For years, technical debt was one of the most useful concepts in software. It gave technology leaders a simple way to describe a familiar trade-off: a team could build quickly by cutting corners and making compromises but would eventually come back to clean things up. The debt incurred had to be repaid – that was the expectation. At some point, someone had to refactor the code, document the system, upgrade the architecture or fix the shortcuts that made speed possible.
That’s not happening with AI.
AI has changed the nature of risk in software development because generation is so easy. It’s easy to create another app or another agent. It’s easy to change a prompt, add a data source, duplicate a workflow or push something into use because it seems to work. But the much harder work is deciding what should exist, who owns it, how it changes and what happens when it stops being trustworthy or useful. Because, unlike with technical debt, there’s no inherent promise to improve those systems later. Teams may be generating things that they never intend to revisit at all.
Debt comes with an obligation to eventually reign in the overreach. Generated sprawl just spreads.
This is the next frontier for CIOs: the layer of working but ungoverned systems – an array of tools that were useful enough to launch, but not mature enough to own, maintain, secure or even properly retire. And they also might not look like what CIOs are used to seeing. They could begin as a prompt, a low-code workflow, an automation or an agent configured for a narrow task. Yet, if people depend on them to run the business, they still may become part of the enterprise, whether IT planned it or not.
And the weirdest part about it all is that failure could actually look like success. Apps that solve local problems could get copied by another team, modified, connected elsewhere and become relied on before anyone asks whether they should be governed as part of the enterprise portfolio. That is, dozens of successes that were never designed to scale is still a risk to your operations.
Shadow IT redux
It’s a bit like Shadow IT, which emerged because teams needed tools faster than centralized IT teams could deliver them. Low-code and no-code platforms expanded who could build applications, and in many cases – like now – this was exactly the right direction. And shadow IT was eventually controlled by lifecycle discipline around what had been built – things like planning, version control, testing, rollback, ownership and governance. It’s why lifecycle management became so important.
AI is showing us we need a similar moment to happen again. If things were weedy before, generative AI is like putting nitrogen in the soil and giving things sunlight – it’s making those weeds grow a lot faster. It’s pushing enterprises toward it even faster and with less visibility. We’re past the first phase of enterprise AI, where the question was whether the technology could do useful work. It’s shown that it can. The next question is harder to answer: can an organization govern the work once it spreads?
Today, generative AI, no-code, low-code and vibe-coding tools give you immense ability to create successful demos. But a successful demo is trivial at this point, and it’s not the same thing as an enterprise-grade system. A working agent is not the same thing as a managed application. And a workflow that saves someone an hour today may create risk tomorrow if nobody knows who owns it, what data it touches, what permissions it has or how changes are tested before they go live.
This is where the technical debt comparison starts to break down. With technical debt, there is usually an asset everyone recognizes: code, architecture, infrastructure, documentation. It may be messy, but it’s there. With AI-enabled systems, the “asset” may be a mix of all kinds of things – prompts, instructions, context, connectors, data sources, permissions, model behavior, human assumptions and so on. It’s a long list. And some of those may live in places that were never designed to be treated as software. Some of it might get changed informally. Some of it might never be documented at all.
A shift in focus
Does this mean organizations need to slow their AI adoption? Not necessarily. But it does mean that they need to stop confusing ease of creation with production readiness. It also means they need to focus on ways to generate applications, automations and agents that are governable from the start.
How does that happen?
Professional developers have always had to-dos in the code base to identify technical debt to go back and fix. That’s still happening with AI, but for non-developers using AI tools, there are no to-dos. You just keep going. So, what’s required is a shift in focus away from speed to control – less about how fast something can be made and more about whether anyone will understand how it works in a year and who will have access to it. It will mean asking important governance questions like “can we test changes before they affect operations?” or “can the decisions in this system be explained?” or “who has data access?” and so on. These are key operational questions that need answering before an AI-built workflow shifts from being an experiment to part of your infrastructure.
Done correctly, governance allows organizations to move forward with confidence. Teams should be able to build and experiment with solutions, and AI should be allowed to make people more capable. But the enterprise needs a framework that helps distinguish between what’s useful, what’s ready for production and what’s safe to scale.
AI-generated development needs versioning, approval gates, testing and staging, and visibility into dependencies and permissions. And it needs a way to rollback changes and ownership and review cycles. Maybe most importantly, at some point it needs to be retired. Just like any other software, AI-generated apps that aren’t useful anymore, or are too risky, can’t be allowed to linger.
Ambition with oversight
Democratized development with AI is not dangerous by definition, but without lifecycle discipline, the risks it carries increase. The goal is to give teams the room to create and to make these systems durable enough for the enterprise – to ensure that they’re secure, monitored, improved and finally retired.
Enterprise AI still needs ambition – the will and vision to build and improve – but it also needs a lifecycle that makes every new system accountable. The organizations that will make AI work in the long run will be the ones that can build quickly and make what they build visible, governable and worth keeping.
