Every product person knows what technical debt is. We've been living with it, talking about it, and trying to pay it down for twenty years. It's the work you didn't do - the test coverage you skipped, the refactor you deferred, the duct tape you told yourself was temporary - that quietly compounds until it's the reason a release goes wrong.
There's a new kind of debt accumulating in product organisations right now, and it doesn't have a widely-used name yet.
I've started calling it lifecycle debt.
Lifecycle debt is the governance work you didn't do.
It's the AI feature that shipped without a clear owner. It's the model you deployed without defining how you would know if it started behaving differently. It's the third-party API you integrated without documenting what data it sees. It's the approval gate you skipped because the release was urgent and you would "do it properly next time."
It's the customer-facing AI that's been in production for six months and has never been formally reviewed.
None of those decisions felt risky individually. Each one was a small, justified shortcut at the time.
But they accumulate, and they compound, and at some point - usually when a regulator asks a question, or a customer raises an incident, or your CEO is asked something on a podcast - the debt becomes visible.
By then, paying it down is expensive.
Why this is happening more now
Lifecycle debt isn't new. It's existed for as long as product organisations have existed.
What's new is the rate at which it's accumulating.
When build cycles ran in quarters, governance work could happen between releases. There was time to define ownership, to build review processes, to document what was shipping and why. The cadence of the work matched the cadence of the governance.
When build cycles compress to days or hours - which is where AI-accelerated development has taken many teams - the governance cadence doesn't compress with it. You can't run a quarterly review process on something that ships hourly. So the governance either doesn't happen, or it happens later, or it happens in a thin policy-document way that doesn't actually govern anything.
Each of those produces lifecycle debt.
What lifecycle debt actually costs you
I want to be specific about this, because abstract "risk" never moves anyone.
The first cost is invisibility. You can't manage what you can't see. Lifecycle debt is, by definition, the AI surface area you don't have visibility over. When something goes wrong, you find out about it from the outside - a customer complaint, a regulator query, a press story - instead of from your own systems.
The second cost is response time. When you do find out, you don't know what to do, because you don't have the documentation, the ownership map, or the rollback procedure that you'd have for a properly-governed feature. The first 48 hours of an incident are spent figuring out what you have, not fixing it.
The third cost is trust. Lifecycle debt erodes the relationships that hold a product organisation together - the trust the board has in the CPO, the trust the CPO has in their engineering counterpart, the trust customers have in the brand. Once those crack, you spend years rebuilding them.
The fourth cost is regulatory. Increasingly, "we didn't realise" is not a defence. Directors' duties under the Corporations Act don't distinguish between known and unknown risk in the way they used to. If you should have known, the law treats it as if you did.
Paying lifecycle debt down
The good news is that lifecycle debt behaves like technical debt in one important way: you can pay it down deliberately, and the work compounds.
The first move is visibility. Build a register of every AI feature, model, and integration in your products. Don't worry about governance maturity yet — just see what you have. You can't pay down debt you can't see.
The second move is ownership. Every item on the register needs a named human accountable for it. Not a team, not a function - a person. The exercise of doing this surfaces the gaps faster than any audit will.
The third move is cadence. Governance has to run at the same speed as the build. If you're shipping AI weekly, your governance review can't be quarterly. The cadence question is the hardest one in this whole space, and it's the one I've spent the most time on.
I've been working on a playbook that goes through what good cadence looks like in practice. It comes out shortly. If lifecycle debt is something you're recognising in your own organisation, it'll be useful.
A note
I've spent the last few months building a practical 7-step playbook for CPOs on exactly this. It covers how to establish portfolio visibility, assign full lifecycle cost, build regulatory alignment in from inception, audit your shadow portfolio, implement continuous health monitoring, build a decommission practice, and govern for portfolio coherence - not just individual It's coming out this week.
In the meantime, if any of the above resonates uncomfortably - that's the point. The role has changed. The work is to update the muscle to match.
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