Yes, AI can generate ideas, and a 2024 innovation study found that AI-generated concepts were seven times more likely to rank among the top 10% of ideas by quality. The same evidence shows a cost: AI ideas can be less novel and more similar to one another, so the key challenge is using AI without collapsing a team into predictable thinking.
The popular advice is to open a chatbot, type “give me creative ideas,” and keep generating until something feels promising. That approach produces volume, but volume alone doesn't create a strong campaign, product direction, or positioning strategy. In real brainstorms, the difficult work starts after the first list appears. Someone has to widen the search space, spot the familiar patterns, connect an idea to a real audience, and decide whether the concept can survive contact with the market.
An “idea” here means a concept, hook, angle, name, theme, or direction a team could develop further. AI can produce these candidates quickly. It can also help people escape a blank page, compare routes, and mutate an early thought. But it doesn't replace judgment, lived context, taste, or responsibility for the final choice.
The useful question isn't can AI generate ideas. It's when should AI enter the session, how much should it contribute, and what sequence protects originality? The answer becomes clearer through three lenses: what the research measures, how prompts shape the output, and how timing changes the team's thinking.
For a broader discussion of how people and AI can work together creatively, see this guide to how AI can help us be more creative. The practical lesson is simple: use AI to expand the candidate set, then make humans responsible for meaning, selection, and direction.
Table of Contents
- The Straight Answer to Whether AI Can Generate Ideas
- How AI Actually Produces an Idea in the First Place
- What the Research Says About AI Idea Quality
- The Hidden Tradeoff Between Quantity and Originality
- Prompt Engineering Principles for Better AI Ideas
- Why Timing Changes the Kind of Ideas AI Produces
- A Structured Brainstorming Workflow With Bulby
- Practical Checklist for Original AI-Assisted Ideation
The Straight Answer to Whether AI Can Generate Ideas
Yes, AI generates ideas. It can produce campaign territories, product features, naming directions, content themes, and alternative messages from a short brief. The 2024 SSRN study on AI-augmented brainstorming found that hybrid human-AI groups outperformed both interactive human-only groups and nominal groups in brainstorming productivity and creativity. The same study concluded that AI alone was much more efficient at generating creative ideas, which helps explain why teams can move through early exploration faster with a model in the room. The SSRN study supports a useful division of labor: AI creates a larger pool, while people evaluate and refine it.
That answer still needs a qualification. A 2024 Science Advances study found that generative AI increased the average novelty and usefulness of ideas compared with unaided human work, but also reduced diversity and increased similarity across participants. A separate 2024 innovation study found that AI concepts were seven times more likely to fall within the top 10% of ideas by quality, while also receiving lower novelty ratings and showing greater pairwise similarity. The Science Advances research and the innovation study point to the same tension: a better average idea isn't the same as a broader set of possibilities.
What AI is good at
AI helps most when the team needs range, speed, and variation. It can reframe a brief for different audiences, generate alternatives from a rough seed, list assumptions behind a proposal, or combine two directions that a busy team might overlook. These are valuable moves in the middle of a brainstorm, especially when people have already supplied context.
AI is weaker as the sole source of direction. A blank prompt often returns familiar category language because the model is drawing on patterns that commonly appear together. The output may sound polished before anyone has checked whether it has a distinctive point of view.
Practical rule: Ask AI to expand human thinking, not to decide what the team should think about.
The rest of the process should therefore protect three things: productivity, so the team can explore enough options; quality, so weak ideas don't consume attention; and diversity, so the strongest direction isn't merely the most familiar one.
How AI Actually Produces an Idea in the First Place
An AI model doesn't begin with a private flash of inspiration. It processes language by identifying patterns in the material used to train it, then predicts what token, or small piece of text, is likely to come next. Modern large language models rely on the Transformer architecture, introduced in 2017, which became a foundation for current language systems. The history of generative AI also includes Alan Turing's proposed Turing Test in 1950, ELIZA in 1966, Generative Adversarial Networks in 2014, and ChatGPT's mainstream launch in 2022. This overview of generative AI history places today's ideation tools within a much longer sequence of technical developments.
Suppose you ask for a product concept for people who struggle to cook after work. The model doesn't experience fatigue, hunger, or the frustration of cleaning a pan. It identifies relationships among words and ideas associated with convenience, recipes, routines, delivery, preparation, and time. It then assembles a plausible continuation based on those learned relationships.
The recipe analogy
A model trained on thousands of recipes can suggest an unusual ingredient combination because it has learned which ingredients and techniques often appear together. It hasn't tasted the dish, formed a personal preference, or tested the recipe in a kitchen. Its strength is pattern-based recombination, not situated experience.
Fine-tuning and reinforcement learning from human feedback help steer the model toward answers people tend to find useful, clear, safe, or well-structured. Settings such as temperature and top-p influence how narrowly or broadly the system samples from likely continuations. Lower exploration tends to produce more predictable responses, while higher exploration can produce stranger combinations that still require human review.
This distinction matters because “creative” output can look intentional even when the underlying process is probabilistic. An idea may feel fresh because the model has combined familiar elements in a useful order, not because it has developed a human-like desire to make something original.
For a plain-language explanation of how generated content works and how to assess it, this AI Video Detector overview offers useful background. Teams using AI for brainstorming should treat the model as a fast associative engine. AI-assisted brainstorming becomes more productive when people supply the situation, the constraints, and the judgment that the model doesn't possess.
What the Research Says About AI Idea Quality
Research on AI-assisted ideation rewards a more careful question than “Which side is better?” The answer changes with the unit of analysis. An individual concept may score well for usefulness or novelty, while the complete set may cover fewer distinct directions.
The research on brainstorming methods points to a practical distinction: AI can strengthen the quality of selected candidates without guaranteeing a broad search of the problem. That makes evaluation design part of the creative process, not a reporting detail.
What the studies actually measure
The 2024 studies used different comparisons, tasks, and definitions of quality. Some assessed productivity, novelty, or usefulness at the idea level. Others examined how similar the ideas were across a group. Those methods answer different questions, so their findings should not be collapsed into one verdict.
A concept that reaches a high quality rating may be a strong candidate for development. It may still resemble other outputs produced by the same model or prompt. Teams therefore need to separate candidate strength from search breadth.
| Evaluation question | What a strong result means | Practical implication |
|---|---|---|
| Can the system produce useful candidates? | Some outputs meet a high quality threshold | Shortlist them for human review |
| Can a team explore distinct directions? | Ideas occupy meaningfully different parts of the problem space | Change prompts, roles, or starting assumptions |
| Does AI improve group work? | The workflow may produce more usable material | Keep human framing and selection in the loop |
| Are highly rated ideas original? | Quality scores alone cannot establish that | Check similarity, precedent, and context |
Methodology also affects what teams should conclude. A controlled brainstorming task may isolate the effect of AI, but a real workshop includes domain knowledge, disagreement, incomplete information, and changing constraints. A model can perform well on a defined prompt while offering little help with the unstated problem behind it.
That is why the research discussion summarized by the Wharton Mack Institute is useful as a corrective. It discourages treating fluency or a high-scoring example as proof that AI has independently produced an original direction.
For teams, the sequence matters. Use AI to widen or reshape a human-framed problem, then test the outputs against customer reality, strategic constraints, and ideas already common in the category. A guide for AI visibility tools makes a related point about evaluating generated material in context: surface quality is only one part of the assessment.
Measure both quality and difference. Quality identifies candidates worth developing. Difference shows whether the team has explored enough routes to avoid settling for the first plausible pattern.
The Hidden Tradeoff Between Quantity and Originality
A longer idea list can leave a team with fewer real choices. AI may produce dozens of answers whose wording changes while the underlying concept stays nearly fixed. A campaign team asking for tagline options might receive different lines built on the same rhythm, emotional promise, or rhyme. A product team requesting features may see the same familiar patterns repeated, such as personalization, automation, dashboards, or community functions.
The practical measure is not output volume. It is the number of clearly different directions your team can evaluate.
How teams prevent convergence
Start by separating exploration from selection. During exploration, ask for routes that differ in audience tension, business assumption, user behavior, or category reference. During selection, compare those routes against the brief and remove weak options. Asking AI to polish too early makes related ideas look more finished without making them more distinct.
Change the input, not only the wording. A team can give the model separate jobs:
- Expand the frame: identify overlooked users, constraints, or moments in the customer journey.
- Cross-pollinate: borrow a principle from an unrelated business or activity.
- Challenge the default: state which category convention the idea should avoid.
- Stress-test the route: list the assumption that must be true for the concept to work.
This creates more separation between outputs than repeatedly asking for “fresh ideas.” It also gives people a reason to disagree with the model instead of treating its first polished list as the working brief.
Human-only time has a role as well. Before showing AI-generated options, ask each participant to create a few directions from the problem as they understand it. Then use the model to extend, combine, or challenge those starting points. A later review can label each output as familiar, adjacent, or new to the team, based on comparison with existing category work and internal ideas.
Timing determines how much influence AI has over the group's point of view. Early use can help expose routes the team missed, while constant use can make every route resemble the model's preferred patterns. Treat AI as a change of angle, not the room's only source of thought.
A useful sequence is human framing, varied AI exploration, human comparison, then targeted AI development. That order preserves room for original judgment while still using the model where speed and range are valuable.
Prompt Engineering Principles for Better AI Ideas
A strong brainstorming prompt doesn't ask the model to “be creative.” It changes the conditions around the problem so the model has to search through different associations. Prompt diversity matters because repeated requests create repeated patterns.
Six ways to widen the search
Constrain the domain, not the imagination.
Give the model a specific situation, audience, and category, then leave room for the answer to move.
Prompt: “Develop campaign concepts for a refillable household-cleaning brand aimed at renters in small apartments. Avoid discount-led ideas and focus on moments of frustration during storage, use, and disposal.”Add a contrarian frame.
Ask for an approach that a conventional competitor would reject. This creates productive tension instead of another category-safe answer.
Prompt: “Propose positioning angles for a project-management product that a traditional enterprise software competitor would consider too provocative. Explain what assumption each angle challenges.”Name the audience's friction point.
A real barrier creates sharper ideas than a broad demographic label.
Prompt: “Generate feature concepts for new parents who want to exercise but abandon plans when preparation takes longer than the activity itself. Avoid generic reminders and tracking features.”Borrow from an adjacent industry.
Cross-category analogies can introduce structures the model wouldn't reach through a standard brief.
Prompt: “Create onboarding ideas for a financial app by borrowing mechanics from museums, specialty coffee shops, and language-learning games. Keep the ideas appropriate for users who distrust financial jargon.”Request alternatives with reasoning.
Don't ask for one “best” answer too early. Ask the model to compare routes and state the tradeoff behind each.
Prompt: “Give five naming territories for a privacy-focused browser. Rank them by clarity, distinctiveness, and emotional appeal. Explain the risk behind each ranking.”Seed a weak human idea and mutate it sideways.
A deliberately ordinary starting point gives the model material to transform without allowing it to define the entire problem from scratch.
Prompt: “Our weak starting idea is ‘a weekly email with productivity tips.’ Mutate it into campaign concepts that change the format, audience relationship, and delivery moment. Keep only directions that could support a distinctive brand voice.”
For teams working on content, product messaging, or creative systems, a guide for AI visibility tools can help frame how models interpret and surface information. That concern connects to brainstorming too. If you want a model to move beyond the most visible patterns, you need to provide unusual inputs, not just ask for a louder version of the same output.
Use ChatGPT for brainstorming as a structured collaborator. Give each prompt a different job: one should challenge the category, another should change the audience, another should import an outside analogy, and another should attack the weaknesses of the current direction. Then have people decide which branches deserve attention.
Why Timing Changes the Kind of Ideas AI Produces
The moment AI enters a brainstorm changes what people notice and build on. An IJCAI 2025 paper found that early AI prompts increased overall creativity but also produced stronger anchoring. Later AI prompts caused a mid-session pivot that supported more divergent thinking while still improving final outcomes compared with brainstorming without AI. The IJCAI 2025 paper treats AI timing as a design choice, not a minor facilitation detail.
AI first
An agency might begin a campaign session by asking AI for territories, audience tensions, and taglines. The team gets polished material quickly, which can help people understand the brief and overcome a blank page. The risk is that the first framing becomes the mental boundary for the rest of the session. People may improve the model's language instead of questioning its premise.
Humans first, AI second
A product team can start with its own rough concepts, objections, customer observations, and category frustrations. Once those human anchors exist, AI can expand each branch, combine distant ideas, or generate alternatives that preserve the team's specific context. This sequence gives the model something situated to work from and gives people a reference point for spotting generic output.
AI as evaluator
A strategy team can reserve AI for the end, asking it to compare positioning routes against stated criteria, identify unsupported assumptions, or draft questions for customer research. This use may improve selection discipline, but it won't create much additional breadth. The model is examining the team's existing space rather than opening a new one.
Sequence matters: Use AI early to unblock, later to pivot, or at the end to pressure-test. Each choice creates a different kind of session.
The right timing depends on the task. A naming exercise may benefit from human-generated associations before AI supplies linguistic variations. A stalled campaign team may need a small AI interruption to break a repetitive loop. A mature product concept may need AI only as a critic. The facilitator should decide the role before opening the tool.
A Structured Brainstorming Workflow With Bulby
A practical workflow keeps human judgment at both ends of the process and gives AI a defined role in the middle. Bulby can serve as the working space for guided exercises, idea sparks, collection, and summarization, while the team retains responsibility for interpreting and selecting the results.
Phase one begins without AI
Start with a human-only divergent warm-up. Give participants a short period to write audience tensions, surprising observations, rough concepts, and deliberately imperfect ideas before seeing model-generated suggestions. The point isn't to create finished answers. It's to establish a reference space that prevents the first AI output from becoming the default frame.
A facilitator can ask each person to produce ideas privately, then collect them without immediate debate. This reduces social pressure and gives quieter contributors a place in the session. Techniques such as virtual brainstorming exercises can help teams create that separation when participants aren't in the same room.
Phase two expands the human seeds
Feed the collected ideas into structured AI exercises. Ask for parallel branches, not a single polished solution. One branch might exaggerate the emotional benefit, another might change the use occasion, and a third might import a mechanic from an adjacent category. Bulby can guide these prompts and keep the outputs connected to the original challenge rather than producing a detached list from a blank request.
Phase three restores human judgment
Have the team cluster the results into thematic lanes. Name each lane in plain language, then mark where ideas are duplicates, where they depend on the same assumption, and where a different route appears. A novelty score can be qualitative, such as low, medium, or high, provided the team agrees on what each label means.
Phase four uses targeted mutation
Bring AI back for specific jobs. Ask it to mutate a weak cluster sideways, cross-pollinate two distant lanes, or stress-test a promising assumption. Avoid asking it to regenerate the entire brainstorm. Focused requests make it easier to see which input created the useful change.
For narrative and concept work, Dunia's guide to brainstorming story ideas offers a helpful reminder that raw prompts become stronger when teams develop characters, tensions, and consequences rather than collecting isolated premises.
Phase five ends with people choosing
The team should select the winners, record why they survived, and identify what needs testing. AI can turn the decision into a brief, summarize the rationale, or draft follow-up questions. It shouldn't own the final choice. The workflow works because AI connects human contributions instead of replacing the human-led divergent and convergent stages.
Practical Checklist for Original AI-Assisted Ideation
Use this checklist before and during the next session. Each item is a guardrail against predictable convergence.
- Write the human brief first: Define the audience, challenge, tension, constraints, and known assumptions before opening an AI tool.
- Seed the session with internal anchors: Bring three to five specific observations, customer phrases, product truths, or rough ideas so the model works from your context rather than a blank category prompt.
- Rotate prompt angles: Move between contrarian, adjacent-industry, anti-trend, beginner-mind, and constraint-led requests instead of repeating the same instruction.
- Limit each AI burst: Cap the number of suggestions per round, then return to a human-only round. A pause helps people form independent reactions before the next model output.
- Score novelty, not just volume: Mark whether each idea opens a distinct route, not merely whether it sounds polished or useful.
- Keep the human trace: Ask shortlisted ideas to identify the human observation or prompt fragment that shaped them. This preserves context and makes the team more accountable for the result.
- Debrief the inputs: Record which prompts produced specific, differentiated thinking and which created smooth but interchangeable filler.
- Assign selection to a person: A strategist, product lead, creative director, or agreed team owner should make the final call. The model can compare and summarize, but it shouldn't decide what the brand or product stands for.
The strongest operating principle is restraint. Use AI when the team needs expansion, recombination, or a fresh angle. Turn it down when people need to establish a point of view, notice a contradiction, or make a consequential choice. That balance captures speed without surrendering originality.
Bulby gives marketing, product, and creative teams structured brainstorming exercises, AI-generated idea sparks, collaborative collection, and summaries that turn scattered input into focused next steps. Visit Bulby to run your next session with AI supporting the middle of the process while your team keeps control of the direction and final decision.