AI’s bigger opportunity is redesigning work

Efficiency is only the first step

Adding a chatbot or automating a few steps can save time. But those changes usually leave the underlying process intact. The more consequential question is whether AI lets a company rethink the work itself.

That distinction matters. AI can cut manual effort, surface information faster and help people complete tasks more efficiently. Those are useful gains. They don’t automatically make a product AI-native: if people still follow the same sequence and the software rests on the same assumptions, AI is speeding up the old workflow.

Efficiency is easy to describe because it can be attached to a familiar task: a person spends less time gathering information, drafting text or moving between steps. Redesign is harder to explain. It asks whether those steps need to happen in that order, whether the same person needs to do them, and whether the handoffs that once made sense still serve a purpose. A faster process may be valuable, but it can preserve unnecessary work along with the useful parts.

Here’s the thing: adding AI to an existing product can improve a specific interaction while leaving the broader experience unchanged. A feature may answer a question more quickly, for example, without changing what the user must do next. That can still be worthwhile. It simply doesn’t settle the larger question of whether the product has been rebuilt around new capabilities.

Start with the work, not the feature

The larger opportunity comes from starting over. Which parts of a job need human judgment, context or trust? Which involve repetitive research, data gathering or drafting? Give AI the work it handles well, and keep people involved where their decisions and relationships matter. That’s a more demanding way to build products, but it can change what the work looks like, not just how quickly it gets done.

This way of thinking begins with the task and the people responsible for it, rather than with a model or a chatbot looking for a place to fit. Similar systems typically work best when the problem is clearly defined: what information has to be assembled, what decision follows, and where errors or uncertainty call for a person to step in. Those questions don’t guarantee a better design, but they make it harder to mistake a new interface for a new way of working.

Some activities are repetitive without being trivial. Research, data gathering and drafting may demand care, especially when the result will shape a decision or be shared with someone else. An AI system can help with parts of that effort, but the product still has to make clear what the person is expected to review, supply or approve. Otherwise, time saved on one step can return as extra checking elsewhere.

And this is the part most people overlook: changing who does a task can also change who has to catch mistakes. If AI takes on preparation or drafting, the people who remain in the process may need enough context to judge its work. That means the design question isn’t only what can be automated; it’s how responsibility and useful information move through the work after automation.

Human judgment remains part of the design

The argument for redesign isn’t that people should disappear from a process. The source makes a more limited distinction: people matter where judgment, context or trust matter, while AI may be suited to repetitive research, data gathering or drafting. That division will vary with the job. It has to be decided in the context of the work, not assumed from the presence of an AI feature.

For a product team, that makes the boundary between machine assistance and human decision-making a core design choice. If a person needs to understand the basis for a result, the product has to support that understanding. If trust depends on a relationship, automating a communication step may not preserve the value of the relationship. There’s no universal sequence that resolves those trade-offs, and a product label can’t do that work on its own.

This is more significant than it looks. A tool that saves time on a task may create a measurable convenience, while a redesigned process can alter where effort sits and what the user is able to accomplish. But the larger claim also calls for more scrutiny: changing a workflow can introduce new dependencies, review burdens or points of failure. Calling a product AI-powered doesn’t tell a reader which of those changes have occurred.

What the company example does—and doesn’t—show

Malte Kramer, founder and CEO of Luxury Presence, describes the company as an AI-powered marketing platform. His perspective comes from building in that space, though this fragment offers no detail on how the platform works or what results it delivers.

That limitation matters for readers trying to assess the claim. The description identifies a company and its stated category, but it doesn’t explain which marketing tasks the platform handles, how people use it, or whether the work has been redesigned. Those details would be needed to judge the product against the distinction between adding efficiency and changing a workflow. Without them, Kramer’s role supplies context for the viewpoint, not evidence of outcomes.

If you’re working in this space, the useful question is concrete: what does the user’s work look like before and after the product is introduced? A clear answer should describe the task, the human decisions that remain, and the steps AI takes on. That kind of account is more informative than a broad claim about being AI-powered, because it shows where the claimed change actually happens.

The test is what changes for the user

For companies building AI products, the standard implied here is demanding. They have to understand the existing process well enough to see which parts are worth preserving, then design around the capabilities AI can provide. They also have to account for the work people still do, including review and relationship-building. A product that only shortens one step may deliver a benefit, but it hasn’t necessarily answered the redesign question.

For customers, the practical distinction is whether the product changes the shape of the task or merely makes one part quicker. That can be difficult to judge from a short description. The strongest explanation would connect the tool to a specific kind of work and show how responsibilities shift between the system and the people using it, without treating automation as proof of better results.

There’s a further test: does the new process make sense to the person expected to use it? A redesign that looks elegant from a product team’s perspective can still impose unfamiliar steps or leave users unsure when to intervene. The source doesn’t provide evidence about particular products or user outcomes, so no claim about success follows from its argument alone. The principle is clear; the proof depends on how a given product works in practice.

Speed matters. But speed by itself is a narrow measure of what AI might change. The more useful question is whether the work has been reconsidered around what systems can do and what people must still contribute. That’s where the promise—and the harder test—lies.

You can read more from [Kramer at Entrepreneur](https://www.entrepreneur.com/author/malte-kramer2), connect with him on [LinkedIn](https://www.linkedin.com/in/malte-kramer/), or visit [Luxury Presence](https://www.luxurypresence.com).