Each tech wave saw many organisations restructure just enough to function. That unfinished work? That’s operational debt. AI will relentlessly expose this.
Operational debt has been quietly compounding across decades of tech revolutions — tolerated, deprioritised, and left to stagnate in favour of flashier transformation goals. It takes many forms: fractured processes, patchwork systems, siloed data, clunky approvals, mismatched roles, and decades-old workarounds that no one remembers designing.
Critically, it isn’t just technical. It’s organisational, structural, procedural, and it often flies under the radar — until AI arrives.
That’s when the constraints become visible, usually mid-flight, when transformation is already underway. By then, what should have been strategic readiness becomes crisis mitigation — if it’s even salvageable.
Consider the following scenario:
The criticality of having functional high-quality data has increasingly skyrocketed as the Data Revolution has taken hold. All roads led to holistic data-centric culture, but many organisations have gravitated towards an MVP mindset: get data working enough for now, and fix the rest for later.
Pragmatic short-termism has calcified into operational fragility, and this goes far beyond data teams or IT departments.
Finance teams built brittle reporting models, supply chain functions stacked workarounds on top of legacy ERPs, factory operators rigged up procedural hacks, customer operations layered spreadsheet fixes.
Operational debt like this hasn’t stayed still — it has compounded year after year.
Operational debt backlogs are everywhere, and unlocking AI success increasingly depends on clearing them.
Most organisations can’t tangibly see their operational debt so the first shift is visibility.
Leaders must treat operational debt like any other risk category: define it, surface it, and evaluate its impact on AI readiness. Because of its scale, this can’t be a full-body scan. What’s needed is a strategic diagnostic to spot the patterns, bottlenecks, and risks most likely to derail priority use cases.
The flow of new debt must also be stopped. Many digital transformations today are still taking the MVP route and mindset must change. Every shortcut must now be judged against a critical question: Will this block AI tomorrow?
Only then can targeted resolution strategies begin.
If the AI use case truly matters, clearing operational debt is no longer optional — it’s a prerequisite.
