Technical debt is a critical obstacle to modernization
Most companies don’t realize how much money they waste keeping old systems alive. Technical debt, legacy software and infrastructure that should’ve been replaced years ago, is dragging entire industries behind. It’s not just a maintenance problem. It’s locking up talent, budget, and time that should be used to build what’s next. Companies move slower, miss market shifts, and fall behind competitors who are more agile with modern infrastructure.
One bank found that $15 million out of a $16 million IT budget was being used just to maintain aging architecture, leaving only $1 million for innovation. That’s not uncommon. And it’s not sustainable. When nearly all your budget is spent staying operational, there’s nothing left for growth, automation, or AI capabilities that actually move your business forward.
Staying with legacy systems is the bigger risk. The idea that addressing technical debt slows things down is shortsighted. What really slows you down is being stuck patching systems that can’t support real-time decisions, security updates, or product innovation. The cost of waiting is future market share lost to faster, smarter companies.
Paul Done, Field CTO of Modernization at MongoDB, said it best: “The true cost of the status quo isn’t just inefficiency, it’s missed opportunities when the market demands agility.” That hits the point directly. It’s about positioning the organization to win in the near-term and long-term. You can’t drive innovation while stuck dealing with yesterday’s infrastructure.
Legacy infrastructure undermines performance, security, and developer productivity
Outdated infrastructure slows down application performance as well as your teams. Developers spend their time fixing brittle code instead of building high-impact features. Core systems can’t scale to current user demands. And too often, they can’t be secured or patched fast enough to keep up with today’s cyber threats. That’s a real operational risk.
Technical teams know this. The slowdown from old architecture is real, day-to-day friction. Poor infrastructure blocks integration with cloud services. It makes it harder to innovate with AI. And when you try to add new capabilities, legacy code pushes back hard. That’s how momentum dies inside organizations.
There’s also a security layer to this. Many of these systems are based on architectures that are no longer supported or understood. You don’t know what’s in them, but you have to run your entire business on them. That opens up your data and your operations to serious vulnerabilities.
Done makes a key point here. When you upgrade to a flexible, high-performance data platform, you gain speed and control. These platforms let you scale the business, secure your data with end-to-end encryption, and put automated updates to work. You take your developers off maintenance tasks and apply them to innovation. That’s what you want.
The longer you rely on outdated infrastructure, the weaker your position gets.
A complete, strategic modernization process is needed
Most modernization initiatives fail because they stop at surface-level changes. Retiring a few legacy apps or moving some data to the cloud doesn’t solve the underlying constraints. Real progress comes from end-to-end transformation. That means breaking things down and rebuilding them in a way that actually suits today’s needs, scalability, resilience, real-time data access, and the ability to integrate with AI systems.
This isn’t about lifting legacy systems and dropping them into a virtual server. That only moves yesterday’s problems into a different location. What works is re-architecting the stack. You shift from monolithic systems to microservices. You move from rigid relational models to cloud-native databases. And you give developers infrastructure that removes friction instead of adding it.
Security, compliance, and performance all improve when you modernize correctly. So does your ability to adapt to market shifts. When Indeed modernized with MongoDB, they cut their total infrastructure costs by 27% in just six months.
Paul Done, Field CTO of Modernization at MongoDB, made it clear: solving for modernization requires evolving both applications and the databases that support them. The goal is not just maintaining systems, it’s reducing complexity and giving your teams a foundation to move at the speed the market demands. That’s how you build real agility into your tech stack.
Prioritizing modernization based on business impact offers better ROI than postponement
Trying to swap out your entire infrastructure at once rarely works. Success starts with identifying systems that create friction and investing decisively where the business will feel it most, cost, reliability, productivity, or user experience. Being strategic about it delivers more impact than spreading resources too thin across the stack.
The real choice isn’t modernization versus the status quo. It’s proactive investment versus delayed risk. And delay almost always increases the cost. Legacy systems break down over time. Maintenance gets more expensive. Security exposures widen. Eventually, the problems reach users, and when that happens, it’s already late.
Toyota Connected made a targeted move when they migrated part of their connected vehicle platform to AWS and MongoDB Atlas. It led to 99.99% uptime across more than 9 million vehicles in North America, according to Toyota Connected’s internal measurements. That’s an operational figure that directly supports business continuity on a massive scale.
Modernization doesn’t have to be done all at once, but it does need to be done with a clear focus. Start with systems that hold the most strategic weight. Prioritize outcomes that make a visible difference to users, operations, and developers.
Modern databases support Real-Time AI and Large-Scale data operations
AI is becoming central to how organizations operate, compete, and scale. But to do anything meaningful with AI, you need architecture that supports it. Legacy systems weren’t designed to feed real-time data into AI models or support dynamic decision-making at scale. Modern databases are built for that pressure. They consolidate structured and unstructured data, eliminate latency bottlenecks, and provide secure, always-on access to information.
Real-time relevance is the baseline for AI. Whether it’s fraud detection, adaptive personalization, or pricing models that shift based on demand, data can’t sit in silos or wait for batch processing. It has to move instantly across systems. That’s the value of distributed, cloud-native databases, designed to handle scale, volume, and unpredictable demand with speed and minimal friction.
MongoDB’s platform is doing this in real deployments today. Customers have reported productivity improvements of up to 70%, cutting down development cycles and scaling AI-powered features faster than internal tools would allow. Lombard Odier, for example, migrated from legacy systems to MongoDB twenty times faster than past modernization efforts. Their developers were able to move faster, because the underlying infrastructure no longer held them back.
Paul Done, Field CTO at MongoDB, explained that their approach blends data and application modernization under one framework. This allows companies to modernize faster, automate more, and make their systems ready for AI with minimal overhead. When your systems respond in real time and scale globally, AI becomes more than a concept, it becomes a core capability. That’s what modern databases unlock.
Key takeaways for leaders
- Eliminate technical debt first: Legacy systems consume IT budgets and stall innovation; leaders should prioritize retiring technical debt to unlock resources for growth and AI integration.
- Upgrade to improve velocity and security: Outdated infrastructure reduces developer productivity and increases security vulnerabilities; modern platforms enable faster development and stronger risk management.
- Go beyond Surface-Level fixes: True modernization requires full-stack transformation, not just migration; invest in scalable microservices, modern languages, and flexible databases to future-proof operations.
- Modernize with strategic focus: Replacing everything at once isn’t sustainable; executives should prioritize modernization projects based on business impact, operational pain points, and ROI potential.
- Build an AI-Ready data foundation: Modern databases support real-time AI at scale; leaders should adopt platforms that provide unified data access, automation, and low-latency infrastructure to stay competitive.
