Can AI Make Your Team More Innovative?
Last Updated: September 2026
AI and team innovation is a trade-off, not a straight gain. The tool hands each person more options, faster, and they tend to score well. It also pulls the whole group toward one middle. They arrive in greater number and closer together, so the pool your team picks from grows and narrows at once. Which half of that trade you buy decides whether the work gets bolder or only quicker.
AI Smart Ventures has guided growing businesses through AI adoption long enough to watch this land in real sessions. A team runs its first AI brainstorm, comes out with four times the output, and cannot say why the shortlist feels familiar. The volume is real. So is the sameness under it.
What you risk is not one bad idea. It is a year of safe ones, each fine on its own, none of them beyond a rival with the same tool.
Key Takeaways
- AI widens and narrows at once: it lifts what one person can produce while pulling a whole group toward one shared middle.
- The model is the anchor: everyone asks the same system, so the sameness comes from the tool and not from your team.
- The effect is measured, not felt: a 2026 review of 19 trials found a small but real narrowing effect running through human-AI work.
- Order sets the spread: options written before the tool sees the brief keep their range, and options the tool offers first do not.
- Count distinct concepts, not total ideas: a session that doubles its output and halves its angles has gone backwards.
- Keep the choosing with people: a novelty score given before the build rarely survives the build.
Those point at sequence, not restraint. Nobody has to use less AI. Your team has to settle what it asks the tool to do first, and which part it never hands over.
How does AI make a team more innovative?
By raising the floor and the volume at once. Almost anyone can now produce a workable first option in minutes, including people who never counted themselves as the creative ones. In a field trial by Harang Ju and Sinan Aral, 2,234 people built 11,024 ads, and human-AI pairs turned out 50% more ads per worker than human-only pairs. More output reaches the table, and it comes from more of the team.
The gain lands where you would least expect it. Work by Anil Doshi and Oliver Hauser, published in Science Advances, found stories written with AI ideas scored 8.1% higher for novelty, while writers rated less creative at the start gained 10.7%. That is a real result for capability building, because the tool lifts the bottom of the range further than the top. A quiet analyst turns up with three angles instead of none.
Why do AI-assisted ideas start to look alike?
Because everyone is asking the same anchor. A model answers from patterns it has seen, so two people in two rooms working from one brief get neighboring answers. In Nature Human Behaviour research from Wharton, people asked to invent a toy from a fan and a brick produced ideas where 94% shared overlapping concepts, and nine of them landed on the same name for it.
None of that is one model’s flaw. Emily Wenger and Yoed Kenett tested 102 people against 22 models in PNAS Nexus in March 2026, and the models sat closer to each other than the people did: 0.459 against 0.699 on a standard uses task. Switching vendors changes the accent and leaves the range where it was. If your team draws on one class of tool, the flatness travels with you.
What does the 2026 research on idea diversity show?
A small effect that holds up. A review by Alwin de Rooij and Michael Mose Biskjaer, accepted for the European Conference on Cognitive Ergonomics in September 2026, pooled 19 trials and 61 effect sizes run since 2022. It found a homogenization effect tied to AI use of d = .334, steady across every check they ran. The authors read it as creative range being reshuffled, not lost.
That reading matters. One person’s output improves while the group’s range tightens, which is how a team can feel bolder and ship a narrower set in the same quarter. The effect ran strongest on idea generation under tight briefs, and weakest on open-ended tasks. So the risk is not spread evenly. It gathers just where most teams point AI first: a tight brief, a deadline, and a call for options.
How do you keep your idea set wide with AI?
Change the order, then change what you count. Have people write their own first option before the tool sees the brief, and let AI build on what they wrote. Mengchen Dong and Hiromu Yakura tested both orders in July 2026 and found AI suggestions squeezed the group range, while AI refinement of human ideas kept it. Same tool, same people, opposite result. The skipped step is the one holding the range open.
Then measure the thing you want. Total ideas is a vanity count; distinct concepts is the number that says whether a shortlist is worth arguing about. Split the group so each part works from a new framing of the same problem, then merge. Record how many angles survive to the shortlist and watch that figure over a month. Two sessions of this teach more than a tool review, and they give AI upskilling something solid.
- Write first, prompt second: each person drafts one option of their own before anyone opens a tool.
- Break the shared brief: give each subgroup a new limit so the model is not answering one question ten times.
- Score the gaps: overlap between groups is your safe bet, and the outliers are why you ran the session.
- Log what you drop: a named list of dead ends stops the next round making them again.
AI Training builds that order into a session your team already runs, so AI enablement shows up in the work instead of a slide deck.
Why should the choosing stay with your team?
Because a novelty score given before the build does not hold after it. In a study of 43 experts who each spent more than 100 hours building an idea they were handed, the ideas a model wrote had rated higher for novelty at the start, then fell further than the human ones on every measure once the work was done. AI is strong at offering. It is weak at telling you which offer survives contact.
That splits the job cleanly. Let the tool make the option set as wide as you can get it, then let the people who carry the risk pick from it: the ones who know the client, the limits, and what your team can ship by Friday. Tool-first AI vendors will offer you a ranking feature for this step, and this is the step that needs a person. Human-first AI is mostly a rule about which half you keep.
Frequently Asked Questions
Does AI make people more creative, or just faster?
Both, on different scales. Working alone, people turn out work rated more novel and better written with AI help, and the lift is largest for those who started lower. Across a group, that same help pulls output inward. So one person gets more creative while the pool gets narrower. Which effect you notice depends on whether you read one person’s work or the whole set.
Do you need to hire specialists to run AI ideation?
No. This is an AI literacy problem well before it is a hiring problem. The people who know your customers, your limits, and your last five failed bets are the ones whose first options carry real range, and they work for you now. Pick one session you hold every month, teach the group an order they can repeat, and buy in outside skill only where the work is truly technical.
What does idea homogenization mean?
People working apart producing answers that sit closer than they otherwise would. Researchers measure it with semantic distance, which turns written ideas into coordinates and checks how far apart they land. A 2026 review of 19 trials put the pooled effect at d = .334. No single idea is worse for it. The set just has fewer separate places for a team to stand.
Does switching AI models fix the sameness?
Barely. A March 2026 PNAS Nexus study compared 22 models with 102 people and found the models sat closer to each other than the people did to each other. New vendors shift the tone and the phrasing while the spread stays narrow. Changing tools can be worth doing for other reasons. It is not a range strategy, and the fix sits in how a session runs.
Should everyone start from the same brief?
Not if range is what you are after. One brief plus one model plus ten people gives you ten versions of a single answer. Split the group and hand each part a new framing of the same problem: one bound by the customer, one by the calendar, one by what you would do with none of your current tools. Then merge, and read the gaps.
Can AI pick which idea is worth building?
Not reliably. Ideas a model rates highly before they are built lose more of that rating afterwards than human ideas do, on evidence from 43 experts who built the ideas they were handed. Scoring novelty on paper is a different job from calling what holds up under real limits. Widen the list with the tool, then let the people who own the outcome choose.
How do you measure idea diversity without a data team?
Count distinct concepts by hand. Print the session output, group any ideas built on one core mechanism, and record how many groups you finish with, not how many lines. Track that number across four sessions. If the total climbs while the group count stays flat, AI is adding volume and not range. It is rough, and it moves the same way the academic measures do.
Does AI help quieter people put ideas forward?
Yes, and it is one of the clearest gains. The largest measured lift from AI help goes to people who rated lower on creative skill before they started. Someone who never volunteered a concept in a live session can arrive holding three. That gain only survives if the first option is their own, written before the tool offers one, so the order matters as much as the access.
Is a longer idea list always better?
No. Brainstorming pays off through spread, not length. Forty ideas built on four mechanisms are worth less than twelve built on ten. Volume also costs review time, and a team reading sixty near-identical options will end up picking on wording. Ask for fewer ideas with more distance between them, and judge the session by how hard the final call was to make.
Does this only matter for creative teams?
No. Anything with options behaves this way: service design, hiring plans, how you stage a rollout, which market to enter next. Any call that starts by listing what is possible inherits the same narrowing. Everyday operating work is often more exposed than design work, because its briefs are tighter, and tight briefs are just where the effect measured strongest in the pooled trials.
What should a team do with the ideas it drops?
Keep them, with one line on why each lost. A dropped idea from a wide set is the clearest record you have of what your team weighed and ruled out, and it stops the next session making the same four again. Reading that list before the tool sees a new brief also gives people something of their own to start from, which is the order that guards range.
What does it take to get started widening our idea set?
Start with one session you already hold each month. Have each person write a first option before a tool is opened, use AI to build on and test what they wrote, then count distinct concepts at the end. Scope tracks team size and how many sessions you cover. AI Smart Ventures runs AI training for growing businesses, and you can schedule a consultation to design the order in.
Executive Summary
AI and team innovation is a trade: more options per person, and less distance between them. A 2026 review pooling 19 trials found a real homogenization effect across human-AI work, and separate research shows models sit closer to each other than people do. Novelty scores for one person rise while the group’s range falls. The answer is a split of the work, not less AI. Let the tool widen the option set, have people write their own first idea before it sees the brief, count distinct concepts not total ideas, and keep the choosing with whoever carries the risk.
What Should You Do Next?
Take the next session where your team lists options and change two things. Ask everyone to write one first idea before a tool is opened, then count how many distinct concepts reach the shortlist, not how many lines. Run it twice and compare the counts before you change more.
AI Smart Ventures offers AI Training for founder-led organizations that want AI adoption to widen the work rather than flatten it, with change management built around sessions you already hold. Schedule a consultation to design a wider ideation order for your team.
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About the Author
Nicole A. Donnelly is the Founder of AI Smart Ventures and an AI Adoption Specialist with 20 years of experience as a founder and CEO and over a decade leading AI adoption initiatives. She helps businesses integrate artificial intelligence with clarity and confidence, driving innovation and sustainable growth. Nicole has trained over 20,217 professionals in Applied AI, delivered 624 workshops, and worked with close to 1,000 organizations across diverse industries.
Expertise: AI Transformation, AI Strategy, AI Implementation, AI Adoption, Applied AI, Marketing, Business Operations
Disclaimer: This content is for informational purposes only and does not constitute professional business or technology advice. Results vary based on industry, existing systems and implementation commitment. Contact AI Smart Ventures for a consultation regarding your specific situation.
