Skip to main content

Overcoming the Challenges of Legacy Applications in Digital Transformation

Venkat Pillay
CloudFrame

In today's rapidly evolving business landscape, digital transformation is no longer a nice-to-have but a necessity for survival. Enterprises that have embraced digital transformation are poised to reap significant rewards, with estimates projecting that they will account for over half of the global nominal GDP by 2023, valued at $53 trillion or more.

However, digital transformation is challenging, especially for finance, manufacturing, and oil and gas businesses. These companies face the daunting task of modernizing legacy IT systems and applications, which often consume a significant portion, sometimes up to 40%, of their IT budget while keeping up with the demands of a fast-paced, digital-first world. One study estimated that just 30% of initiatives achieve their transformation targets, implying works needs to be done to understand and overcome the inclusion of legacy applications in digital transformation initiatives.

The Challenges of Legacy Application Transformation

Transformation requires overcoming multiple obstacles. These include:

■ Understanding the scope and implications of legacy applications and systems – Many organizations are finding that, through a lack of available skills and knowledge, they don’t fully understand how the application works or the risks exposed if it is transformed. This also means a full view of inter- and intra-dependencies is often missing, with many integrations with other applications and data hidden and only coming to light when they break.

■ Equivalency – the applications being transformed may be decades old. Still, they deliver consistent and expected results. Any migration or transformation of these applications must provide equivalent data results. In some cases, they will also need to produce equivalent functional results.

■ Controlling scope – Simultaneous conversion of data and logic transformation adds layers of complexity, making it harder to demonstrate how the transformation is progressing and achieving equivalency.

■ Changing Business Needs – Successful transformation can take a long time to get right. Success becomes more complicated because business needs do not go dormant while legacy transformation is in progress. Continuous modernization is required to transform the legacy application and integrate new business requirements as they are defined.

■ Risk aversion – With various regulatory and customer-driven demands for continuous, error-free operations, many organizations will not accept any disruption to outcomes.

How do businesses overcome these challenges of including legacy applications in their digital transformation initiatives?

Guidance for Overcoming the Challenges

Embrace Incremental Modernization

Using an approach of strategic application selection, transformation, and implementation allows organizations to learn, improve, and expand modernization initiatives. Incremental modernization relies on a strategy of repeated success, with each application modernization project leveraging the tools, processes, and knowledge of previous transformation efforts. Confidence and momentum can be built by demonstrating how transformation can be achieved with proven equivalency and precision without disrupting business functions or SLAs.

Conduct Extensive Discovery and Assessment

Understanding the application's breadth and depth is a critical success factor. This may be straightforward if the application has a narrow purpose or may be expansive if the application is comprised of multiple inputs and outputs and far-reaching integrations. Automated discovery and assessment tools are becoming more sophisticated, with many adopting AI-enabled processes that produce comprehensive guidance and insights.

Leverage Automation

Automated transformation reduces time, cost, and risk. An automated transformation engine that can be configured based on your requirements ensures consistent, predictable, and maintainable applications. Transformation engines that learn from previous iterations, recognize patterns, optimize processes, and produce readable and understandable new source code are invaluable to transformation success. If most of your legacy application code is transformed using an automated transformation engine, you can then concentrate on the small percentage that is genuinely critical and must be manually written.

Comprehensive Testing and Validation

Acceptance of the transformed application requires demonstrated proof of meeting the business and IT requirements. Understanding how functional and data equivalence, precision, integrations, and even DevOps acceptance is critical. Success in this area demands thorough testing and validation plans and processes.

Mission Critical Logic and Processes Fit for the Future

Including legacy applications in digital transformation initiatives can be a challenge. But the days of thinking a COBOL application can’t be touched or leveraged in the future business process are gone. With the right approach, deep knowledge, automated tools, and comprehensive validation, legacy applications can be transformed, and their mission-critical logic and processes brought into the organization's digitally transformed future.

Venkat Pillay is CEO and Founder of CloudFrame

The Latest

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Overcoming the Challenges of Legacy Applications in Digital Transformation

Venkat Pillay
CloudFrame

In today's rapidly evolving business landscape, digital transformation is no longer a nice-to-have but a necessity for survival. Enterprises that have embraced digital transformation are poised to reap significant rewards, with estimates projecting that they will account for over half of the global nominal GDP by 2023, valued at $53 trillion or more.

However, digital transformation is challenging, especially for finance, manufacturing, and oil and gas businesses. These companies face the daunting task of modernizing legacy IT systems and applications, which often consume a significant portion, sometimes up to 40%, of their IT budget while keeping up with the demands of a fast-paced, digital-first world. One study estimated that just 30% of initiatives achieve their transformation targets, implying works needs to be done to understand and overcome the inclusion of legacy applications in digital transformation initiatives.

The Challenges of Legacy Application Transformation

Transformation requires overcoming multiple obstacles. These include:

■ Understanding the scope and implications of legacy applications and systems – Many organizations are finding that, through a lack of available skills and knowledge, they don’t fully understand how the application works or the risks exposed if it is transformed. This also means a full view of inter- and intra-dependencies is often missing, with many integrations with other applications and data hidden and only coming to light when they break.

■ Equivalency – the applications being transformed may be decades old. Still, they deliver consistent and expected results. Any migration or transformation of these applications must provide equivalent data results. In some cases, they will also need to produce equivalent functional results.

■ Controlling scope – Simultaneous conversion of data and logic transformation adds layers of complexity, making it harder to demonstrate how the transformation is progressing and achieving equivalency.

■ Changing Business Needs – Successful transformation can take a long time to get right. Success becomes more complicated because business needs do not go dormant while legacy transformation is in progress. Continuous modernization is required to transform the legacy application and integrate new business requirements as they are defined.

■ Risk aversion – With various regulatory and customer-driven demands for continuous, error-free operations, many organizations will not accept any disruption to outcomes.

How do businesses overcome these challenges of including legacy applications in their digital transformation initiatives?

Guidance for Overcoming the Challenges

Embrace Incremental Modernization

Using an approach of strategic application selection, transformation, and implementation allows organizations to learn, improve, and expand modernization initiatives. Incremental modernization relies on a strategy of repeated success, with each application modernization project leveraging the tools, processes, and knowledge of previous transformation efforts. Confidence and momentum can be built by demonstrating how transformation can be achieved with proven equivalency and precision without disrupting business functions or SLAs.

Conduct Extensive Discovery and Assessment

Understanding the application's breadth and depth is a critical success factor. This may be straightforward if the application has a narrow purpose or may be expansive if the application is comprised of multiple inputs and outputs and far-reaching integrations. Automated discovery and assessment tools are becoming more sophisticated, with many adopting AI-enabled processes that produce comprehensive guidance and insights.

Leverage Automation

Automated transformation reduces time, cost, and risk. An automated transformation engine that can be configured based on your requirements ensures consistent, predictable, and maintainable applications. Transformation engines that learn from previous iterations, recognize patterns, optimize processes, and produce readable and understandable new source code are invaluable to transformation success. If most of your legacy application code is transformed using an automated transformation engine, you can then concentrate on the small percentage that is genuinely critical and must be manually written.

Comprehensive Testing and Validation

Acceptance of the transformed application requires demonstrated proof of meeting the business and IT requirements. Understanding how functional and data equivalence, precision, integrations, and even DevOps acceptance is critical. Success in this area demands thorough testing and validation plans and processes.

Mission Critical Logic and Processes Fit for the Future

Including legacy applications in digital transformation initiatives can be a challenge. But the days of thinking a COBOL application can’t be touched or leveraged in the future business process are gone. With the right approach, deep knowledge, automated tools, and comprehensive validation, legacy applications can be transformed, and their mission-critical logic and processes brought into the organization's digitally transformed future.

Venkat Pillay is CEO and Founder of CloudFrame

The Latest

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...