Much of the current AI conversation is centred on experimentation: which applications to test, which departments to prioritise, how to manage exposure. Those discussions are necessary. But they overlook a more structural consequence of delay.
Technical debt is a familiar concept across enterprise IT. It arises from legacy platforms, short-term fixes and postponed upgrades. Over time, it constrains agility and increases cost. Eventually, it demands remediation.
A different kind of liability is now emerging. It does not stem from past shortcuts, but from postponing the structural adjustments required to support AI at scale. This is forward-looking technical debt, created by hesitation rather than haste.
In AI, the effects are already apparent. Each quarter spent debating readiness instead of building it increases the distance between legacy operating models and AI-enabled competitors. As models advance and user expectations shift, that distance grows.
While companies continue to debate the timing of adoption, their room for manoeuvre narrows. Time spent confined to limited pilots or extended governance reviews does not preserve optionality indefinitely. It allows the distance between internal capability and external expectation to widen.
By 2026, more than 75% of organisations are expected to face moderate AI-related technical debt. The impact will not be limited to slower automation. It will reflect a deeper misalignment between enterprise systems designed for static processes and a workplace that is becoming adaptive and AI-assisted.
Technical debt, redefined
Here’s the paradox: organisations are either rushing into unsuccessful AI pilots that create immediate technical debt, or they’re avoiding AI entirely and creating forward-looking debt through inaction. Both paths lead to the same place—systems that can’t support the future of work.
AI is fundamentally changing how people interact with systems and how work gets done. When AI becomes the interface, for customers and employees, organisations without AI-ready foundations will find themselves unable to compete on speed, efficiency, or experience.
The companies that hesitate aren’t just missing out on automation benefits today. They’re building a deficit that grows exponentially as AI capabilities advance. Each new model release, each successful implementation, each customer expectation shift adds to the debt. Unlike legacy systems that degrade slowly, this gap increases.
AI changes the operating model
Addressing forward-looking technical debt requires a shift in approach. The priority is not additional tooling or a proliferation of pilots. It is establishing the conditions for AI adoption that strengthens the organisation rather than adding complexity.
Organisations making sustained progress tend to take a phased route. They focus on whether their data, systems and operating environment can support AI at scale. They treat adoption as an ongoing capability rather than a one-off project.
That work begins with visibility across the technology estate. Leaders need clarity on which systems can support AI workloads, where data quality limits outcomes and which processes are sufficiently stable to automate. Without that foundation, new capabilities are forced onto brittle environments.
AI can then be introduced incrementally. Systems are modernised where constraints limit scale. Governance evolves alongside deployment. Over time, this builds resilience rather than compounding risk.
Clear debt to stay competitive
Forward-looking technical debt does not appear on a balance sheet. Its impact shows up in extended product cycles, manual workarounds, integration backlogs and employee frustration. It becomes visible when competitors embed AI-assisted services as standard and customers begin to expect the same.
Timing therefore becomes strategic. AI capability builds cumulatively. Early investment in data integrity, workflow design and interoperable systems creates a platform for continuous improvement. Each iteration becomes easier to implement and scale.
Delay produces a different trajectory. Complexity increases. Retrofit costs rise. Strategic choice narrows.
The issue is no longer whether AI should be adopted in principle. It is whether leadership teams are prepared to treat system readiness as urgent.
Reducing forward-looking technical debt requires action before competitive pressure dictates the pace. It involves aligning technology modernisation with operating model reform and recognising that steady progress now limits the need for reactive acceleration later.
AI adoption will continue across industries. Organisations that invest in robust foundations shape how it transforms their operations. Those that postpone structural adjustment may find the constraints more commercially significant and more expensive to resolve.
Adam Spearing
Adam Spearing is Vice President of AI Go To Market, EMEA at ServiceNow. He has more than 30 years of experience in the global software industry, with leadership roles at SUSE, Salesforce, Hewlett Packard and Sun Microsystems.
