Coding a clean slate: how AI is writing off tech debt

For decades, enterprises have wrestled with the burden of technical debt. Legacy systems, outdated code and aging infrastructure can become an anchor that slows innovation, increases costs and limits the ability to adapt. Modernisation efforts are often daunting, with migrations stretching over years, consuming vast resources and carrying significant operational risk.

Today, however, artificial intelligence is changing the equation. Enterprises are discovering that AI is not only a driver of innovation but also a powerful tool for clearing the roadblocks of the past. Through agentic AI and modernisation solutions, organisations are accelerating their move to modern technology stacks and shrinking timelines that once seemed immovable.

In the past, the only way to address tech debt was to throw people, time and money at the problem. IT teams would painstakingly analyse legacy code, plan migrations line by line and hope that their new environments worked as intended. These projects often failed to deliver on time or budget, leaving many organisations hesitant to even begin.

AI has now reached a point where it can do much of the heavy lifting. Agentic AI refers to intelligent agents that can analyse, plan and execute on complex tasks. In the modernisation context, these agents can examine legacy systems, rewrite code in newer languages, test functionality and prepare applications to run in cloud-ready environments.

The impact is already being felt. Hyperscalers have introduced tools such as AWS Transform to modernise VMware, mainframe, .NET and Java environments. Similarly, Snowflake and Databricks provide capabilities to help enterprises move away from older data platforms. What once took years can now be achieved in months, as AI converts, tests and deploys code more quickly and reliably than traditional approaches.

Not all organisations have legacy systems that fit neatly into off-the-shelf modernisation tools. Many run bespoke or niche platforms with unique requirements that standard solutions cannot address.

Here, custom agentic AI solutions are key. Enterprises can now work with providers to develop tailored AI-driven agents that understand the quirks of their environment and build migration paths specific to their needs. This unlocks the ability to innovate without abandoning mission-critical processes and brings agility even to highly specialised industries.

At the same time, it is important to approach AI-driven modernisation with care. Rushing into projects without proper governance, clear data policies or realistic expectations can lead to unintended outcomes. Sensitive data must be handled in line with organisational policies, and employees need clear guidance on which tools are appropriate for which workloads. AI is a powerful enabler, but success still depends on leadership, planning and oversight.

Waiting to modernise comes at a cost. Vendors of legacy systems are increasing licensing fees and reducing support, leaving organisations with fewer options and higher bills. Meanwhile, competitors that embrace AI-driven modernisation gain the agility to innovate, cut costs and respond faster to market demands.

By tackling tech debt proactively, organisations free their teams from maintaining outdated systems and instead focus on developing new capabilities and services. This shift not only improves efficiency but also boosts morale by allowing IT teams to contribute to forward-looking projects rather than keeping the lights on.

Tech debt has long been seen as a barrier to innovation. With AI-powered approaches, organisations can turn this burden into an opportunity to modernise and compete at a higher level. Rather than being constrained by the past, businesses can build systems that meet the needs of tomorrow, today.

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