Scope creep in the age of AI: how to set priorities when every feature looks cheap

Prioritisation and requirement inflation (scope creep) are among the biggest challenges in software engineering and product management today. The number and complexity of the features that stakeholders believe a system "should" have grow fast and often unchecked, simply because AI tools have made those features easier to generate and ship.

Scope creep used to be slow: an extra report here, one more field on a form there. With AI it speeds up, because the brakes are off. Every idea used to be priced in weeks of developer time, and that price alone filtered out many requests. Today the first version of a feature takes an hour, and the cost that really matters comes later, where it is harder to see.

Requirement inflation is one effect of a wider shift we describe in our post on project management in the age of AI agents: execution gets shorter, and the weight of a project moves to defining the problem and checking the result.

Why AI fuels requirement inflation

Free prototypes and the illusion of cheap features

An AI assistant can write the code for a form, a report or an integration in minutes. A working prototype is more persuasive than a slide, so it is tempting to conclude that anything quick to build is worth having. Yet writing the code is only a small part of what a feature costs. The rest is paid over years: for testing, fixes, updates, user support and security.

Requirements that write themselves

AI generates specifications as well as code. Ask for "user stories for the billing module" and within a minute you have forty of them, each one sensible and neatly written. A requirements list that used to take a week of workshops now grows in an afternoon. The team's ability to judge which items are really needed does not grow at the same pace.

Everyone has a prototype

Ideas no longer arrive only as tickets. More and more often someone from sales or accounts brings a ready-made prototype they built themselves with AI: "it almost works, we just need to plug it in". It is hard to say no to something that looks finished, even though it still lacks permissions, error handling, data from real systems and tests before it is ready for production.

The pressure to "have AI"

Some requirements come from the technology rather than from a user's problem: "the competition has an assistant, so we should have one too". A feature like that starts with a solution, and the problem gets added later. On top of that, features built on language models carry their own hidden complexity. Their answers are not fully predictable, so they need evaluation and monitoring, and someone has to control what data the model can reach.

The demo effect

An impressive demo built in two days easily turns into a promise to a customer or the board. But a demo shows the best case. Edge cases, performance on real data, permissions and regulatory compliance stay out of the picture, but not out of the project's scope.

The consequences of requirement inflation

The effects do not show up straight away, which is exactly what makes them dangerous. Each feature on its own looks harmless; the problem is the sum.

• Rising maintenance costs. Every feature is code to test, update and secure, documentation to write and user questions someone has to answer. AI-generated code also has to be understood before it can be fixed, and understanding someone else's code, even a machine's, takes time.
• A blurred product. The more options there are, the harder it is to find the few that users came for. Onboarding new people takes longer, and mistakes and support tickets pile up.
• Slower delivery of value. The paradox is that code gets written faster while important changes reach users more slowly. The team's attention is spread across dozens of small features, and each one needs review, testing and integration.
• Technical and quality debt. Features added "while we are at it" rarely have proper tests or an owner. A few months later nobody knows who uses them or whether they can be changed safely.
• A larger attack surface. Every new form, integration or AI assistant is another place the company's data passes through. Someone has to decide who can access it, on whose behalf the system acts and where a record of its actions is kept.
• Costs that grow with use. AI features are often billed per model call. A pilot with ten people looks cheap; the bill for the whole company can look very different.
• Prioritisation turns into theatre. A backlog of several hundred items stops being a tool for making decisions. Everything is "important", so in practice whoever asks loudest sets the order.

How to deal with it: a new approach to prioritisation in the age of AI

Now that the cost of building a feature no longer works as a natural filter, the filter has to be built on purpose. Three tools help: total cost of ownership (TCO), a well-defined MVP and testing the riskiest assumption first (RAT).

TCO: count the cost of owning, not the cost of writing

Total cost of ownership (TCO) is everything a feature costs over its whole life, not just while it is being built. Before anyone says "let's do it", it pays to go through a short list:

1. Building: with AI, often the cheapest item.
2. Checking: code review, testing, verifying what the AI produced.
3. Maintenance: dependency updates, fixes, changes to integrations.
4. Security and compliance: permissions, data protection, audits.
5. Running costs: infrastructure and the cost of model calls at full use.
6. People: documentation, training, user support.
7. Retirement: what it will cost to remove the feature once it is no longer needed.

Popular scoring methods such as RICE or WSJF divide value by effort. With AI the estimated effort to build drops, so suddenly every feature looks worthwhile. Put TCO in place of effort and the ranking reflects reality again. A simple test question: who will be responsible for this feature in two years' time, and what will it cost them?

MVP: start from the problem, not from a feature list

An MVP (minimum viable product) is the smallest version of a product that lets you check whether you are solving a real problem for real users. With AI it is tempting to read "minimum" as "everything we can generate before the deadline". But "minimum" refers to scope, not to effort.

A well-defined MVP starts with a few written answers:

• What problem are we solving, and for whom? One or two sentences, no feature names.
• How will we know it has worked? A measurable outcome, such as cases handled faster or fewer errors, not "module deployed".
• What is out of scope? A list of things we are deliberately not doing in this version. This is often the most important part of the document, because it is what protects the project from inflation.
• Who decides on changes to the scope? Every new requirement goes to one person or team who knows what the MVP is for.

Much of this definition is the work done in pre-implementation analysis. When AI shortens the build, good analysis at the start matters more, because a mistake in scope turns into finished code faster than ever.

RAT: test the riskiest assumption first

The riskiest assumption test (RAT) reverses the order of thinking. Instead of asking "what should we build in the first version?", it asks "which assumption would sink the whole idea if it turned out to be wrong?" and looks for the cheapest way to check it. Often you do not need to build a product at all.

With AI this approach takes on new meaning. A cheap prototype is no longer a commitment; it becomes a tool for an experiment you can throw away. Here is how it might go:

1. List the assumptions. That users have this problem, that they are willing to change how they work, that the data is available and good enough, that the running costs add up.
2. Pick the riskiest one. The one that is both the least certain and the most critical to success.
3. Design the cheapest test. Interviews with five users, a mock-up, a prototype built with AI in a day, or delivering the service by hand "behind the scenes".
4. Set the criterion in advance. Before you see the results, write down which outcome means "go ahead", which means "change direction" and which means "drop it".
5. Make the decision and record it. After the test, the prototype goes in the bin or into a planned, properly built version. Never straight into production.

Everyday rules

TCO, MVP and RAT work best when a few simple team habits back them up:

• Every requirement starts with a problem. A request for "an AI assistant in module X" goes back with a question: what problem should it solve, and how will we measure that it has?
• A complexity budget. Decide how many new features the product can take on in a given period. Once the budget is used up, a new feature only comes in to replace another.
• A prototype is not a promise. Label prototypes as disposable, and do not show them to customers as a preview of the product without a team decision.
• Regular clear-outs. Once a quarter, check which features nobody uses and plan their retirement. A feature that has been removed costs nothing.
• A record of decisions. Write down who set a priority, when and on what basis. Six months later this lets you tell deliberate decisions from the ones that "just happened".

Summary

AI did not invent requirement inflation, but it took the brakes off. When building a feature takes an hour, the question "can we do it?" loses its meaning. What counts is whether it is worth having and maintaining. Teams that count the total cost of ownership, stick to a narrowly defined MVP and test their riskiest assumptions first use AI to learn faster, not to bloat their product faster.

Would you like to get your project's scope under control? Get in touch.