AI can help a team see an idea sooner. A proposition that would once have remained a paragraph can become a pack, a demonstration or a scene from someone’s day. That makes possibilities easier to discuss and alternatives easier to explore.
It can also make an unresolved choice look like a finished product. The important question in AI-assisted concept development is which choices the representation has already made on the team’s behalf.
The picture contains a proposition
Imagine an early idea for a refillable personal-care range. It’s an illustration, though a familiar kind of development problem.
An AI-generated image shows a beautiful bottle on a bathroom shelf. The description beside it promises convenient refills and less packaging.
The image makes the idea tangible. It also suggests a material, a size, a place in the home and a level of quality, and the words suggest that refilling is easy. One reviewer might picture inexpensive pouches delivered when needed; another might assume a subscription. Nobody has agreed either arrangement.
A positive response could be encouraging. It would still leave open what attracted the person, whether the appearance, the environmental ambition, the convenience they imagined or the whole combination, and whether the business could deliver that combination at a price they would accept.
None of those choices needs to be completely resolved before research begins. Early research exists partly to help resolve them. They do need to remain visible as choices, rather than becoming facts because a persuasive image includes them.
Use the speed to explore the choices
Faster visualization is particularly useful here. The team can make different versions of the opportunity concrete enough to discuss before investing heavily in one expression.
For the refillable range, a durable object people want to keep might support one proposition. An inexpensive system that asks almost nothing of the user might support another. Seeing both could reveal a strategic choice that a single polished image conceals.
Those alternatives need enough definition to be understood. They don’t need every incidental detail settled. The purpose is to learn which differences matter, including differences the team had treated as secondary.
If production takes less time, the extra capacity could make room for an alternative the team would otherwise leave unexplored. That is a more useful ambition than simply producing more versions. It is also different from generating more simulated reactions: additional output doesn’t by itself provide new evidence about customers.
It is also worth noticing when one option has received much more expressive care than another. A finished demonstration and a thin paragraph are different invitations to imagine. A comparison between them may tell the team less about the underlying opportunities than it appears to.
This problem predates AI. Generative tools make it easier to encounter early, when a plausible expression can arrive before the decisions behind it.
A convincing first version can become an anchor
In a CHI 2024 experiment, 60 people sketched ideas for a chatbot avatar. Those using an AI image generator fixated more on an initial example than those with no support.
The experiment concerned one design task and one tool. It says nothing about how buyers react to polished concepts, or about today’s systems in general. It gives a reason to examine how early representations shape exploration, and it doesn’t show that AI makes ideas worse.
For a commercial team, the practical concern is what becomes harder to question once an idea has an attractive form. A format, occasion or audience can start to feel intrinsic to the concept when it was originally one possibility among several.
AI can help loosen that attachment too, by making another direction easier to imagine. Its contribution depends partly on whether the team uses it to explore a choice or to keep embellishing the first answer.
The right degree of finish depends on what you need to learn
Some questions require realism. A service may be difficult to judge without experiencing its sequence, and a packaging idea may depend on how it appears among alternatives.
Removing the detail could remove the very thing the research needs to understand. The aim is to make clear what the stimulus demonstrates, what it invites someone to imagine and what the team is still considering. Those distinctions help a response support an appropriate next decision.
An encouraging reaction might justify developing the refill experience further. It might support exploring a different price or format. It doesn’t automatically settle the whole proposition.
AI gives innovation teams a valuable way to bring possibilities into view earlier. The next question is whether the evidence makes a particular commitment more sensible. An idea can look ready long before the business knows what it is ready to back.
Sources
- Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q. and Velloso, E., “The Effects of Generative AI on Design Fixation and Divergent Thinking,” Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24) (2024). Peer-reviewed conference paper; a between-participants experiment with 60 people on one visual ideation task, comparing an AI image generator, image search and no support.