Technical debt used to be easy to spot. You could walk into a development team and see it in the codebase, the lack of tests, or the patchwork of quick fixes that no one wanted to touch. It had a smell to it. You knew that every release took longer, and every new feature came with a new risk. Over time, that debt could be measured, managed, and eventually paid down. But now a different kind of debt is accumulating quietly in companies everywhere, and it is one that few boards or leadership teams have learned to recognise. It is the debt of unmaintainable or misused AI models.
I often find that AI models have been built with great enthusiasm but little or no thought for what happens next. A model may perform well in its first quarter, but after that no one knows who owns it, how it was trained, or when it should be retrained. In some cases, the data pipeline that feeds it has changed, yet the model still runs as if nothing has. The result is a deterioration in accuracy and trust that no one sees. That is where the new technical debt begins to take hold.
Invisible debt on the balance sheet
From a board perspective, this kind of debt is almost invisible. It does not appear on the balance sheet, and unless someone is asking the right questions, it can go unnoticed until it begins to distort decisions or expose the company to risk. The governance challenge is that AI systems are often approved as one-off projects, without defining how they will be validated or monitored. I have seen boards approve AI investments on the promise of automation or insight, only to discover later that no one planned a target operating model which included those models.
Good governance requires that AI models be treated as living assets, not as completed projects. They need care, documentation, and a defined owner. The NIST AI Risk Management Framework, and the UK government’s guidance on AI assurance, both point to this principle of continuous oversight. Yet very few organisations have translated that advice into practical accountability structures. A board may have an audit committee, but does it know how to audit an algorithm? Would your own board know whether a model’s training data is still representative, or whether the bias controls are still valid? These are not abstract ethical questions; they are operational risks that accumulate over time.
The maintenance nobody planned for
From the technical side, as a CTO I see the same pattern play out with regularity. A model is created in haste to deliver a proof of concept. It gets attention, perhaps even funding, and before long it becomes part of a critical process. But no one has built a path to retrain it, or documented the environment it depends on. The original data scientist has moved on, and the DevOps team cannot reproduce the results. At that point, the company has inherited a black box that nobody understands and everyone fears to touch.
There is also the problem of misuse. I have seen well-intentioned teams apply a model far outside the conditions it was designed for. A model trained to classify one type of behaviour is suddenly used for a different market, or a different customer base, because it is already available and seems to work. Over time, errors creep in, confidence falls, and the damage to trust becomes hard to repair. These are not stories of bad faith, but of misplaced optimism and under-resourced governance.
The practical reality is that maintaining AI systems requires a blend of skills and disciplines. It is not just about data engineering or model tuning; it is about creating an organisational rhythm for monitoring, retraining, and verifying alignment with business objectives. That rhythm often does not exist, because no one has made it part of the company’s governance framework.
Where strategy meets stewardship
This is the point where board governance and technical leadership must meet. The board should define the principles: who owns an AI asset, how risk tolerance is set, and how oversight is reported. The CPTO’s role is to translate those principles into practice. That means defining retraining schedules, maintaining model version control, documenting assumptions, and setting clear operational thresholds for performance and drift.
In one recent engagement, I was asked to assess an AI model that had been driving pricing recommendations for nearly two years. The team could not reproduce the original training environment, and the data that once powered it had altered significantly as the systems which fed it changed. When the model was rebuilt with current data, its recommendations diverged from production output by almost 20 percent. No one had noticed, because no one was monitoring for drift. The problem was not malice or neglect; it was the absence of a governance loop connecting the board’s understanding of risk with the engineering team’s maintenance practices. That absence is what turns good technology into technical debt.
Paying down AI debt before it compounds
AI technical debt is not about broken code; it is about broken accountability. The models may still run, but without a clear plan for maintenance and ethical use, they become liabilities that accumulate interest every day they go unchecked. The longer they run without retraining, review, or ownership, the harder it becomes to fix them without disrupting the business.
If you are on a board, ask yourself whether your AI assets are being treated as products or as processes. If you are leading technology delivery, ask whether you have the governance, tooling, and culture to keep your models explainable and current. If neither question has a confident answer, you may already be carrying more AI debt than you realise.
At DigitalTeddy, we work with boards and leadership teams to uncover and address this new form of technical debt. Whether through an AI governance review or a fractional CPTO engagement, the goal is the same: to ensure that your company’s AI assets remain transparent, sustainable, and aligned with your strategy.