% of SSA’s CIO team eligible for retirement, DOGE’s targeted cuts to these experienced workers is concerning. These systems in addition to systems run by other government agencies provide essential benefits to millions of Americans, and allowing this institutional knowledge to “walk out the door” without a maintenance and upgrade strategy is unsustainable.

The need for system assessment and modernization across federal agencies is not new, so why haven’t the IT teams at these agencies moved forward? Lack of resources, time investment and the scars from previous failures have all influenced the desire to “not fix what isn’t broken.” But time is quickly running out. So let’s take a look at 3 key challenges that agencies have faced when attempting to modernize and see how lessons learned from federal and state government peers may be able to help.

Modernizing custom-built government applications that have minimal documentation

Federal government agencies operate thousands of applications built and maintained with custom code and technical workarounds. At the SSA alone, 3,600 applications span 10 regional offices, each developing unique system maintenance methods. Many of these systems still operate off of legacy programming languages. For example at the SSA, approximately 60 million lines of COBOL code support core business functions, and the IRS has approximately 160 applications written in COBOL code as well as core applications written with IBM Assembler. With inadequate documentation and knowledgeable talent departing, the risk of system failure grows substantially.

Agencies can address this challenge by leveraging automated tools that provide comprehensive, current documentation without needing to rely on support staff. Solutions like Intellisys use AI/ML technologies to reverse-engineer source code in 20+ programming languages, generating artifacts including assessment reports, system diagrams, and business logic representations that help personnel understand system operations. This simplifies not only the maintenance of legacy systems but also facilitates successful government modernization efforts when an agency is ready. For instance, a critical Department of Defense (DoD) recruitment system had been compromised due to unsupported technologies and legacy components, with no subject matter experts available to help. The application was successfully documented and converted to a modern Java application without run-time dependencies in just 6.5 months using the Intellisys platform.

Today, system maintenance and modernization must be considered continuous exercises with the rapid rate of change of technology. That being the case, keeping a set of up-to-date documentation particularly for custom systems is critical to avoid a reprisal of the problems agencies are facing today. Finding a tool that supports automated documentation generation for both modern languages as well as legacy languages allows agencies to use one solution both now and in the future.

Implementing government modernization within strict procurement and budgeting cycles

Government procurement and budgeting cycles can be notoriously difficult. Not only are there multiple hoops to jump through but agencies must submit budget proposals for multi-year projects that may not include insights needed from the preliminary parts of modernization. If those budgets turn out to be inadequate, then the process of explaining the shortfall and putting together a new budget must start. Previously, modernization was approached as a one-time “big-bang” effort where IT teams attempted to transform systems in one go. However with the failures that those efforts generated, most IT teams are now looking to a more “phased” approach – a modernization strategy that breaks the initiative into steps and prioritizes using detailed information to plan and test modernization approaches prior to scaling. One example of a 5 step phased approach is:

A key benefit of phased modernization is the ability to segment application budgets by distinct stages. Agencies can initially fund just an application assessment project to understand their legacy systems before committing to a full project. A State Department of Transportation successfully employed this approach when modernizing business process mainframe applications written in four different languages including COBOL code and C#.NET code. The department was hoping to adopt some modern platforms as replacements for these applications and needed to understand not only the code and business logic but how the logic then consolidated into business rules or business policies, Working with a services partner, they first contracted with EvolveWare for automated documentation and analysis, using these insights to structure their business rules extraction and budget appropriately. Similarly, the Defense Logistics Agency (DLA) began their ERP modernization by analyzing and rationalizing their application portfolio, generating $23M in savings. Demonstrating such interim success metrics also helps secure funding for subsequent modernization phases.

Managing the transition from mainframe while maintaining continuity of critical services

When it comes to government systems, the costs of failures or downtime can have resounding impacts on the economy, the nation’s defense, citizen healthcare access and more. While modernizing these systems is critical, agencies must be able to maintain their current systems until the new systems are ready to be switched in. System updates may be made to the current system during a government modernization project to address security loopholes or changing regulations and policies amongst other reasons. For longer modernization efforts where larger systems or more extensive transformations are involved, this can lead to large disconnects between the modernization work performed and where the source system is by the time the target system is ready, requiring even more work to update the target system itself.

But automation and agile techniques have changed the game here. A state department of health and human services was looking to extract the business rules from their Medicaid claims system in order to move to a modern platform. However, they were aware that changes would be made to the original system during the modernization effort and therefore needed a solution that could keep track of these changes and allow the modernization team visibility into how the changes would affect their work. Intellisys’ Agile Business Rules Extraction (BRE) solution uniquely provides these abilities. New versions of source code can be run through the platform to identify changes from the original source code and to generate reports to see the impact on modernization work already performed. Using these capabilities, the state was able to document and extract rules from 1.3MM lines of COBOL code in 6 months.

There is no need to keep “kicking the can” down the road

As federal agencies face increasing pressure to modernize their legacy systems, the path forward requires strategic thinking and modern tools. By leveraging automated documentation technologies, adopting a phased approach to modernization, and implementing agile modernization strategies, agencies can overcome the challenges of limited expertise, complex procurement cycles, and service continuity demands. The success stories from various state and federal agencies demonstrate that effective government modernization is possible when the right approach is taken. With millions of Americans depending on these systems, the time to act is now—before institutional knowledge walks out the door and the technical debt becomes insurmountable.

Application Assessment Documentation & Analysis

Detailed Application Analysis: The Key Driver for Modernizing Systems

Article Summary: Legacy systems pose serious security risks, as demonstrated by the 2024 Change Healthcare ransomware attack that exposed 100 million patients’ data due to a lack of modern security measures. Before attempting any modernization effort, organizations need a thorough assessment of their existing applications — covering architecture, data flows, business logic, and integrations — as skipping this step can lead to costly, preventable errors. This analysis delivers three core benefits: mitigating operational risk, surfacing inefficiencies and technical debt, and enabling successful automated code transformation. Automated documentation tools are increasingly vital to this analysis with institutional knowledge walking out the door as the workforce evolves. Organizations that commit to ongoing application analysis are better positioned to modernize safely, stay competitive, and align technology decisions with broader business goals.

Understanding and analyzing your legacy applications is crucial for any organization’s digital success and long-term sustainability. This knowledge is indispensable for the following reasons:

Secure your applications for the future

Legacy applications often harbor security vulnerabilities, as they were designed in an era with different security requirements. And we’ve seen the consequences of that play out in practice. Take for example the healthcare industry which faced significant breaches both in 2023 and in 2024, such as the ransomware attack on Change Healthcare. This attack exposed the private health information of over 100 million people and is largely attributed to the lack of multi-factor authentication (MFA) methods on these systems. MFA is an example of modern technology that can be incompatible with legacy systems.

As 73% of healthcare organizations still use legacy systems, it’s imperative that they identify areas of concern in their current systems and execute on a plan to patch up these areas. This is where application analysis comes in. A thorough application assessment provides crucial insights into the layered complexity that accumulates over years of system modifications. Armed with this comprehensive understanding, IT and security teams can better identify risks and their potential ripple effects throughout the application ecosystem. This knowledge empowers organizations to take preemptive action against costly security breaches and system infiltrations.

Transform your systems to enhance productivity, deliver intuitive experiences and outplay the competition

While identification of security loopholes is a first step, ultimately many of these legacy applications fall short as organizations look to the future. In the rapidly evolving landscape of technology, application modernization has become imperative for organizations seeking to remain safe, competitive, and efficient. The key to successful modernization lies in developing a deep, comprehensive understanding of existing applications before making any transformative changes. While this is generally well-understood, the level of detail needed from legacy applications in order to modernize these applications successfully cannot be overstated.

Each application within an organization represents a complex ecosystem of interconnected processes, business logic, data flows, and legacy integrations. These systems have typically evolved over years or even decades, accumulating intricate knowledge and serving critical business functions that may not be immediately apparent to current team members.

Application analysis begins with a thorough assessment of the application’s current state. This involves mapping out its entire architecture, including its technical components, data dependencies, integration points, and underlying business processes. Organizations must conduct detailed documentation reviews, perform code analyses, and potentially engage with long-time employees who understand the application’s historical context and nuanced functionality. Sometimes the latter is no longer possible with those employees having left the organization or workforce.

Tools such as the Intellisys platform use your source code files to to provide assessment reports, graphs and charts, and explanations of the code using 100% automated documentation generation. This technology built on AI/ML models also allows organizations to identify missing source code files or linkages to systems that may not have initially been associated with the system in question. The compilation and analysis of all this data facilitates multiple paths to modernization by:

Mitigating risks: Applications that have been in use for extended periods often contain unique solutions to complex business problems. Modernization without full comprehension can lead to significant operational disruptions, potential data loss, and unexpected system failures. A meticulous understanding allows organizations to develop robust migration strategies, create comprehensive backup plans, and implement phased modernization approaches that maintain business continuity.
Pinpointing areas of strategic optimization for the new application: The process of understanding applications also provides an opportunity for addressing technical and procedural debt. It allows organizations to identify inefficiencies, redundant processes, and potential areas for improvement that go beyond simple technological upgrades, particularly for a business transformation. This holistic view enables more effective decision-making, ensuring that modernization efforts align closely with broader business objectives.
Enabling automated code transformation success: Automated code transformation has had a varied track record in the past as some methods of doing this transformation don’t take into account the difference between modern programming languages and architectures and legacy ones. However, tools that do take into account these differences have changed the game. They allow you to create a target application with them in mind such as by generating modern code like Java in microservices. In order to generate clean code without needing too much manual intervention, it’s imperative to start with the type of thorough assessment discussed above. In our experience, neglecting these steps inevitably leads to preventable errors that drain time and resources.

Don’t make the same mistake twice. Keep knowledge of your new systems current for on-going application analysis

With the rate of change of technology, modernization can no longer be thought of as an infrequent process. Organizations must adopt the continuous modernization mindset and therefore keep track of the health and inner workings of their systems on an on-going basis with tools such as the Intellisys platform, CAST Software, etc.

Ultimately, understanding your applications is not just a technical exercise—it’s a necessity. It transforms modernization from a potentially risky technological overhaul into a carefully planned, value-driven transformation. Organizations that invest time and resources in truly assessing their existing systems and business processes are better positioned to create application transformation and management strategies that are not just technologically sound, but also aligned with their broader business goals.

To learn more about the benefits of using Intellisys to analyze and assess your applications, contact us.

Application Modernization Business Rules Extraction EvolveWare

New Year, New Intellisys: Updates that Transform Code Modernization Workflows

Application modernization initiatives are complex. 71% of IT leaders report that code modernization is stressful and over a quarter of respondents fear job impacts from projects that do not meet expectations. If you’ve been involved in one of these projects or heard stories, you can understand why from the level of detail needed, the planning, and the heavy load of time-consuming tasks involved. Understanding these challenges, we built the Intellisys platform to reduce risk and streamline the modernization process. With that in mind, we are announcing several new enhancements to Intellisys to tackle modernization workflows with greater speed and confidence.

Spend less time trying to “translate” code, more time moving forward

Rules extraction plays a vital role in modernization efforts that involve redesigning or reimagining systems. However, the process of consolidating business logic and distilling it into coherent policies and rules can be labor-intensive. While the Intellisys platform already streamlines many of these challenges, we are introducing three new AI-powered plugins to further accelerate the process. All of these enhancements are available as optional add-ons to the platform. You are welcome to take advantage of them as and when they make sense for your organization.

Intelligent Variable Renaming: The first AI utility tackles the complex task of variable renaming that occurs early in rules extraction. Legacy code often resembles an archaeological site, with layers of obsolete practices and historical decisions embedded within. Variable names frequently contain obscure acronyms whose meanings are buried in old manuals and documentation, with the original developers long since departed. Legacy code modernization teams previously had to act as detectives, investing significant time researching and decoding these cryptic naming conventions.
This investigative burden is now significantly reduced. Intellisys can automatically generate contextually appropriate suggestions for variable names, which modernization teams can review and implement at their discretion. The platform analyzes various data sources to provide organization-specific acronym expansions. Importantly, all suggestions undergo human review to ensure accuracy and contextual appropriateness before implementation. This approach maintains human oversight while leveraging AI code review tools to accelerate the modernization process.
Language Conversion for Code Comments: We’re also introducing a language conversion utility to Intellisys that uses AI technologies to translate Spanish and Portuguese comments within the source code into English. This feature enables English-speaking analysts to get the full context in the source code while reviewing business logic, facilitating smoother collaboration across international teams. Future development plans include implementing bidirectional translation capabilities, allowing English annotations to be converted back to the application owners’ native language, as well as expanding support for additional languages.
Automatic Business Rule Summary Generation: We now have the ability to automatically generate summaries of business logic annotations and descriptions created by the modernization team. This eliminates the need for manual summary creation on top of their initial annotation work. As with our other AI features, these summaries are presented as suggestions for team review and implementation. By leveraging intelligent technology for summary generation, team members can focus less on tedious resummarizing work and more on strategic planning for the system’s future.

Leverage more comprehensive modernization options to stay ahead of future risks

The software development landscape encompasses thousands of programming languages and frameworks. While the challenges of popular legacy languages such as COBOL have been well-documented, talent shortages and technical limitations are emerging even for more recent technologies. The Intellisys platform has long addressed this complexity by providing AI code review tools for over 20 legacy and modern programming languages, enabling comprehensive analysis of diverse software portfolios. Our flexible extraction technology allows us to expand language support based on customer demand and with access to the necessary inputs, which is how we’ve integrated additional languages in the past.

One such language is PowerBuilder. As we’ve seen an increase in organizations looking to move away from PowerBuilder in the past year, we have improved the way in which we extract data from these applications to better support linkages and data windows. Ultimately, this allows us to provide our clients with better, more relevant application assessment information which helps ensure successful modernization planning and outcomes.

Technologies of today also continue to evolve. What’s popular to develop in today is not exactly the same as it was even just two years ago. To stay ahead of these changes, we continuously update our code migration technologies as well to ensure generation of the latest frameworks and technologies to meet various use cases.

To that effect, we’re announcing the release of new automated code transformers that support COBOL to Java code migration with a Remix/React frontend as well as legacy Java to modern Java code migration with the same frontend. The code is generated at an automation rate of 90% or more with the remaining requiring minimal manual intervention.

Take advantage of simplified workflows with EvolveWare and Intellisys as trusted partners

The Intellisys platform is built on a foundation of AI/ML technology that has evolved and matured over the past 15 years to drive successful code modernization initiatives. Through today’s announced enhancements, we further advance our mission of making application modernization as seamless as possible for our clients. Whether organizations seek to leverage our AI code review capabilities or utilize the platform for comprehensive business and technology transformations, Intellisys delivers the insights and tools needed to achieve their objectives through reliable, streamlined workflows.

To learn more about how EvolveWare’s latest enhancements in Intellisys can simplify and speed up your code modernization effort, please contact us.

Application Assessment Application Modernization Business Rules Extraction Documentation & Analysis Optimization & Migration

Cracking the Code: The Best Legacy Modernization Approach in 5 Steps

Modernizing legacy applications can be daunting, especially since legacy applications tend to grow in complexity over time and have multiple dependencies on other enterprise systems. In a recent survey, 71% of respondents reported that legacy modernization efforts were stressful¹. Fear of failure from previous attempts has increased IT leaders’ fear of harming their careers. Culprits of failed modernization span the gamut – from misaligned expectations and costly strategy missteps to hard-to-find resources and tools that can provide the right knowledge and capabilities. At the same time, it is clear that organizations must grow and transform to enhance innovation potential, improve internal and external experiences, and reduce risk and costs. Cracking the code on the best legacy modernization approach is therefore essential.

As the saying goes, all seemingly intractable problems can be untangled by breaking them apart in ways that still make sense and tackling them in smaller doses. In fact, mapping out modernization projects in phases empowers every organization on its modernization journey to achieve success without drowning in the time and resource costs of failures. By continuously aligning with business stakeholders, thoroughly reviewing systems, zeroing in on specific modernization paths, and using a test or pilot project prior to scaling, IT teams can finally crack the code of successful application modernization.

Here’s a tried-and-true roadmap for the best legacy modernization approach for your team, outlining the critical steps:

Step 1: Align on goals with business stakeholders and take stock of your organizational constraints
When you start the initial planning for your modernization project, work with key stakeholders on what the business goals are that the modernization project needs to address and different ways in which those goals can be achieved. Typically, this will take the shape of either a business transformation, technology transformation, or both. Then, take stock of your constraints: people, processes and budget. Consider who needs to be involved during the process, such as business and technical stakeholders, and resources that will be required for the project. Budget, resource or time constraints may dictate if you can pursue a business transformation or if you need to focus only on a technology transformation to start.

More questions to consider include: What is the timeline for modernization, and how quickly do you need to show results? Do you need to embark on a major transformation initiative or do only certain systems need to be addressed? How will you measure the success of the project? What is your available budget? Taking stock of your initial requirements and constraints will help you identify key considerations as you determine your ideal path to modernization.

Step 2: Assess your legacy applications, including critical dependencies
The next step is to assess your legacy applications thoroughly. Review the source code and generate an up-to-date list of documentation artifacts to understand the logic, complexity and dependencies within the applications. This documentation process will reveal the software’s complexity as well as dead or redundant code that is surely lurking inside. The output at this stage is a series of reports, diagrams, and logic representations that provide a detailed view into the composition, database access details, and logic of the applications processed. These artifacts give you necessary information to categorize the current state of your applications, estimate the investments needed to determine your transformation options and help outline the business value associated with each option. This step also allows you to see how you could break up the modernization process across multiple applications, i.e. which modules have dependencies and which don’t.

Using manual methods, the effort required for documentation and analysis of your applications can be significant. This is where your team can benefit from intelligent automation. There are tools out there that can automate the reverse engineering of your source code and produce documentation artifacts for you. For example, the Intellisys platform reduces the time to create documentation by 90% or more using its patented technology. This documentation provides both graphical and non-graphical artifacts allowing both technical and business analyst resources to dive deep into understanding the inner workings and complexity of their systems. Using that knowledge, they can estimate the investments needed to achieve the business value associated with the outlined modernization goals.

Step 3: Determine the best legacy modernization approach(es) for you

Every enterprise system is unique, so determining the type of modernization you want to implement for each system that needs it is a vital step in the modernization process. The most common modernization options fall into five broad categories – Retain, Migrate As-Is, Optimize & Migrate, Reimagine & Redesign, or Retire. Here’s a quick visual to describe these options:

Retain: Use this path for an application where no changes are needed for business processes, that has few dependencies, and/or can be supported using modern technology
Migrate As-Is: This is one of two technology transformation options. Use this path for an application where code/rule/architecture optimizations are not essential prior to migration or where there are significant benefits to moving the application immediately.
Optimize & Migrate: This is the second of the technology transformation options. Use this path for an application where minor code/architecture optimizations are essential prior to migration.
Reimagine & Redesign: This is a business transformation path. Use it for an application where there are significant policy and technology changes that need to happen to meet business goals moving forward.
Retire: This path can also be part of a business transformation. Use it for an application that has duplicate functionality with another or no longer makes sense for business goals moving forward.

Determine the path that best fits your organization’s needs and integrate it into your planning. To learn more about these approaches and how to choose between them, download our application modernization strategy and planning guide.

Step 4: Start small with a pilot project

Create a small, clearly defined project to test your process, such as modernizing a single component of the application. Apply your modernization process to this small project to validate your planning, communication, and expected results. The test project will help you identify any flaws in your process and enable you to course-correct before embarking on a full-scale modernization project. Take what you’ve learned, integrate it into the larger plan, then run another test to confirm the process works as you planned.

Conducting small, quick tests will verify which processes work and are repeatable and will save resources, pitfalls, and hidden risks in the larger project. It may also help you confirm whether your vendors are a good fit for the initiative. If a vendor fails to perform as expected during the test project, use it as an opportunity to clear up any requirements, or to consider finding a new vendor.

At this point, you should have a clear understanding of what it will take to complete your modernization project. During this and any of the previous two steps, regroup with the key stakeholders and revise their expectations based on the revealed complexity, risks, and resources (both internal and external) needed to successfully complete the project. Include revised cross-organizational dependencies, budget projections, and timeline.

Rewrite the deliverables and timing for each phase of the modernization process. Mapping out your current state, available resources, and expectations will help you avoid mismatches between capabilities and expected outcomes- a common pitfall in application modernization projects.

Step 5: Scale with confidence

Upon completion of a successful pilot or pilot projects, scale the initiative to achieve your full transformation goals.

Throughout all these phases, embrace automation over manual processes. Automation enables IT teams to implement modernization initiatives on a larger scale, consistently, and minimizes the risk of errors, at a fraction of the cost and time that manual processes take. Automation also enables organizations to more quickly test and understand if a modernization approach will meet their needs. Organizations can iterate and adapt prior to starting a large-scale effort, using the pilot phase. Once this evaluation is done, the rest of the phases can be more successful because you have the learnings from the proof of concept step.

Consider these automation benefits: a multi-billion dollar investment firm turned to Intellisys to automate the reverse engineering and migration of an AS400 COBOL transactional system to Java and PL/SQL. To start, they were able to extract meta-data from 151,000 lines of COBOL code in two weeks, which, in turn, empowered the organization to analyze and optimize the extracted meta-data in four weeks. At the end, just under 90% of the COBOL code was transformed automatically, with the remaining code requiring manual completion.

To learn more about how EvolveWare can help you define the best legacy modernization approach to meet your goals, schedule a consultation with our expert team.

1 Mechanical Orchard Research Report. (2024 September). “Legacy Issues: What IT leaders really think about modernization.” Retrieved (2024/09/26). From https://www.ciodive.com/press-release/20240829-enterprise-it-leaders-twice-as-likely-as-juniors-to-delay-advocating-for-le-1/

Application Modernization

3 Considerations for an Organizational AI Implementation Strategy

We recently sat down with our CTO, Bruce Kirchner, to discuss legacy modernization and system transformation in today’s rapidly evolving technological landscape. This is the second of a 2 part series discussing the state of application modernization, the current and potential impact of artificial intelligence (AI) and modern technologies, and how organizations can devise an AI implementation strategy and move forward with AI adoption while finding value amongst the hype.

At EvolveWare, we’ve been working with AI and machine-learning (ML) technologies for over 15 years. As these intelligent algorithms lie at the foundation of our Intellisys platform for application modernization, we’ve learned a thing or two about where to invest AND where to be cautious when it comes to using these technologies in a safe, secure, and value-driven way. Here are my top 3 considerations for developing an AI implementation strategy within an organization.

AI is not ready to be the solution for everything

A leading figure in the AI space, Rodney Brooks, recently discussed the limitations of AI stating that “people see it as so capable they even want to use it for applications that don’t make sense.” It is important to understand both the benefits and constraints of AI technologies today and to plan your AI adoption strategy wisely. With the current state of technology, purpose-built models are really where you’ll derive the most value.

If we look at AI models today, they provide a lot of efficiencies when it comes to tasks like summarizing and pulling out useful pieces of information from a large dataset or corpus of text. For example, most application modernization projects require an introductory assessment phase of the organization’s software portfolio or at least of the systems in question. To determine where to take these applications and how to plan the project, you must first understand what they are currently doing, how they are structured etc. If this kind of task were to be done manually with a human poring over millions of lines of code, not only does the task become extremely time-consuming but it is also harder for any person to “see the forest for the trees” and determine information important for the project. AI and ML technologies reduce time, cost, and risk for these types of tasks, and at EvolveWare, purpose-built ML algorithms for application understanding were the foundation of what is now the Intellisys application modernization platform.

That being said, there’s been a lot of chatter about trying to use the new generalized large language models (LLMs) to quickly develop an understanding of large applications or other specialized use cases in order to further reduce time needed and improve explainability. But at this point, the technology does not give the types of results needed for custom use cases. While there are some techniques such as retrieval augmented generation (RAG) where external datasets are referenced to help models improve their accuracy rate, ultimately size of the dataset needed for a use case must also be taken into consideration. For example, the context window is not large enough for these LLMs to process an entire program or application that is likely hundreds of thousands or millions of lines of code. And trying to break up that code into chunks and piecing the results back together leads to disjointed results. So it’s essential to evaluate all the characteristics of a use case against the actual capabilities of AI technology today before assuming that AI will improve the manner in which that use case is performed. While we fully expect the technologies to mature, that will take improvement of the hardware, software and costs around it which will take time.

Data – especially data important for your business – is power

It’s paramount to use data related to your specific business to train AI models for your use case, not just generalized data that is openly found on the internet. This is why techniques such as RAG have become such a standard in enterprise Gen AI implementation strategy.

When it comes to application modernization, this business specific data is also stored in your application code. EvolveWare’s automated documentation generation capabilities can help organizations uncover that data by giving you an understanding of the various components of the application. This information could be used to build an AI solution to help organizations gain specialized knowledge about their specific applications as they look to maintain these applications or plan and implement a modernization initiative.

At the same time, organizations need to take into account privacy related concerns with the data that they think about using when training models. For example, healthcare organizations in the US have to be careful with data that would fall under HIPAA (Health Insurance Portability and Accountability Act). This is because information leakage is the biggest security concern when it comes to AI technologies. It is important to keep data that would fall under a restricted category separate from data that models are being trained on to avoid ethical and costly repercussions.

Let AI shine as the intelligent assistant, not the ultimate decision-maker

As we discussed above, AI technologies in their current form have both strengths and weaknesses. The important component is to recognize these strengths and use them to complement what we do well as humans to generate maximum efficiencies. At this point, AI can help accelerate processes that involve poring over thousands if not millions of data points and summarizing or making sense of that information to help with decision-making. But the ultimate decision is still best left to the human.

The recent mishaps related to “hallucinations” are a prime example of this. A hallucination is when an AI model misleadingly presents a response that is false as true based on the data it was trained on and how its predictions are designed. Some famous examples of these are Google’s first Bard demo where the model stated that the James Webb Space Telescope took the first pictures of a planet outside our solar system (this actually happened 16 years prior) and when Microsoft generated an article that listed a food bank as a top tourist destination in Ottawa, Canada.

Now had a person been involved in fact-checking or editing this content prior to publishing it, these errors could have been caught. And that is why we have and will continue to focus on building human-in-the-loop processes as we incorporate AI/ML into the Intellisys platform. Human-in-the-loop processes as we think about them are where humans review outputs and can provide feedback to the system. For example, one of the prime areas where we believe AI can add value for our product is in further speeding up the processes involved in the extraction of business rules. These include understanding and renaming cryptically-named variables and summarizing and categorizing rules automatically. However, once these “suggestions” are made, we do believe that there should be a person involved to accept or reject the suggestions prior to moving forward – i.e. the person uses the information presented to make the final decision on the variable rename, rule category, rule summary, etc.

Partnering for the future

There is no question that AI/ML technologies will continue to reshape the internal and external experiences that we build. And as hardware and software advancements are made and costs decrease, tasks that the technology cannot appropriately handle today will possibly become routine. However, as we think about product roadmaps and business decisions for the near future, it’s essential to be pragmatic and play to the technology’s current (and near future) strengths. While powerful, AI remains limited in crucial ways – lacking true understanding, common sense reasoning, and adaptability to novel situations. The most effective approach is viewing AI as an intelligent assistant rather than an autonomous solution. By leveraging AI’s strengths in processing, pattern recognition, and prediction alongside human expertise, creativity, and judgment, we can achieve far more than either could alone.

EvolveWare has been recognized as a Representative Vendor in the Gartner Market Guide for Mainframe and Legacy Systems Migration and Modernization Tools. To learn more about EvolveWare, our AI implementation strategy, and AI adoption within the Intellisys platform, please contact us.

Application Modernization Optimization & Migration

How Spec-Driven Development Is Changing Legacy Application Modernization

Spec-driven development is a faster, more flexible alternative to traditional legacy modernization, where organizations first generate comprehensive technical specifications from a legacy system — capturing both what the code does and why — before migrating to modern technology. This article walks through Intellisys’ 5-phase process for putting this into practice (mapping legacy metadata, refining it, generating agnostic tech specs with LLMs, generating modern code from those specs, and testing the result), backed by a real-world case study of a US-based accounting firm modernizing a 3-million-line PowerBuilder application.

Key Takeaways:

Spec-driven development flips traditional modernization. Instead of translating legacy code line by line, organizations first generate technology-agnostic technical specs that capture what a system does and why — then use those specs as the blueprint for migration.

Specs simplify long-term maintenance. Once migrated, future changes happen at the spec level instead of through granular code edits, reducing dependency on developers fluent in the original legacy language.

The process runs in five phases: mapping and refining legacy metadata, generating specs with LLMs, generating modern code from those specs, and validating the result through rigorous testing.

Automation is high and verified. Code generation is 90%+ automated, with all output run through third-party tools to check for vulnerabilities before deployment.

The approach works in practice. A US-based accounting firm is using this process to modernize a 3-million-line PowerBuilder application, validated through a client-approved proof of concept.

For decades, enterprise organizations have been tethered to legacy applications that, while foundational to their operations, have become increasingly expensive and risky to maintain. The traditional methods of code modernization—often involving manual code rewrites or “lift-and-shift” migrations—frequently fall short, resulting in budget overruns, extended timelines, and systems that still carry the technical debt of their predecessors. However, a new era has arrived, centered on AI-Augmented spec-driven development. By leveraging automatically generated technical specifications as the blueprint for migration, organizations can finally break free from legacy constraints.

The Problem with Traditional Migration

Most organizations recognize that standing still with legacy systems is expensive. These systems often lack scalability, carry security vulnerabilities, and face a growing skills gap as the developers who originally wrote the code retire. Despite this, many modernization projects fail because teams lack a deep understanding of the legacy environment and attempt to translate the code line by line.

This approach is fraught with peril: it preserves obsolete logic, ignores hidden dependencies, and perpetuates code bloat—all while failing to capture the “why” behind the original code.

The new way to migrate code moves away from this flawed approach, instead generating a comprehensive set of technical specifications (tech specs) before a single line of new code is written. This shift delivers two key benefits:

Technology-agnostic clarity: Specs describe the source system in detail without requiring developers to be fluent in its original technologies
Simplified maintenance: Changes to the transformed system can be made at the spec level, rather than requiring granular code-level edits

Turning these benefits into reality requires a structured, phased process—beginning with detailed extraction of metadata from the source application. Here’s how the Intellisys platform puts that process into practice.

Phase 1: Mapping the Legacy Landscape

The first step in a spec-driven migration is gaining complete visibility into the existing application portfolio. Intellisys facilitates this through its Documentation & Analysis toolsets, which extract metadata from the source system and use it to provide a 100% automated set of reverse-engineering artifacts.

Instead of spending months manually mapping out old codebases, Intellisys uses custom-trained AI/ML technologies to generate these documentation artifacts with 90% less time and effort than traditional methods.

These artifacts allow architects to:

Analyze implemented logic and understand the impact of potential changes.
Identify hidden dependencies that could create risks during the migration window.
Keep documentation continuously updated as the legacy system evolves during the project, eliminating the need for code freezes.

The same metadata that is used to generate these artifacts also enables the successful generation of technical specifications.

Phase 2: Refining the Metadata

Before the tech specs themselves are generated, the extracted metadata must be optimized. Intellisys includes robust optimization features that reduce complexity, ensuring the specs built from this metadata describe not just a copy of the old system, but a modernized blueprint that meets today’s standards for scalability and security.

The goal is for the target code to have zero vulnerabilities, as measured by leading code quality tools. By optimizing the metadata before the specs are generated, organizations mitigate the risk of carrying legacy security flaws into their new environment.

Phase 3: Building the Blueprint for Spec-Driven Development

Once the code has been refactored, Intellisys uses large language models (LLMs) to automatically generate agnostic technical specifications. These specifications are the blueprint that the client will use moving forward to transform and maintain the new application. Here is a sample tech spec:

Phase 4: Generating Modern Code from Specs

Once the tech specs have been generated, the Intellisys platform then uses them to migrate code to modern technologies using locally installed GenAI models. While some GenAI powered migration methods have struggled with the nuances of specialized legacy languages, the Intellisys platform provides the extracted context necessary to guide these models accurately. The generated code is 90% + automated, with some case studies having an even higher rate of automation.

The new system can then be maintained from the technical specifications themselves. When requirements change, the organization can edit from the spec and then regenerate the code from there. There are no proprietary mechanisms or libraries when using Intellisys, and the code is run through 3rd party tools to ensure that no vulnerabilities are present.

Phase 5: Testing the Target System

After the modern system is generated, unit and regression testing must be performed for each module as well as all integrated modules. This testing will validate that individually generated functions and methods behave correctly as well as that the system as a whole behaves as expected. Parallel testing with the source system in production can also be conducted to ensure the results match before placing the modern system into production.

Real-World Success: Accounting Firm

EvolveWare is partnering with a US-based accounting firm to modernize a PowerBuilder application of approximately 3MM lines of code as the first part of a much larger project. The spec-driven development process detailed above was tested through a proof of concept to validate all steps on a smaller piece of the application, and the client reviewed and accepted the results after all tests passed.

Named in the 2026 Gartner® Magic Quadrant™ for AI-Augmented Code Modernization Tools, the Intellisys platform offers several distinct advantages for this client and others:

Unparalleled Language Support: Supports 20+ legacy and modern languages, including COBOL, PowerBuilder, Natural/ADABAS, and Java.
Reduced Risk: A phased approach — Align, Assess, Select, Pilot, and Scale — lets organizations validate their approach before committing to full-scale migration.
Security and Accessibility: The platform can be installed on-site or hosted to meet strict security policies, is accessible to authorized personnel via web-based interfaces, and runs all AI models locally to keep data secure.
Cost and Time Savings: Automating documentation, business rules extraction, and code migration significantly reduces the manual labor of modernization, letting teams move to modern systems faster.

Modernization, Re-imagined

The time and risk associated with legacy modernization have long been a blocker for organizations looking to move forward. But as technology evolves, that equation is shifting—modernization is becoming both more urgent and more achievable, as new tools make the process faster and more flexible than ever before.

By generating technology-agnostic tech specs as the foundation for migration, Intellisys turns that shift into a concrete path forward—one where organizations no longer have to choose between standing still and betting everything on sub-optimal modernization strategies.

FAQ

What are technical specifications?

Technical specifications describe the requirements, features and characteristics of a product or system. They typically include functional and performance requirements, technical constraints, and interfaces amongst other details. They are useful from a modernization perspective because they describe the system in a developer-neutral manner so that organizations are not tied to the original programming language of the product or system and because they make future maintenance easier.

What is spec-driven development?

Spec-driven development is a software development approach where a detailed, structured specification is maintained and used to generate code as opposed to maintaining the code directly. This approach is useful for legacy modernization efforts as instead of migrating legacy applications by translating code line by line, organizations first generate a comprehensive set of technical specs that describe what the system does and why, independent of its original programming language. These specs then become the blueprint used to build the modern system, and any future changes are made at the spec level rather than through granular code edits — making the system easier to maintain and update over time.

Does the documentation and/or technical specs stay current automatically as the codebase changes?

Using automation and AI in the Intellisys platform, organizations can keep their systems documented in an on-going manner. The platform allows organizations to re-run code as changes are made back through the platform’s extraction engine. Intellisys identifies where changes have been made to the code and generates reports highlighting these changes. Intellisys can also regenerate technical specs if needed using the updated metadata.

How accurate is AI-generated code compared to manually written code?

The accuracy of AI-generated code is dependent upon the context provided to the models and the processes used to generate the target code. The Intellisys platform has a 90%+ automation rate after testing. The platform uses patented metadata extraction technologies to retrieve highly detailed context from the source system and leverages that to generate the technical specifications that are then used to generate code. All processes that use AI technologies in Intellisys are built using human-in-loop review processes and our phased approach ensures successful results at each stage.

Application Modernization Application Rationalization Business Rules Extraction

6 Modernization Use Cases for Business Rules in System Analysis and Design

Agile Business Rules Extraction (BRE) is a modern approach to identifying and documenting the policies and algorithms hidden within legacy software. Business rules are a fundamental component of system analysis and design. Using the Intellisys platform, organizations can uncover and leverage these rules or policies to ensure the success of digital transformation projects, such as re-imagining and rebuilding systems or migrating to modern platforms like Oracle ERP or low-code environments.

Key Takeaways:

The role of business rules in system analysis and design: Organizations that attempt system migrations without first extracting their legacy rules risk losing critical policies “in translation.” Having documented rules gives developers and AI models a reliable blueprint for rebuilding with confidence.

BRE helps clean up bloated software portfolios: The extraction process surfaces obsolete rules, duplicate logic, and dead code across systems — allowing organizations to rationalize their application landscape and cut unnecessary complexity.

Human-readable rules are a compliance superpower: In regulated industries, being able to point auditors to plain-English translations of cryptic COBOL or legacy code is invaluable. It removes the dependency on a handful of technical experts to explain system behavior.

Extracted rules inform smarter platform decisions: Rather than guessing which systems belong on a COTS product, a low-code platform, or a custom build, organizations can use rule complexity as an objective measure — as the U.S. Air Force did when evaluating 55 systems for Oracle ERP compatibility.
Agile BRE eliminates the dreaded “code freeze.” Traditional extraction required halting all policy updates during the process. Modern platforms like Intellisys allow continuous updates, ensuring extracted rules stay in sync with the live environment and are immediately usable in modernized systems.

In the world of IT, legacy systems are often described as “black boxes.” They perform critical functions—processing insurance claims, managing military logistics, or handling state-wide tax records—but the exact logic governing these functions is often buried under decades of code updates and lost documentation.

Business Rules Extraction (BRE) is the process of uncovering the policies and algorithms embedded within these systems. Once these rules are extracted, they cease to be “trapped” logic and instead become versatile assets that can drive an organization’s future. Based on the capabilities of the Intellisys platform, here is a deep dive into key use cases for business rules in system analysis and design, once they are successfully extracted from a legacy environment.

1. Driving Digital Business Transformation

The primary use for extracted business rules is to serve as the blueprint for digital business transformation. Many organizations find that their transformation initiatives—such as moving to the cloud or rebuilding a system from the ground up—stumble because they do not fully understand the logic running in their current environment.

By extracting these rules, organizations can:

Re-imagine and rebuild systems with confidence: Instead of guessing how a legacy system handles a specific edge case, developers have a documented list of rules to replicate or update in the new system based on evolving business needs.
Ensure functional parity: Extracted rules allow a new application to maintain the same business logic as the legacy version, ensuring that no critical policy is “lost in translation” during the migration.

2. Streamlining Portfolio Rationalization

Large organizations often suffer from “bloated” software portfolios where multiple systems perform overlapping functions. Extracted rules are the key to application rationalization.

Through the BRE process, organizations can identify:

Obsolete rules: Logic that is no longer used by the business but still exists in the code.
Duplicate rules: Situations where the same policy is being calculated or enforced in multiple ways across different systems.
Dead code: By using tools like Intellisys to deactivate “dead weight” and environment-specific code blocks, organizations can slim down their logic to only what is necessary for the business.

3. Satisfying Audit and Compliance Requirements

In highly regulated industries, it isn’t enough for a system to work; you must be able to prove how it works. Extracted business rules are invaluable for audit and compliance.

Legacy code is often written in cryptic languages (like COBOL or early Java) that auditors cannot read. BRE tools bridge this gap by providing a plain English translation of these tools. This allows both technical and management personnel to verify that the software is adhering to current legislative and business policies. If a regulator asks why a certain decision was made by the system, the organization can point to a specific, human-readable rule extracted directly from the source code. AI-Augmented platforms such as Intellisys use the latest technologies to automate the generation of these summaries, saving organizations more time and effort.

4. Facilitating Modern Platform Selection

One of the most strategic uses of extracted rules is informing the selection of a new technology stack. Not every legacy application belongs on the same modern platform. BRE helps organizations identify which applications are best suited for:

Commercial Off-the-Shelf (COTS) Products: For example, the U.S. Air Force wanted to do a comprehensive analysis of USAF legacy systems to determine whether the Oracle E-Business Suite Enterprise Resource Planning (ERP) system would be a good fit. To do so, they extracted and rationalized rules from 55 different systems to determine which ones should be implemented in the Oracle ERP suite and how best to do so.
Low-Code/No-Code Platforms: By understanding the complexity of the extracted rules, IT leaders can decide if an application can be moved to a low-code environment or if it requires a more robust custom build.

5. Architecting for Microservices

Modern software architecture is built on microservices—small, independent units of logic. Extracted business rules provide the “DNA” for these services. Once rules are extracted and consolidated, they can be transformed into modern code and used to define and generate specific microservices. This allows a massive, monolithic legacy application to be broken down into agile, manageable components that are easier to update and scale.

6. Bridging the Gap Between IT and Business

In many legacy environments, there is a “knowledge silo” where only a few senior developers understand the code. When business rules are extracted and exported into project management tools like Jira, they become visible to the entire organization.

This visibility allows for:

Better Informed Decisions: The State of New York extracted business rules from 31 million lines of legacy code written for their Integrated Eligibility System (IES). They then exported these rules into Jira to help them plan a rebuild of the entire IES system.
Clearer Communication: Because rules are presented at a granular level and annotated for readability, business stakeholders can provide feedback on the logic without needing to understand the underlying programming language.

The Importance of the "Agile" Approach

A significant challenge in traditional rules extraction is the “code freeze.” Historically, organizations had to stop making policy updates while the extraction took place. However, Agile BRE (as seen on the Intellisys platform) allows for continuous updates.

If a legislative policy changes while the extraction is in progress, the platform can automatically transfer those code updates without affecting unrelated rules. This ensures that the finalized, extracted rules are always in sync with the live business environment, making them immediately ready for use in a modernized system.

The Future of Business Rules in System Analysis and Design: User Stories and Tech Specs

Extracted business rules are more than just documentation; they are the functional core of each organization. Whether they are being used to rationalize a portfolio, validate a COTS platform, or generate microservices, these rules provide the clarity needed to move from a legacy-bound past to a digitally transformed future.

While extracting and understanding business rules will continue to remain important for all the reasons outlined above, the latest technologies are enabling organizations to use these higher level policies in a multitude of ways while reducing time and effort spent on transformation. Platforms such as Intellisys now allow automated generation of user stories or technical specifications. The user stories can be exported and used as part of a system re-write. The technical specifications can be automatically converted into code using spec-driven development tools or custom AI pipelines.

With platforms like Intellisys bridging the gap between legacy logic and modern development, organizations no longer have to treat their past as a barrier — instead, they can use it as the blueprint for a faster, smarter, and more confident path forward.

Contact us to see how Intellisys can help leverage your business rules in system analysis and design use cases for legacy applications.

Application Assessment Application Modernization Documentation & Analysis

Successful AI Adoption Requires Technical Debt Management Tools

Article Summary: AI-assisted development is generating architectural technical debt faster than it resolves it— creating systemic instability for unprepared organizations. Adopting the right technical debt management tool to meet an organization’s requirements is imperative. EvolveWare has been recognized as a Representative Vendor in the 2026 Gartner® Market Guide for Technical Debt Management Tools. The company’s Intellisys platform helps organizations get ahead of this risk by providing deep visibility into existing systems across legacy and modern codebases.

According to the 2026 Gartner® Market Guide for Technical Debt Management Tools (accessible to Gartner subscribers only), “By 2028, AI will create more technical debt on architecture levels than it solves.”¹ As organizations such as Microsoft and Anthropic roll out tools to address fixing code level technical debt, the remaining technical debt will be in architecture, system design, and dependencies. According to the Market Guide, that debt “creates systemic instability and long-term fragility.”¹ This report is not the only one that mentions the potential pitfalls of AI-assisted development. In a report on the state of AI generated code, Sonar found that 61% of developers agree that AI often produces code that seems correct but isn’t reliable. They also found that 90% of issues associated with AI generated code are considered code smells or harder to pinpoint maintainability challenges. This means that a lot of potential time savings from using AI code generators are getting eaten up by oversight and verification that is required later. Or worse, if that verification is not performed, potential system failures may occur.

That’s why one of the report’s key recommendations is to “Prioritize the adoption of a technical debt management tool that addresses your most pressing need: architectural technical debt reduction, code quality improvement, application portfolio prioritization, or preparation for large scale system modernization.”¹ EvolveWare is proud to be recognized as a Representative Vendor in this report. The company’s Intellisys Platform uses AI and automation to speed up and de-risk key processes involved in maintaining and modernizing applications, including providing robust documentation, diagrams, and reports of the current state of applications. Organizations can start simply by understanding their current systems and then use that information for future portfolio prioritization or modernization initiatives. For those who choose to rationalize or modernize their systems, the platform extracts unparalleled context from source systems and allows users to use that information to refactor first to power successful system migrations.

This is even more important for those with legacy systems or systems written in older technologies. With legacy systems there is the existing technical debt and then there is the new technical debt that is introduced when trying to adopt these newer AI technologies. If an organization is not careful, the compound of all of this can create major security gaps, budget overruns or complete shutdowns. Since the Intellisys platform supports reverse-engineering of applications written in 20+ languages including both legacy and modern ones, large organizations are able to assess entire portfolios written across multiple years as opposed to only being able to concentrate on a few. A State’s Department of Transportation used the tool to create an enterprise repository for consumable inventory applications written in COBOL, C#.NET with T-SQL, PL/SQL, and VB .NET prior to moving off the mainframe.

Organizations are also able to continue to assess the state of their code as changes are made. Intellisys supports agile documentation capabilities where newer versions of the source code can be processed and reports are generated identifying where changes to the code have been made and the impact. While working on a modernization initiative for their large IES system, the State of New York used this feature to identify where changes in the production code were made and how they impacted modernization work that had been performed prior to those changes. This ensured that the modernized version of the application would be in sync with any updates made to the production version of the code, as the project involved 31 million lines of legacy code and took 3 years to complete.

The relationship between AI adoption and technical debt is neither simple nor one-directional. AI offers productivity gains, but those gains come with architectural and maintainability costs that are only beginning to be fully understood. As the evidence from Gartner, Sonar, and others makes clear, the organizations best positioned to benefit from AI-assisted development are those that approach it with discipline — maintaining visibility into their systems, enforcing architectural standards, and actively managing the debt that accumulates over time.

For organizations carrying legacy systems into this new era, the stakes are particularly high. The compounding effect of pre-existing technical debt and the new debt introduced by AI adoption is not a theoretical risk — it is an operational one. The good news is that the tools and frameworks needed to manage this challenge are maturing alongside the AI technologies creating it. As AI continues to reshape how software is built and maintained, organizations that invest in understanding what they have today using a robust technical debt management tool will be far better equipped to make sound decisions about where they are going tomorrow.

To learn more about EvolveWare’s Intellisys Platform technical debt management tool capabilities, contact us.

1 Gartner, Market Guide for Technical Debt Management Tools, Tigran Egiazarov, Howard Dodd,Aaron Harrison, 13 April 2026

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Application Assessment Documentation & Analysis

Legacy Application Mapping: The Compass to Successful Modernization

Article Summary: Thorough legacy application mapping and documentation is an essential prerequisite for any modernization effort. Undocumented legacy systems carry serious risks including hidden security vulnerabilities, mounting technical debt, and the danger of losing critical business logic during migration. Modern AI-powered tools have dramatically accelerated this process — reducing documentation time by over 95% and enabling continuous updates without code freezes. Real-world examples, including a Department of Defense system and a global insurance company, demonstrate that investing in documentation first leads to faster, higher-quality modernization outcomes. Keep reading to learn more.

In the world of enterprise IT, legacy systems are often treated like ancient, mysterious artifacts. They are foundational to the business, yet few people truly understand how they work from the inside out. As the original developers who built these systems retire from the workforce, organizations are left with millions of lines of code—often in languages like COBOL, Fortran, or PowerBuilder—that have been patched and extended for decades. This “black box” scenario creates a significant barrier when it comes time for legacy modernization.

Many organizations realize that legacy application mapping or creating application documentation is not just a secondary administrative task; it is a critical requirement for future planning and a fundamental prerequisite for any successful transformation, even more so when using newer AI technologies. Without a comprehensive understanding of the current state, any attempt to modernize is akin to sailing into a storm without a compass.

The Hidden Risks of Undocumented Code

The lack of in-depth understanding regarding source code is one of the primary challenges that compromises an organization’s ability to generate scalable future applications. Legacy systems that have been modified over many years often contain hidden security vulnerabilities, making them prime targets for hackers. Furthermore, these systems typically carry immense technical debt, which create unnecessary costs for organizations. In a recent Deloitte survey on Global Technology Leadership, they estimate that technical debt accounts for 21% to 40% of an organization’s IT spending.

When thorough legacy application mapping is missing or outdated, the risks of modernization skyrocket. There is a very real fear of unknowingly leaving out a vital business rule or a critical line of code during the migration process. This is particularly true for large-scale government or enterprise projects where hundreds of systems may be interconnected and where regulations are a large concern. For example, the Department of Defense once struggled with a critical recruitment system that had become impossible to maintain because so little knowledge existed regarding its internal workings, as it had been developed by multiple third-party vendors over several years and no current documentation existed. The system had essentially failed by the time modernization became imperative.

Legacy Application Mapping as a Strategic Foundation

Modernization is rarely a one-size-fits-all process. Depending on the organization’s goals, the path might involve a Business Transformation or a Technology Transformation.

Business Transformation: This involves changing business models and processes. In this scenario, Agile Business Rules Extraction (BRE) is essential. Detailed documentation allows subject matter experts to identify and extract embedded business logic and policies so they are not lost when moving to a new platform like a COTS or Low-code/no-code environment. It will also allow organizations to identify which rules to keep, update and/or retire for their future system(s).
Technology Transformation: This focuses on replacing outdated systems to reduce costs and staff attrition risks. Here, the modernization path typically chosen is Code Optimization & Migration. Current documentation helps with determining how to best optimize the code prior to migrating it, allowing developers to identify and deactivate dead or unwanted code before migrating to a modern language like Java or C#. In addition, code transformation to modern technologies is only as successful as the detail or context provided to it. This context relies on the mapping or documentation generated.

In both cases, obtaining a foundational understanding of the application is the first step.

The Power of Automated Documentation

Traditionally, documenting a legacy system was a manual, grueling process that required months of interviews and code reviews. However, modern platforms like Intellisys have revolutionized this by using AI and machine learning to automate the process.

Automated documentation provides several game-changing benefits:

Speed and Efficiency: Automation can lead to a 95%+ reduction in the time required to generate application documentation.
Accuracy and Completeness: Human error is a significant risk in manual documentation. Automated bookkeeping ensures that no critical dependencies or data flows are missed.
Continuous Updates (No Code Freezes): One of the biggest hurdles in modernization is the “code freeze,” where developers must stop making changes to the live system while it’s being documented. Advanced automation allows the documentation and knowledge repository to be continuously updated even as changes are made to the legacy application.

Accessibility for Non-Experts: Documentation “translates” legacy code into a format that more people can understand. As noted by technical leads at BMI, automated tools like Intellisys make COBOL code available to a large group of people who are technical but not fluid in mainframe programming, allowing them to see program relationships and attribute changes clearly.

Real-World Examples

The criticality of documentation is best seen through the success of organizations that prioritized it. The Department of Defense, for instance, was able to transform the old, undocumented recruitment system mentioned above into a modern, multi-tiered Java architecture in record time by using automated optimization and migration solutions. By first establishing a 100% automated extraction of metadata, they were able to document, modernize, and deploy a system of 220,000 lines of code in just 6.5 months.

Similarly, a Global Insurance Company was facing critical risks across legacy systems powering insurance policy renewals, premium calculations, and claims processing. Documenting these systems to both plan the modernization effort as well as provide essential context for code migration was key. Due to the robust nature of the mapping provided, 2 million lines of legacy code in IBM iSeries COBOL, Oracle Forms & Reports, and .NET were migrated to modern Java and .NET at a 95% automation rate.

Agile Documentation Enables Continuous Modernization

In the rush to adopt new technologies like the cloud, AI, or microservices, it is tempting to skip the “boring” work of legacy application documentation. However, the evidence from the sources is clear: documentation is the key to modernization success. It reduces maintenance costs, identifies vulnerabilities, preserves critical business rules, and provides the “evidence” needed to make smart decisions about the future of an application portfolio.

It is vital to view documentation not as a one-time project, but as a living asset. The most successful modernization journeys involve creating an application knowledge base that evolves with the transformation. Once an application is moved to a modern language, maintaining that documentation ensures the organization never falls back into the “black box” trap.

Modernization is a journey. But you can’t know where you are without the legacy application mapping that documentation provides. Whether you are aiming for a total business transformation or a targeted technology update, your first step should always be to understand your applications to plan for the future.

To learn more about legacy application mapping within the Intellisys platform, download our infographic or contact us.

Application Modernization

How AI Code Modernization Tools Can Change the Game

Gartner® recently released their 2026 Market Guide for AI-Augmented Code Modernization Tools . The report defines AI-Augmented code modernization tools as “software solutions that use specialized AI agents, generative AI, and deterministic analysis to accelerate the transformation of legacy systems. These tools automate and enhance a broad spectrum of modernization activities, including deep code and architecture analysis, software documentation, dependency mapping, risk assessment, migration planning, and refactoring.”¹ EvolveWare is thrilled to be named as a Representative Vendor in the 2026 Gartner Market Guide for AI-Augmented Code Modernization Tools report for its Intellisys Platform, a solution that automates core processes in the modernization journey using an AI-led process.

The report mentions that “AI-augmented tools are poised to fundamentally reshape the economics of application modernization, enabling organizations to achieve greater efficiency, scalability, and compliance.”¹ We’ve seen similar viewpoints asserted by others in the industry such as Anthropic, as they compare these modernization initiatives to ones done manually in the past. And we at EvolveWare agree that these tools can be used to better understand applications, minimize risk, and properly plan out modernization efforts. The ways in which GenAI technologies are now successfully used to speed up modernization efforts is an evolution of a trend that started 20+ years ago with older AI technologies. At the same time, the costs of not modernizing only continue to increase. More and more legacy technologies are experiencing looming end-of-support dates, and the pool of talent that understands these technologies continue to dwindle, making maintaining these systems exponentially more expensive and risky.

The question then becomes how effective these tools and technologies are at delivering successful modernization efforts. The report states that “AI-augmented code modernization tools can materially accelerate legacy application modernization, but cannot fully automate them. Human oversight and experienced developers remain essential.”¹ In our experience, this is what we’ve seen as well. In one of our most recent projects with a Fortune 50 Insurance company, we used the Intellisys platform to document and understand 6.255MM lines of code written in languages including COBOL, Oracle Forms & Reports, legacy .NET and more as well as transform 2MM lines of code to modern .NET and Java using a GenAI led process. While we were able to reach a 95% automation level for complete and compilable code, we include a human in the loop review and testing process by partnering with the client to ensure the code is working as expected prior to going into production. Using this combination of AI, automation, and human oversight, we were able to save the client thousands of developer hours based on their own estimates.

Ultimately, any modernization project involves putting together the right team, processes, and tools in order for it to be successful. With AI-augmented code modernization tools, the economics around modernization are improving but require the correct processes, alignment, and implementation to realize expected gains. The Intellisys Platform is an example of a tool that provides a solution for each of these inputs:

Uses a phased approach to modernization that starts with using its patented extraction technology to provide a comprehensive understanding of your legacy applications.
Presents business logic in multiple formats including a format that is more of an “English-like” translation for non-technical users, enabling collaboration.
Incorporates the latest AI technologies where they provide value such as automated summaries of business rules and code transformation using Intellisys generated context

To learn more about how AI is used in EvolveWare’s Intellisys Platform to help with your modernization efforts, contact us.

1 Gartner, Market Guide for AI-Augmented Code Modernization Tools, Prasanna Lakshmi Narasimha, Howard Dodd, C.A. Swan, Erin Khoo, 2 February 2026

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Application Modernization

Tackling Common Modernization Challenges in Government

Earlier this year, the Pennsylvania Office of Administration (OA) reported $37 million in cost savings attributed to modernized legacy technologies and financial management. The benefits of modernization, when planned and executed using a successful formula, are both known and documented. And yet, the US government itself spends over $100B on IT where most of it is to maintain and operate existing systems. Last year despite the promise from the Department of Government Efficiency (DOGE) to modernize federal technology to reduce inefficiencies, their efforts seemingly made it more difficult for agencies to access and adopt the latest innovations. Government modernization only becomes more imperative as the years go by, particularly with the retirement of the current workforce and the acceleration of innovation. In order to cross the bridge successfully, agencies must first identify and then tackle common modernization challenges in government.

Top Modernization Challenges in Government and How to Address Them

Lack of organizational engagement:

Modernization initiatives require backing from the C-suite and leadership teams. In the public sector, there is constant pressure to justify spending and outcomes and scrutiny is high regardless of the level of government or particular state agency. In addition in any regulated organization, the costs of making changes to the systems that execute or oversee these regulations can seem extraordinarily high, leading to lackluster support from leadership teams. The old adage of “why fix what isn’t broken” rings even more true in government.

→Addressing this modernization challenge in government:

Build your coalition of support early on in the project. Continue to keep this coalition informed as the project progresses.
Present actionable solutions including the business and not just the IT case. Explore words of wisdom from a former CIO of the Air Force.
Keep a transparent record of the project, steps taken and presenting visible milestones and results.
Highlight the actual and opportunity costs of not modernizing to show the true cost of delaying these initiatives.

Enduring legacy of past failures:

It is estimated that only 30% of government app modernization projects have succeeded in the past. With that level of success rate, it can be hard to get past the above mentioned mentality of “if it’s not broken why fix it” as these projects require significant time and resources. It’s important to identify and address why these past efforts may have failed to not make the same mistakes. Reasons include:

Trying to do everything at once or going with a “big bang approach” which was high risk and frequently led to modernization efforts over budget that also underperformed.
The level of manual services that were used to pore over thousands if not millions of lines of code. Particularly in industries dealing with critical changing regulations, this kind of work requires automation to avoid costly errors and mistakes.
Not having a complete understanding of systems prior to planning and modernizing them.

→Addressing this modernization challenge in government:

Use a phased approach to first assess your current set of applications and then plan and implement a modernization effort. See how a state’s Department of Transportation used this in practice.
Select a tool that uses automation including AI technologies to both speed up and reduce the risk involved in these efforts.
Thoroughly assess your current systems including understanding linkages, dependencies, logic and more. The more information and knowledge you have to learn from and build off of, the better the conclusion of a transformation effort.

The cost and ROI trade-off of modernization efforts:

In a recent survey, 71% of IT decision makers in state and local government found the costs of modernizing and transitioning from legacy systems prohibitively high. With such large-budget endeavors, ROI must be significant to compel investment in these projects. But agencies don’t think about ROI in all the same ways that a private organization would. While both sides think about cost savings and internal efficiencies, agencies also think in terms of “public value”, including a heavier emphasis on providing services in a timely manner, mitigating security challenges, and general public opinion on their execution. Estimating these aspects of ROI can be a complex endeavor, but government agencies must show ROI on every dollar spent on technology as budgets can be constrained.

→Addressing this modernization challenge in government:

Use application rationalization as an intermediary step to inventory and assess your portfolio of applications for business, value, cost, and technical fitness. This will start you off on a lower budget, provide more immediate cost savings, and help you prioritize where to invest in modernization efforts.
Include public value related metrics in your ROI calculations. These could include estimated service delivery efficiencies, security incident reductions, or even access improvements.
Consider how modernization could allow the use of new technologies such as AI to add new or increase improvement in public value related metrics. Include these estimations when looking at the trade-offs in moving forward with modernization efforts

Moving Forward with Confidence

Government modernization is not optional—it’s essential. The barriers are real, but they’re not insurmountable. Agencies that have succeeded share common traits: they build broad coalitions early and stay aligned with those coalitions, they embrace phased approaches over big-bang transformations, and they leverage automation to reduce both risk and cost.

Citizens are in need of better and more efficient services. Both the workforce and the technology landscape are evolving. The question isn’t whether modernization is worth it, but whether your agency feels confident to tackle the unique modernization challenges in government. Are you ready? The bridge to modernization is there—it’s time to cross it.

EvolveWare Blog

How can we modernize legacy applications without disrupting business operations?

Communicate, Collaborate and Double Down on the Details

Master the Blueprint Before Building the Future

Maintain Synchronization between Legacy Systems and Modernization Work

Embrace Continuous Modernization for Long-Term Success

Secure your applications for the future

Transform your systems to enhance productivity, deliver intuitive experiences and outplay the competition

Don’t make the same mistake twice. Keep knowledge of your new systems current for on-going application analysis

The Problem with Traditional Migration

Phase 1: Mapping the Legacy Landscape

Phase 2: Refining the Metadata

Phase 3: Building the Blueprint for Spec-Driven Development

Phase 4: Generating Modern Code from Specs

Phase 5: Testing the Target System

Real-World Success: Accounting Firm

Modernization, Re-imagined

FAQ

1. Driving Digital Business Transformation

2. Streamlining Portfolio Rationalization

3. Satisfying Audit and Compliance Requirements

4. Facilitating Modern Platform Selection

5. Architecting for Microservices

6. Bridging the Gap Between IT and Business

The Importance of the "Agile" Approach

The Future of Business Rules in System Analysis and Design: User Stories and Tech Specs

The Hidden Risks of Undocumented Code

Legacy Application Mapping as a Strategic Foundation

The Power of Automated Documentation

Real-World Examples

Agile Documentation Enables Continuous Modernization

Top Modernization Challenges in Government and How to Address Them

Moving Forward with Confidence

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