McKinsey refers to Tech Debt as “Dark Matter” - you can infer its impact, but you can’t see it or measure it.
AI and machine learning are reshaping industries, offering new ways to innovate and drive efficiency. However, many organisations face a persistent obstacle to fully leveraging these advancements.
Technical debt - inefficiencies introduced through shortcuts or limitations in legacy systems - acts as a bottleneck to innovation. When left unmanaged, it hampers scalability, increases operational costs, and slows down the time-to-market for new solutions. With research stating that organisations pay an additional 10 to 20 percent to address tech debt on top of the costs of any project.
The term "technical debt" was first coined by Ward Cunningham, a software developer and one of the creators of the Wiki, in 1992. Introducing the concept during his work on a financial system, he used the analogy of financial debt to describe the trade-offs developers make when they write code that is "good enough" to meet immediate needs but may require future rework to ensure maintainability, scalability, and performance.
Technical debt can be compared to financial debt: a decision made to take a “shortcut” today with the expectation of “paying it back” in the future. Just as financial debt accumulates interest if not repaid, technical debt compounds over time, increasing maintenance costs, creating inefficiencies, and potentially leading to system failures.
Tech debt amounts to 20 to 40 percent of the value of some organisation’s entire technology estate (before depreciation), and continues to rise. With a large number of organisations undergoing modernisation programs that are unsuccessful in reducing technology debt.
Types of Technical Debt
Technical debt can manifest in different ways, depending on the systems and processes involved.
Code Debt:
Poorly written or unoptimised code, which can often be introduced during rushed development cycles.
Example: Code pulling unnecessary or excessive data through layers of a system, or lack of application of standard design patterns.
Architecture Debt:
Legacy system designs that are no longer scalable or compatible with modern technologies.
Example: Monolithic systems that cannot integrate with cloud-based services.
Process Debt:
Inefficient workflows or lack of standardised practices, creating bottlenecks in development or deployment.
Example: Only manual testing instead of introducing automated testing, slowing down release cycles.
Data Debt:
Poor data management practices, such as incomplete datasets or lack of data governance.
Example: Data pipelines that fail to support real-time analytics.
How Technical Debt Can Impact Innovation
69% of enterprises agree that technical debt limits their capacity to innovate, highlighting the importance of addressing this issue to unlock growth opportunities.
Unmanaged technical debt can directly hinder an organisation’s ability to innovate through factors such as:
Resource allocation:
Teams spending excessive time maintaining legacy systems instead of working on new projects, slowing innovation.
Innovation challenges:
Legacy systems burdened with debt struggle to integrate with emerging technologies like AI or cloud platforms.
Reduced agility:
Organisations weighed down by technical debt take much longer to define, build and deploy new features.
Balancing Technical Debt with Technology Adoption
To navigate the dual pressures of reducing technical debt and adopting emerging technologies, IT leaders can take a number of approaches. Below are some actionable strategies through our experiences in working with organisations to address tech debt whilst delivering new technology initiatives:
Prioritising Tech Debt
Not all technical debt is created equal. If possible, focus on the debt that directly blocks the adoption of new technologies, appreciating that this includes both a strategy for reducing existing technical debt and also a process for minimising and managing technical debt as it arises on an ongoing basis.
Debt mapping: Identify the areas across teams and functions where technical debt most severely impacts innovation goals.
Risk assessment: Evaluate the potential business risks of leaving certain technical debt unaddressed, such as downtime, data security vulnerabilities, or reputational damage.
PoCs and Pilots: Test modernisation efforts on smaller components of high-priority systems before scaling the approach to the entire system.
Creating Dual Roadmaps
Develop parallel roadmaps that simultaneously address technical debt and plan for new technology rollouts.
Portfolio prioritisation: Categorise systems into "legacy-critical," "legacy-noncritical," and "innovation-enabling" to create a clear hierarchy for roadmap alignment.
Milestone integration: Tie technical debt reduction milestones to measurable innovation outcomes, such as improved time-to-market or reduced operational costs.
Resource allocation: Allocate dedicated resources for each roadmap, ensuring neither debt reduction nor innovation projects compete for the same bandwidth.
Considering Middleware and Integrations
Middleware can bridge the gap between legacy systems and modern technologies, enabling progress without a complete system overhaul of existing systems.
Deploy APIs: Use APIs to allow legacy systems to easily integrate with modern platforms.
Data integration: Look at consolidating and normalising information from disparate systems.
Robotic Process Automation (RPA): Use RPA to automate repetitive processes that interact with both old and new systems.
Data aggregation: Leverage tools that aggregate data from legacy systems, preparing it to be used by AI, without extensive re-engineering.
Focusing on Data Modernisation
Gaining success from AI and machine learning technologies rely heavily on the quality of the data you provide.
Data cleansing: Implement processes to clean and standardise legacy data for AI/ML readiness.
Data migration: Gradually migrate data from legacy systems to modern, cloud-based solutions.
Data replication: Use data replication tools to create real-time copies of test or production (potentially anonymised/obfuscated) legacy data for AI Proof of Concepts while keeping the original system intact.
Using the New to Manage the Old
AI and automation tools can streamline technical debt identification and reduction, freeing up resources for innovation.
Code analysis: Use AI-powered tools to analyse codebases, identify high-risk areas, and suggest improvements.
Process automation: Automate repetitive maintenance tasks to reduce manual intervention.
Predictive modelling: Deploy AI to forecast the impact of technical debt on future innovation timelines.
Implementing Iterative Innovation
Encouraging teams to innovate within the constraints of legacy systems while incrementally addressing technical debt can help to promote continuous improvement.
Empower teams: Allow developers to propose and implement small-scale improvements to legacy systems.
Incremental refactoring: Dedicate a portion of sprint cycles to refactor critical code or improve integration points.
Experimentation sandboxes: Create isolated environments where teams can test new technologies alongside legacy systems without risk to operations.
Addressing Legacy to Keep Pace With Innovation
Technical debt remains one of the most significant challenges for organisations seeking to adopt emerging technologies like AI and machine learning.
As research reveals, 69% of enterprises in the UK cite technical debt as a barrier to innovation, with rising costs and inefficiencies stifling growth opportunities. Legacy systems, while essential to maintaining operational stability, often struggle to keep pace with modern demands, creating bottlenecks in scalability, integration, and agility.
However, this challenge is not insurmountable. By implementing parallel roadmaps, targeting critical technical debt while modernising incrementally, IT teams can speed up delivery goals. Whilst exploring how to leverage existing legacy systems, without the need for a complete overhaul, can enable them to work smarter and in tandem with new technologies.
