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Technical debt has long been the scourge of IT departments, but today it’s accumulating faster than ever. High-powered computing, technology innovations like AI and speed to market all require modern, scalable solutions. Unfortunately, many businesses are pressing forward with outdated systems and legacy applications, operating under the misconception that addressing technical debt just slows them down, because it demands time and budget the organization thinks it can’t afford to spend. But in today’s landscape, what organizations really can’t afford are the huge hidden costs of legacy applications, which all directly impact performance, security, and innovation.

“Modernization isn’t just about catching up — it’s about building a future-ready foundation for innovation,” says Paul Done, field CTO, modernization at MongoDB. “The true cost of the status quo isn’t just inefficiency — it’s missed opportunities when the market demands agility, making developers use their valuable time to keep legacy architecture running, versus positioning the company for AI with modern infrastructure and applications.”

The mounting costs of technical debt

IT leaders are well aware of the concrete costs that legacy systems entail. For example, the IT team at a top bank reached out to MongoDB when they discovered that out of their team’s $16 million IT budget, $15 million was being spent just on maintaining legacy architecture. That left the bank with only $1 million for innovation.

There are also hidden costs that directly impact performance, security, and innovation. Not all infrastructures are built for the modern, transformative applications that are vital in today’s competitive market. Furthermore, developer productivity is hampered by technology built on outdated code, which makes it difficult for developers to maintain and implement new features, and also lacks the scalability and resilience required to support modern user demands and development practices.

Plus, these systems make organizations significantly more vulnerable to threats because outdated, brittle architecture can be difficult to update or secure. Some companies lack the necessary institutional knowledge or visibility into the underlying legacy code, which also increases vulnerability. And a lot of these systems are simply not compliant, or no longer supported, increasing the inherent new risks that AI and other modern applications can add to a technology stack. Innovation is completely hamstrung, unless businesses address these potential security gaps. 

“To overcome these challenges and come up to speed in a fast-paced world, organizations need to adopt flexible, high-performance data platforms,” Done says. “By doing so, they’ll reduce infrastructure complexity and maintenance overhead. Modern databases also help organizations improve security with encryption, compliance tools, and automated updates, and architecture designed for high-performance applications helps them scale. All this accelerates AI adoption by enabling real-time, high-quality data access.”

Assessing the extent and impact of architectural limitations

At a high level, determining when it’s time to modernize is about quantifying cost, risk, and complexity. In dollar terms, it may seem as simple as comparing the expense of maintaining legacy systems versus investing in new architecture. But the true calculation includes hidden costs, like the developer hours lost to patching outdated systems, and the opportunity cost of not being able to adapt quickly to business needs.

True modernization is not a lift-and-shift — it’s a full-stack transformation. That means breaking apart monolithic applications into scalable microservices, rewriting outdated application code into modern languages, and replacing rigid relational data models with flexible, cloud-native platforms that support real-time data access, global scalability, and developer agility.

Many organizations have partnered with MongoDB to achieve this kind of transformation. For example, to ensure they didn’t give up any of their performance, storage capacity or support benefits, Indeed tapped MongoDB to streamline their infrastructure efficiency. In just six months they reduced total costs by 27% — far exceeding the company’s initial goals for its modernization initiative.

Security must also be factored in, assessing how much risk legacy systems add to the organization’s overall security posture. And from an operations and innovation perspective, it’s critical to account for future-forward objectives and overall goals. That’s why Bendigo Bank worked with MongoDB to modernize its core banking technology, leveraging generative AI to modernize the bank’s legacy Agent Delivery System (a retail teller operation) in less than three months. The bank was eager to enable its developers to focus on more meaningful innovation so the bank could remain agile in a fast-moving market.

Overall, Bendigo Bank migrated onto MongoDB Atlas at one-tenth of the cost of a traditional legacy-to-cloud migration. Plus, MongoDB solutions helped reduce the development time required to migrate a core banking application off of a legacy relational database to MongoDB Atlas by up to 70%. With new AI tooling, they automated repetitive developer tasks to accelerate developers’ pace of innovation. For example, AI-powered automations reduced time spent running application test cases from over 80 hours to just five minutes.

But modernization projects are usually a balancing act, and replacing everything at once can be a gargantuan task. Choosing how to tackle the problem comes down to priorities, determining where pain points exist and where the biggest impacts to the business will be. The cost of doing nothing will outrank the cost of doing something.

For instance, Toyota Connected recently experienced reliability issues with the legacy database solution underlying the telemetry-based technology that powers connectivity solutions like Safety Connect in more than 9 million Toyota and Lexus vehicles in North America. The company decided to migrate to Amazon Web Services (AWS) and MongoDB Atlas, an integrated suite of data services centered around a cloud database designed to accelerate and simplify building with data. Safety Connect has attained 99.99% availability and the company aims for that number monthly, according to Toyota Connected’s internal measurements.

“We’re usually going in and tackling some of a company’s biggest, ugliest applications,” Done says. “How you design a solution in this AI era is about finding that right partner who can help evolve not only your applications, but your supporting database to consolidate workloads, reduce complexity, and adapt in a rapid, agile way.”

How modern database solutions enable AI-driven workloads

AI is often a game-changing catalyst — once technical debt is eliminated, a company can embrace all the potential it offers. In order to react instantly and make real-time decisions in things like dynamic pricing, fraud detection, adaptive user experiences, and more, AI solutions depend on fluid, instantly-accessible data. Modern databases can make this happen by consolidating structured and unstructured data to help organizations scale without constraints, and to adapt to AI workloads, massive data volumes, low latency operations, and meet the demands of any AI workload while protecting sensitive information both at rest and in motion.

While modernization is often seen as complex and time-consuming, MongoDB has helped speed up and simplify the modernization process in a repeatable manner for many organizations, providing full-stack modernization at both the data and application layer tailored to a company’s specific architecture. The company’s seamless data model and distributed architecture are built to manage data at scale as new technologies emerge, making it the perfect foundation for AI-powered applications. These solutions make developers at least 50% more productive, with some customers seeing productivity gains as high as 70%, Done says.

For Lombard Odier, a gen AI-assisted modernization initiative with MongoDB enabled the bank to migrate code three times faster than previous migrations; move applications from legacy relational databases to MongoDB twenty times faster; and automate repetitive tasks with AI tooling to accelerate the pace of innovation, reducing project times from days to hours. 

The bank’s largest application, PMS (which has thousands of users) manages shares, bonds, exchange-traded funds, and other financial instruments. MongoDB’s ability to scale was key to this system migration, as this system is used to monitor investments, make investment decisions, and generate portfolio statements.

“MongoDB’s AI-powered software-driven approach fully modernizes data and applications at scale in a simplified way,” he explains. “We deliver high-impact results in a short timeframe. We’ve got more than 17 years of experience creating best practices and modern, data-driven applications, so we’re uniquely positioned to understand the ideal end-state of applications for modernization and how to achieve it.”

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