Legacy enterprise applications are often seen as problematic and subjected to large-scale
rip-and-replace or rewrite efforts. When this modernization of complex, aging applications happens,
it focuses on the oldest technology and platforms rather than the most valuable capabilities, often
leaving many organizations with scaling challenges and restricting the best value of their applications
modernization.
Looking back at the evolution of IT and software engineering over the past two decades, one thing is
evident: “Change is constant.” There is paradigm-shifting innovation in computing hardware, cloud,
technologies, and software development methodology and processes. This evolution has allowed IT to
keep up with ever-changing business demands. However, it has not been easy or cost-effective. Even
today, most IT budgets are consumed by maintaining legacy applications and staying relevant with
patches and upgrades. The modernization costs have too often depleted IT budgets before business
benefits are even unlocked, let alone realized.
Another key reason a leader cannot overlook legacy applications is the takeaways from the impact of
the COVID-19 pandemic. Cybercrime, non-digital service assets/offerings, and on-premises applications
that are not accessible are just a few examples of how the landscape of grave threats to business has
grown in complexity and range. Therefore, it is imperative to prepare for the survival of business
continuity and build business resilience to be better prepared for the next hurricane, whenever it may
come.
In the AI era and enterprise application modernization, technology leaders are increasingly turning to AI
tools and generative AI techniques to simplify the process and unleash the full potential of legacy
systems. According to Gartner, by 2025, 80% of legacy applications will be considered unsuitable
for modernization due to their inability to support evolving business needs and technological
advancements. Traditional approaches, such as rip-and-replace methods, often prove ineffective
and expensive, resulting in extended modernization timelines and suboptimal outcomes.
