Omar Abdulaal – Senior Engineering Manager at Finaira
In a fast-paced and rapidly evolving world of Software Engineering, we often face the classic Speed vs Quality dilemma. We view speed as the ability to reach outcomes and insights, thus, effectively the ability to pivot and make business decisions, while quality is the “antidote” or gatekeeper. As engineering leaders, we are realizing a hard truth: True speed is unlocked only through quality.
We use “technical debt” to describe shortcuts and trade-offs, and the rework that systems need to redress these issues. Most teams fall into the trap of only measuring known debt. However, there is a “hidden debt” far more pervasive-lost in thousands of lines of legacy code, years of undocumented decisions, and outdated technology.
The true danger of tech debt is that not only do we lose the ‘why’ behind the logic, but it also amounts to higher onboarding costs and fear of change from developers, creating an ‘innovation ceiling’ that quietly, but firmly stifles the company’s growth. According to McKinsey research: Tech debt can account for up to 40% of an organization’s estate value. In the intersecting circles of FinTech and AI, quality becomes non-negotiable. Failing to manage this debt isn’t just a technical oversight; it’s a strategic liability.
So, how do we eliminate or avoid the technical debt? Unfortunately, there is no easy answer to the question. However, with AI, we are entering an era where we no longer need to choose between speed and quality. AI is transforming technical debt management from a manual chore to an automated health check.
1. Documenting the “Why”: ADRs
One of the largest sources of technical debt is the gap between knowledge or decisions and the written code. AI can autonomously synthesize and update ADRs (Architecture Decision Records) from existing codes & changes, Slack notes, and Jira tickets. This effectively ensures knowledge debt is kept to a minimum, reducing the onboarding costs.
2. Automated debt detection and mapping
You can’t solve what you don’t know. And while code scanning tools have existed for a long time, AI-driven code assistants and intelligence goes deeper, enabling analyzing PRs for outdated technologies and libraries, and beyond to identify “behavioral debt”. LLMs can also spot hotspots where velocity is dropping and churn is high, creating a map of functional bottlenecks for teams.
3. Predictive Refactoring
The most dangerous debt is the kind you don’t know you have until it breaks. AI models can now predict which code sections are most likely to fail based on historical bug patterns and structural complexity. By flagging these “ticking time bombs,” engineers can prioritize refactoring tasks based on risk and ROI rather than just “cleanness.”
For leaders to move from reactive firefighting to proactive debt management, they should start with adopting three core principles:
- Understand the business cost of your debt: McKinsey’s report suggests a 10% to 20% extra “tax” is paid on top of the cost of a new project to address tech debt. It’s critical that leaders can translate the cost of the debt into business metrics that are meaningful. Whether it’s added time-to-market, operational cost from infrastructure, or lower innovation rate, mapping and communicating the cost of the debt could be the difference between having it accumulated or reduced. When you can show a CFO that $500k of the annual budget is being “taxed” by specific legacy modules, the conversation shifts from technical aesthetics to ROI.
- Invest your time wisely: Not all debt is managed equally, in the same way. A mentor once told me: “Spend less time on easy-to-change things, and more time on the hard choices.” Prioritize your time dedicated to debt remediation on ‘high impact’ areas, guided by the remediation scores. Some tech debt isn’t even worth repaying at all.
Commit to and protect the decision to manage your debt:
Managing debt is one of those topics that everyone agrees is important, yet extremely difficult to execute due to shifting needs, etc. Embedding the practice of managing debt in your organization strategy and OKRs is key. This allows you to keep track of and assess performance against the rate of new features released.
Technical debt is not a failure; it is a byproduct of success. A product with debt is a product that is being used, changed, and scaled. However, in an industry as volatile and fast-moving as Fintech, allowing that debt to accumulate unchecked is a choice to become obsolete.
By leveraging AI as a strategic partner in our development, we can finally break the cycle of “bandage-and-build.” We can build systems that are not just functional today, but “reinvention-ready” for the innovations of tomorrow.