The AI Productivity Paradox: Companies Are Producing Waste at Unprecedented Speed

We have made it dramatically easier to produce things. We have done far less to establish whether those things are worth producing.

With AI, a product brief can become a prototype in an afternoon. A meeting can become a summary, a presentation and a list of actions. An idea can become a backlog before anyone has seriously questioned the idea itself.

Each of these can be useful. Each can also make an organisation busier without making it more effective.

That is the AI productivity paradox: a company can become faster at completing tasks while becoming worse at choosing which tasks deserve to be done.

When that happens, AI accelerates waste.

Waste does not have to look like poor work. It can be a beautifully designed feature that nobody needs, a convincing strategy document that changes no decisions, or well-written code that adds complexity without improving the customer’s experience.

The quality of the output can make the waste harder to recognise.

The work was unnecessary before AI made it faster

Imagine a product team asked to improve a reporting experience.

The team uses AI to summarise feedback, generate requirements, explore interface options and accelerate implementation. A process that previously took weeks now takes days. The demonstration looks impressive. Everyone agrees that the team has become more productive.

But the original problem was never properly examined.

Customers did not need more reporting options. They needed to understand why two existing reports showed different figures. The underlying issue was inconsistent definitions across the product.

The new interface adds filters, charts and explanations around a problem that remains unresolved.

The team has delivered faster. Customers still cannot trust the numbers.

What, exactly, improved?

Speed matters when it helps us reach a worthwhile result sooner. It becomes a misleading achievement when the result itself has escaped scrutiny.

For product leaders, this should be familiar territory. We have spent years arguing that shipping features does not automatically create value. AI makes that distinction more consequential because it increases our capacity to ship before we have established what matters.

An organisation with weak priorities now has a powerful way to execute them.

One person’s saved time can become everyone else’s workload

Work rarely ends at the point of creation.

A document needs reading. A proposal needs a decision. A prototype needs evaluation. Code needs review, integration and maintenance. A feature needs support, explanation and, eventually, a decision about whether to keep it.

Imagine a designer generating twenty concepts in the time previously needed to produce three. That could improve exploration. But if the designer passes all twenty to colleagues without evaluating them, the saved effort becomes someone else’s evaluation burden.

The same applies to an AI-generated specification. Producing it quickly says little about how much time other people will spend checking its assumptions, resolving contradictions and correcting its requirements.

The productivity calculation needs to follow the work through the organisation.

If one person saves an hour but creates three hours of avoidable work elsewhere, the company has a loss disguised as a gain.

Yet that loss can be difficult to see. The creator can point to a clear improvement: “This used to take me four hours. Now it takes twenty minutes.” The costs are distributed across colleagues, meetings and future maintenance.

Everyone becomes busier, while the dashboard suggests that everyone is becoming more productive.

Polished output can acquire authority too quickly

An unfinished idea used to look unfinished. Rough notes and incomplete sketches made uncertainty visible.

AI can give an untested idea the appearance of maturity. A speculative direction arrives with a persuasive narrative, a detailed roadmap and a polished prototype. The presentation suggests that substantial thinking has already happened, even when the assumptions remain untouched.

This creates a particular challenge for product and design leaders. Once an idea looks ready to build, questioning its foundations can feel like delaying progress.

A team may find itself discussing the layout of a proposed feature before agreeing that the feature addresses a meaningful problem. Stakeholders start refining the solution. The conversation moves towards delivery. The original assumption quietly becomes an accepted requirement.

A plausible explanation is not customer evidence. A detailed roadmap does not establish demand. A working prototype demonstrates a possibility; its existence alone cannot justify investment.

We need to make the status of the thinking as visible as the quality of the presentation.

What do we know? What are we assuming? What evidence would cause us to change direction or stop?

Without those questions, polish can become an accidental substitute for proof.

Faster experimentation still needs a purpose

There is a reasonable response to all this: cheaper production makes experimentation easier. Surely producing more options helps us learn?

It can.

An experiment that disproves an assumption can be valuable even when the idea never ships. An abandoned prototype can save months of development. Exploring several directions can reveal possibilities that a team would otherwise miss.

The distinction is whether the work resolves uncertainty that matters.

Ten prototypes exploring a clear question can be a sensible investment. Ten prototypes produced because generating them is easy can leave the team with more material and no better understanding.

The useful question is: what decision will this work enable?

A prototype might help us decide whether customers understand a proposed interaction. An analysis might establish which problem deserves investment. A technical experiment might reveal whether a direction is feasible.

In each case, the output serves a purpose beyond its own completion.

If that purpose is unclear, increasing production is unlikely to fix it.

Leadership has to change what it rewards

If a company rewards visible production, people have a reason to use AI to produce more visible work.

More documents. More tickets. More concepts. More releases.

Telling teams to “focus on outcomes” will achieve little if performance conversations continue to revolve around how much they delivered.

Leaders need to change the evidence they ask for.

Alongside time saved and output delivered, ask what happened afterwards. Did customers complete the task more successfully? Did the change remove a recurring problem? Did it reduce the total effort required across the business? Did the experiment settle a question that was blocking a decision?

The answer will not always be immediate or neatly measurable. That does not make production counts an adequate substitute.

We should also give credit to decisions that prevent unnecessary work: removing a duplicate initiative, simplifying a requirement, retiring an unused feature or challenging a request before it becomes a project. These contributions are easy to overlook because there is less to demonstrate. No impressive launch. No new interface. No large delivery announcement.

Their value lies in the cost and complexity the organisation never has to absorb.

For design leaders, this means treating problem framing, evaluation and judgment as essential parts of the craft. A team that generates impressive screens quickly still needs the confidence and authority to question whether another screen is the right intervention.

Otherwise, we risk reducing design to an increasingly efficient production service.

What deserves to exist?

I want AI to help teams explore more, test earlier and spend less time on repetitive work. Those are worthwhile ambitions.

But the promise depends on what we do with the capacity we gain.

We can fill it with more production. We can also use some of it to understand customers better, examine assumptions, simplify existing experiences and make more considered decisions about where to invest. Time saved becomes valuable when we put it to valuable use.

That requires leadership willing to question a convincing proposal, stop a weak initiative and recognise that a smaller backlog can represent progress.

Before celebrating how much faster your company can produce, ask how it decides what deserves to exist—and who carries the cost when that decision is wrong.

Without that discipline, we risk building organisations that are exceptionally efficient at creating work for themselves.