How to address technical debt

Organisations are racing to adopt AI while grappling with technical debt. Forrester predicts that more than 50% of technology decision-makers will see their technical debt rise to a moderate or high level of severity in 2025, with that number projected to reach 75% by 2026.

Technical debt is a future cost born from building software quickly instead of focusing on the long term. When developers sacrifice quality for speed, they eventually reach a point when resources are needed to fix, redesign, or replace a solution.

Left unchecked, technical debt silently builds until an organisation reaches a tipping point, draining resources and stifling efficiency. For organisations to successfully implement and scale technologies such as AI, leaders must address technical debt.

Consider that three in five AI projects will be abandoned through 2026 due to a lack of AI-ready data, according to Gartner. Technical debt can be a factor in hindering data quality, governance, and consistency. For organisations to exploit the advantages of AI, they must first get their data ready, which means tackling technical debt head-on.

Let’s explore three ways to rein in technical debt.

Target and update technology

Challenge: Many workflows are built on outdated legacy systems that make it difficult to integrate new technologies.

Solution: Target and update technology that creates the biggest return on investment (ROI).

The first step to reduce technical debt is identifying where it exists. One of the best ways to track and measure technical debt is by looking at development cycle time. Longer cycle times imply that existing code is inefficient. Known as code debt, this impacts an organisation’s overall technical debt.

AI ratchets up the speed that legacy systems become obsolete. For organisations to stay on top of technical debt, they must ensure that AI works with their existing technology stack.

New technology sitting on top of old technology hinders efficiency and productivity. Deciding which legacy systems to update or replace is the main challenge in the context of technical debt.

An evaluation can be based on four factors: business value, financial resources, direct risk, and indirect risk. Alongside this initial assessment, research which systems and processes provide the best ROI for the organisation.

Get the data right

Challenge: Siloed and disparate data limits technological effectiveness, increasing technical debt.

Solution: Increase oversight by bringing data together in one easy-to-view platform.

All technology investments are only as effective as the data that flows through them. Modernising an organisation at scale requires getting the data layer right. Once leaders have an overview of their data, they can see where blockages exist and address challenges that arise as a result of technical debt.

For AI to work seamlessly, organisations must ensure data is clean. Without robust data governance, technical debt becomes entrenched and holds organisations back from adopting new technologies. If data is siloed or missing, AI can’t use that data to inform results, making it less effective.

Siloed data increases the complexity of system maintenance, creating more work for IT teams and reducing the efficiency gains that AI presents. The ServiceNow Enterprise AI Maturity Index found that 56% of Pacesetter organisations—those that are ahead in AI adoption—have made significant progress connecting data and operational silos, compared to 41% of others.

A strong data layer enables organisations to confidently implement AI, powered by real-time intelligence. ServiceNow Workflow Data Fabric connects data anywhere, all on one platform. By doing so, it can help organisations better track and measure technical debt and see opportunities for improvement.

Centralise and co-own the strategy

Challenge: Technical debt prevents the adoption of new technologies.

Solution: Create a centralised strategy for technology use and deployment, making sure that employees are invested and on board.

Encouraging and empowering employees to take ownership of their technology strategy creates more accountability, which in turn identifies delays caused by technical debt. Organisations must establish clear guidelines and expectations for the ongoing management of technical debt.

Our research found that fostering a culture of collaboration and autonomy empowers employees. About 70% of Pacesetters say they work to promote cross-functional alignment of AI across the enterprise.

Organisation-specific guidance for reducing technical debt should clearly outline what success looks like, who will lead the effort, how decisions will be made, and how results will be measured and overseen. The three factors of centralisation, collaboration, and co-ownership must come together to create consensus and reduce technical debt.

The bottom line

The rush to adopt new technologies without a strategic, long-term plan is creating a future cost that will hinder innovation, reduce productivity, and ultimately lead to customer dissatisfaction.

Overcoming this challenge requires a holistic approach that involves data governance, fosters a collaborative culture, and prioritises the most impactful modernisation changes to the organisation.

Find out how ServiceNow can help you connect your data.