"Others / no answer" includes Manufacturing (35), Media / creative / agencies (2), and respondents who skipped the question (24). Total = 100%.
"Quality" and "AI" are the twin triggers — buyers come in with a business problem and an executive mandate at the same time.
LLM search has overtaken Google as the #1 first move. 76% of buyers start in an AI or search engine.
Buyers want to see the product work first. The demo is the #1 proof moment — more powerful than peer results or case studies alone.
LLM search and LinkedIn now outrank G2 and Capterra as review channels. Presence in AI-generated comparisons matters more than star ratings alone.
Comparison pages are #1 — 11 points ahead of technical docs. Buyers arrive mid-shortlist wanting direct contrast, not isolated features.
The #1 pain is trust in accuracy, not speed. Buyers fear their products will be misrepresented — making trust and quality control the primary buying criteria.
All quotes are reproduced exactly as submitted — unedited. Theme groupings are based on word patterns across the full response set, not editorial interpretation. Themes overlap; counts reflect responses where the theme appears, not mutually exclusive categories.
303 substantive responses · selected verbatim below
Themes overlap — individual responses often contain more than one trigger type. Counts reflect responses where the theme appears, not mutually exclusive categories.
We were adding thousands of new products for a big seasonal push and our internal editing queue just kept backing up so I called up a few people I know at other companies to see what they were doing and then had my team start pulling demos
The trigger was slow turnaround times and inconsistent image quality across listings. The first steps we took were to identify pain points in our current workflows, gather input from marketing and product teams, and compile a list of potential vendors through peer recommendations and online research.
The release of Gemini ai tools in the media sparked a widespread growth of ai generated imagery. We started to compare other vendors to create more unique content.
We had been using a free software and a small team, which allowed us a lot of control over theming, but as the company scaled we were unable to keep up with demand at pace, which got us looking at competitors and what they were using.
New campaign demanded faster, consistent visuals; we researched tools on Google, compared vendors, reviewed demos, and tested outputs with sample workflows.
Our workflow was bottlenecked by slow rendering and a lack of real-time collaboration tools for remote designers.We first conducted a needs assessment to list "must-have" features like cloud syncing and AI-driven editing.Then, we identified three top platforms and launched a one-week pilot with a small team to test their integration with our current asset manager.
We noticed that our competitors had clearer Images and were selling more products on an e-commerce platform so we decided that we should make our visuals better and include demos
Because the volume of image editing and production we were encountering wasn't addressed sufficiently with our existing solutions. We first addressed our pain points and use cases, then defined our requirements and our success metrics.
We needed to handle a growing product catalogue more efficiently, so the team looked for better tools. First, colleagues raised the issue, then we searched online and reviewed vendor case studies to see which solutions might fit.
was triggered by a significant increase in content volume, which caused bottlenecks in our marketing publishing schedule.Here are the first steps our team took (Defined Key Pain Points and Requirements)
The last time my team evaluated a new solution was triggered by the increasing pressure to produce high quality visuals faster at a lower cost which our already existing tools was slowing us down. The first step we took was identifying what the problem was, like why things are slow and our editing quality is somewhat poor and not that clear and bright. We defined our requirements, researched and shortlisted the tools that seems good and is able to meet our requirements and then we ran tests to see if it's actually effective and efficient with our workflow.
What trigger a new solution was a high turnover on the production floor, we were constantly updating visual work instruction and safety guides.Our old method of using basic power point and stock photos was not effective any more.My team and I took the first step by evaluating the tool that could create clearer procedural images,we map out which specific process needed visual update.
Last time we had a product launch of 1000 new products and we were wanting to make sure our images were updated onto our PIM and our current system was not being as effective as we possibly wanted it to be
The evaluation was triggered by slow production time and inconsistent design quality.First, we identified the key problems and set criteria (ease of use, speed, collaboration). Then we researched tools like Canva and Adobe Photoshop, tested a few options, and gathered team feedback before deciding
we started evaluating a new image editing tool after our content deadlines kept slipping due to slow rendering and limited features, so we first defined our must have requirements and tested a few top options with small real world projects to compare speed, quality, and eae of use.
The last time our team evaluated a new solution for image editing was when we noticed limitations in our current tool—specifically, it couldn't handle detailed background removal or realistic style transfers efficiently. The first steps we took were to identify the key requirements (speed, quality, ease of use), research available tools, and run a small batch of test edits to compare results before deciding whether to adopt it.
Our team needed to scale content production for a growing product catalog. We first discussed requirements internally, then researched vendors, reviewed demos, and consulted peers for recommendations
My team's always on the lookout for tools that boost our workflow. Last time we evaluated a new image editing solution, it was triggered by a surge in demand for faster turnaround times on creative assets. In the first step we put together a list of must-haves: ease of use, collaboration features, and AI-powered editing tools.
297 substantive responses · selected verbatim below
Themes overlap — individual responses often name more than one trust signal. Counts reflect responses where the theme appears, not mutually exclusive categories.
If their tool consistently delivers high quality results in real use, not just demos, and they're transparent about security, pricing, and limitations. Proof of concept results, strong integration support, and real customer references matter more than marketing.
seeing clear proof they've handled the scale we operate at with real results from similar companies
transparency regarding training data (avoiding copyright issues), robust data privacy/security protocols, and explainability of AI outputs
I trust vendors who show real faces, own their mistakes, and prioritize my data's safety.
Trust is built on transparent data sourcing and robust copyright protections that ensure enterprise-grade legal safety. I prioritize vendors with proven security certifications and clear documentation on how they handle proprietary data. Ultimately, consistent output reliability and seamless integration into existing creative workflows prove a vendor's long-term value.
Strong customer references Proven results with similar companies Transparent product capabilities and limitations Clear ROI or efficiency gains
I trust vendors who offer clear copyright indemnity, SOC 2 data security compliance, and the ability to fine-tune outputs to match specific brand guidelines.
For me , it mostly comes down to how reliable and honest tool feels after actually using it. a lot of vendors make big promises but i trust the one where the results actually match what they advertise.
We tend to trust vendors that shows consistency with their output quality, clear security and data handling practices, and reliable performance at scale.Though, for us, transparent pricing, positive user reviews and strong documentation also plays a big role in building trust.
Proven case studies where they've been used for another competitive service, whether it works, whether it doesn't work and how much of the audience is engaged.
If they have clear documentation on what data they train on, if they publish their security practices and have strong model security and clear audit trails for AI actions, and can provide security questionnaires.
I trust a vendor when they demonstrate consistent product quality, provide clear examples or case studies from similar companies, offer reliable support, and show seamless integration with our existing workflow
Proven accuracy, consistent quality, transparent outputs, strong integrations, security compliance, customer references, responsive support, flexible pricing, and reliable performance at scale.
I would trust a vendor more if their data is ethically sourced and they assure me that they will take responsibility if the content generated by the AI is similar to a work that has copyrights
Factors like result quality, transparent pricing, strong security, positive reviews, and consistent performance across real world cases make me trust one vendor over another.
I trust a vendor when they show proven results with companies like mine, and demonstrate strong product quality, and clearly offer scalability with easy integration into existing systems.
I usually trust a vendor more if they nail the following: Transparency, ethical safeguards, performance & quality, support & updates, user reviews & reputation, and security.
I trust AI image tools that are reliable, transparent, and responsible. That means they consistently produce good results, openly share how they work, respect copyright and privacy, and admit their limitations. A strong reputation, good support, and an active user community make them even more trustworthy.
322 open responses · selected verbatim below
Counts reflect how many responses named at least one source in that category. 8 respondents explicitly said they follow nothing specific or declined to answer.
Stratechery, The Information, TechCrunch, Benedict Evans, Lenny's Newsletter, Product Hunt, Harvard Business Review, MIT Technology Review, and McKinsey Insights regularly.
I keep a pulse on the industry through Stratechery, The Verge, and Lenny's Newsletter for deep dives.
The Pragmatic engineer , Andrew NG, MIT Technology Review, TLDR
The EditPetaPixelThe Design Tools WeeklyScott BelskyDavid Airey
I follow a mix of newsletters, tech sites, and creators to stay on top of AI and creative tools. Stuff like Import AI, The Algorithm, Wired, and VentureBeat keeps me updated on trends and breakthroughs. I also watch people like Andrej Karpathy and communities around Runway ML or DALL·E for hands-on tips and inspiration.
Retail Dive
LinkedIn posts are the main majority of where we go because we trust what is on there more than any other place that you can find industry insights
I follow Reddit more than any others. The depth of knowledge and insight is incredible, so it is an easy choice to make.
Lenny's Newsletter – for product‑strategy‑level AI insights, including how teams adopt generative tools in real workflows.
Allie K. Miller, Andrew Ng, The batch, TLDR AI
I regularly follow TechCrunch for tech and AI updates, The Verge for digital media trends, and creators like Marques Brownlee (MKBHD) for practical insights on new tools and innovations.
I regularly keep up with a mix of newsletters, publications, and creators to stay current. For example, I follow MIT Technology Review, Harvard Business Review, and The Verge for broad insights, along with creators like Andrew Ng and Lenny Rachitsky who share practical perspectives on AI and product development.
I regularly follow industry leaders like Creative Bloq, The Verge, and TechCrunch for insights on content production and AI image editing tools. I also subscribe to newsletters from G2, Creatio, and Forrester for updates on enterprise software and emerging trends.
I follow marketing week, communicate magazine, hello partner newsletters and business insider.
Walmart and Amazon insiders, digital commerce 360, retail dive etc
i follow the rundown, TLDR and tech crunch
I follow substack on YouTube and import AI by Jack Clark
Marketing brew and creator science