A customer can want the benefit of an AI feature and still hesitate at the moment it acts for them. There is a meaningful difference between recommending something, preparing it for approval and deciding without asking.
For product teams, that difference can be central to the proposition. Research into an AI-enabled product needs to understand what people want to achieve and which decisions they are willing to hand over along the way.
Consider an illustrative household service that helps with food shopping. It suggests meals, notices ingredients likely to run out and prepares a shopping basket. That could remove a tedious task. If it can adapt to changing plans and preferences, it could offer something more useful than a fixed reminder or a standard weekly order.
Now let it place the order automatically. The intended benefit is still convenience, but the experience has changed. It is spending money and committing the household to particular choices. A person who welcomed the prepared basket might want to approve the substitutions. Another might consider approval an irritating extra step that defeats the point.
The question is more precise than whether either person likes AI.
What is the customer actually handing over?
A product concept can describe an outcome attractively while leaving the degree of autonomy vague. “Takes care of your weekly shop” could mean several different things to someone reading it.
That ambiguity matters when interpreting an enthusiastic response. People may be endorsing different versions of the experience. Some imagine assistance they can direct; others imagine a task disappearing entirely. The development team needs to know which promise it has made.
The consequences of getting something wrong also vary. An unsuitable meal suggestion can be ignored. An unwanted delivery takes more effort to undo. The same person could be comfortable delegating familiar purchases and prefer to decide when hosting guests.
These are possibilities to investigate in the relevant category. They aren’t universal rules about consumer behavior, and they show why a general question about trust may miss the conditions that make the product valuable or unwelcome.
“AI” doesn’t settle the value proposition
In experiments led by Washington State University researchers, US adults shown product descriptions that mentioned artificial intelligence reported lower purchase intentions than those shown the same descriptions without it.
The experiments, published in 2024, measured stated intentions toward described products. They don’t establish how people feel today, in every market, or what happens to sales. They do challenge the assumption that adding the AI label will, by itself, increase appeal.
That is no reason to conceal how a product works. People need clear information about relevant functions and implications. They also need to understand why the experience would be better for them.
In the shopping example, the benefit might be less planning, fewer forgotten ingredients or an easier way to accommodate a change of plans. Those are different promises. A product that is excellent at one may disappoint someone who bought it for another.
The research question therefore reaches beyond the attractiveness of a feature description. It includes the circumstances in which the benefit matters, the alternatives people already use and the work the new service still leaves them to do.
More control isn’t automatically a better experience
It would be easy to conclude that every AI action should require confirmation. That could make an otherwise useful service exhausting.
A person buying relief from a recurring task may have little appetite for supervising each step. Equally, automatic action can be an unwelcome interpretation of a promise to help. The value may lie in preparation rather than completion, or in handling routine situations while making an exception easy to recognize.
Those boundaries may change with experience. A reassuring first demonstration doesn’t establish how someone will feel after an unexpected result, a changed routine or repeated requests for approval. Product research needs to leave room for that learning, rather than treating a first reaction as a settled relationship with the service.
AI can create valuable possibilities for products that adapt and respond to context. Understanding the customer’s desired role helps make those possibilities concrete. The proposition becomes clearer when the team can explain both what the product will take care of and what the customer still wants to decide.
Sources
- Washington State University, “Using the term ‘artificial intelligence’ in product descriptions reduces purchase intentions,” WSU Insider (July 30, 2024). The university’s account of experimental surveys with more than 1,000 US adults, led by Mesut Cicek and published in the Journal of Hospitality Marketing & Management; the measure is stated purchase intention.