97% of people say they don’t understand what most AI tools do.
For SaaS companies, that stat means the features you’ve invested in, the product launches you’ve worked hard to market, and the landing pages you’ve polished likely confuse your audience.
We’ve passed the goldrush AI era. Now users demand to understand what AI products do and how they can be used.
Today’s challenge is to stand out by understanding your audience and clarifying your value in a way that resonates with them. This guide walks you through exactly how to do that.
The Problem: Only 3% of People Understand What Your AI Tool Does
We recently conducted a study to test how the market is adopting AI tools and features.
97% of participants didn’t understand any of the AI tools and features they read about, and 26% say they feel overwhelmed by the sheer volume of AI tools and claims.
Instead of creating clarity, the flood of updates about AI tools has created fatigue and confusion.
The problem isn’t that users don’t care about AI, they do. It’s that most companies are adding to the noise rather than explaining what value their AI tools and features deliver.
The Gap Between User Struggles and AI Messaging
For all the noise around AI, most users approach new tools with very specific problems in mind.
They’re not looking for abstract claims about “intelligence” or “automation.” They’re trying to fix the daily challenges that slow them down.
Here are the top challenges that our study participants wanted AI tools to help them with:
These are practical and relatable problems most people experience, and that AI has the potential to solve.
Yet SaaS marketing often misses this connection, focusing on broad promises or minimalist, quippy taglines instead of showing how features address these everyday struggles.
For instance, v0’s tagline, “What will you ship?”, is unclear to users unfamiliar with developers’ jargon.
Considering the landing page for this AI product features little other explanation, it left participants scrambling to figure out what the tool does or how it can help them.
The result?
A widening gap between what users need and what companies explain. Until that gap is closed, AI features risk being seen as noise instead of solutions.
That said, there are many companies that offer more explanation for their features, like Jasper.
They connect their product and features to a specific audience segment and they tailor their marketing material to solve this segment’s daily challenges.
Jasper also links specific features to various marketing roles making it easier for users to understand exactly how the platform can help them:
In our study, Jasper was consistently one of the most highly rated tools for how well it explained its features and being rated:
- 4.4/5 for how effective its messaging was at driving signups
- 4.8/5 for its ability to keep users engaged
- 4.4/5 for how well it explained what it does
These ratings reflect relative performance. Compared to other AI companies at the time, Jasper explained its features more clearly and kept users engaged better. Yet participants still felt that clarity was lacking overall, especially around:
- How Jasper’s AI works
- How it generates content
- What sets it apart from competitors
- How its pricing and features break down
In other words, Jasper was ahead of the pack but still not quite at the level of transparency users expected.
When we ran the study (April 2024), Jasper’s homepage reflected this gap. Since then, Jasper has rebranded and improved its messaging, though in my review of the current site, many of the same participant concerns (particularly around examples, case studies, and feature/pricing detail) remain only partially addressed.
What Users Really Want to Know About AI Tools
As your audience becomes desensitized by AI hype, they become increasingly sceptical.
Many participants in our study expressed concerns and objections over:
- The creativity, control, and quality of certain AI features and their outputs
- The believability of reviews and testimonials, especially for new AI platforms
- The excessive hype that makes features sound too good to be true
These objections lead them to look deeper for answers. It’s no longer enough for you to say your platform offers AI features.
You must also explain exactly how it works and demonstrate outcomes before users can trust that your solution is worth signing up for.
My colleague, Daniel, has written an excellent guide on polishing homepages for AI tools.
But what about the rest of your marketing? Here are the top five components that matter most.
1. Believability — Do your features sound too-good-to-be-true?
For AI tools, the biggest credibility hurdle is believability.
Audiences have been burned by inflated promises that oversell what a product can do. Early hype led many to believe AI could replace entire workflows overnight. And it was easy for companies to lean into the hype, overpromising capabilities, because the technology was nascent.
The market was figuring it out just as fast as the people creating the software.
Today, expectations have leveled out: people know AI can accelerate tasks, but they’re equally aware of its limitations. For example, most people still don’t trust AI to completely create publishable content without human involvement.
Therefore, if your messaging leans too heavily on “magic button” claims, it will trigger scepticism rather than excitement. Here’s an example:
Most participants in our study expressed concerns around the quality of AI outputs. Promising “human-quality work” immediately sounds too good to be true and can quickly trigger scepticism.
The key to overcoming AI scepticism is authenticity.
Potential buyers now scrutinize everything from marketing copy to testimonials, asking whether they’ve been AI-generated themselves. If you offer a promise that’s too good to be true followed by polished and unrealistic content, the more likely your solution is to be dismissed.
Here is some of the feedback we received in our test from sceptical participants echoing this sentiment:
“I don’t know. [The testimonials] sound like fake quotes.” – Marketer
“I’m a little skeptical… It sounds very impossible to build a website or an app just from writing in a prompt.” – Marketer
“I’m 90% sure this website was made with AI. And if [this AI platform] made the website itself, it’s not very promising.” – Director of IT Company
“Some of these numbers, like a 3x increase? How do you measure that to know that it was an increase?” – Head of Marketing
Instead of claiming that your AI “instantly makes things better,” buyers want clarity about what it can specifically handle.
For example, saying your tool “builds an instant website” isn’t enough—developers want to know which languages, frameworks, and processes it supports.
To build trust, dial back the superlatives and highlight concrete, believable outcomes.
People aren’t expecting miracles anymore. They’re looking for tools that can deliver incremental, tangible value and quality they can rely on.
2. Technical transparency — How do things work under the hood?
If believability is the first hurdle, technical transparency is the second. Even when buyers trust your claims, they still want to know how the tool works.
Glossing over the details leaves them feeling uneasy, especially when the product seems like a black box. Unfortunately, the current trend of many tools seems to be glossing over all the details.
For instance, this is Lovable’s homepage:
And here’s v0’s:
Both tools use AI to create apps and websites from natural language. But they offer very little substantial information for users to compare the two. Now multiply this minimalism across most of the AI tools being launched and it quickly becomes apparent why consumers are yearning for more technical transparency.
Without it, many people experience overwhelm and fatigue when evaluating new AI tools.
The burden is on the prospect to try and figure out the nuances of each tool and which tools can deliver what they’re after.
In particular, consumers are more wary of platforms that are cleverly-disguised GPT wrappers.
In our study, they also consistently asked for clarity on the role of AI versus human input. They wanted to know how much they could control, edit, or override the system.
Skilled audiences are more likely to seek nuanced information about how the system works than generic audiences. For instance, these are some of the responses in our study sharing a similar sentiment:
“I would definitely be interested in seeing its capabilities, how much quality comes from [the] prompts and how much I need to edit.” – Developer
“I don’t know how it integrates, for example, a shopping module. How can I host this? How can I just get notified? How [are] all of these APIs going to work?” – Developer & Entrepreneur
“I still have questions about how it learns about your brand guidelines, what the quality of copy would be like, what the approval levels will be, how many varieties it will create.” – Product Lead
“What AI software or technology is behind this? What exactly are you pulling from in order to generate the content?” – Head of Marketing
The key to solving this isn’t drowning people in technical jargon. It’s about showing the mechanics in plain language: what the AI does, what it doesn’t, and where human oversight comes in.
For example, Howso does this quite simply with this visualization:
It maintains technical transparency without overloading the reader with too much jargon they may not be able to understand.
However, Copy.ai has taken it a little too far since it relies on specific industry acronyms that many visitors may not know.
Even though there’s a lot of technical information on the screen, it does not offer the transparency that answers how it works under the hood.
Transparency about integrations, limitations, and processes signals confidence in your product and gives buyers the confidence they need to try it.
3. Positioning — How is your tool different from ChatGPT or competitors?
Another common source of scepticism comes from positioning. With so many AI products flooding the market, users struggle to see what makes one tool different from another or why they should pay for something they could already do in ChatGPT.
For example, Jasper faced this struggle a few times in its early days.
It initially launched as a GPT wrapper that exclusively focused on content generation a few years before ChatGPT was made publicly accessible.
At the time, access to OpenAI’s product was limited to the public. So Jasper had a competitive edge. It ran prompts on behalf of users (as most GPT wrappers do).
The business model was simple. They had a nice dashboard with pre-made prompts loaded in. Users could add context for short-form content they needed to generate for ads, social posts and more.
Boss mode was a revolutionary development back then, allowing users to generate a full blog post with AI. But even then, users had little control over the prompts.
At the time, it was one of the first tools of its kind and was considered best in its class, amassing wild popularity among marketers.
But after ChatGPT launched, its value became obsolete. People were paying hundreds a month for something they could now do for free to a reasonably similar level using (mostly) the same technology. Jasper was not the only company that faced this situation.
The phrase “powered by AI” no longer carries weight. It’s table stakes. Buyers want to know exactly what makes your solution stand out:
- Is it faster?
- Does it integrate better?
- Does it use AI in a revolutionary way?
- Is it better at controlling the output quality?
- Does it solve a very specific problem for a defined audience?
Messaging that pinpoints your unique benefits is critical. Without it, your tool risks being dismissed as “just another wrapper”, as evidenced by the feedback we also received in our test:
“I want to know why this is better than [ChatGPT].” – Founder
“What makes it special? Why should I give you my money?” – Project Coordinator
For example, FirmPilot, an AI law firm marketing platform, addresses this question head on:
The challenge is that most landing pages across AI tools look the same.
Dozens of tools pop up claiming to “do a thing,” but from the outside, it’s nearly impossible for consumers to judge which ones are well-engineered, thoughtfully designed, or offer a genuinely new approach.
Jasper is a good example—after multiple rebrands and identity shifts, it eventually carved out a place in the market by clarifying who it was for (marketers) and how it was different (unifying the brand experience).
That level of specificity (what the tool does, how it works, and why it matters) is what cuts through the noise.
Positioning isn’t about being louder than competitors. It’s about being sharper, clearer, and more relevant to the exact audience you want to reach.
4. Social proof — Do you have real world examples and user stories?
If there was one piece of feedback every tool received in our study, it was this: not enough real-world examples.
When evaluating AI tools, users expect more proof than they would for other software categories, especially if they suspect AI was used to generate the landing page they’re reading.
They want to see evidence that’s both tangible and authentic. That means showing (not just telling) how your product delivers value.
Participants specifically asked for:
- Case studies that focus on authenticity and outcomes rather than sensational claims.
- Product interface screenshots so they can picture how it works in practice.
- Video demos or walkthroughs that show tasks completed step by step.
- Interactive product tours to “try before you buy.”
- Comparisons with manual processes to quantify time saved or quality improved.
- Use-case driven examples for specific roles or industries.
It’s not enough to provide just one example. People process information in different ways. Having a mixture of textual, visual and auditory options can help offer different forms of proof before consumers feel confident moving forward.
“I need to see a demo. I don’t actually understand how it works yet.” – Entrepreneur
“Highlight three or four case studies.” – Marketer
“The biggest thing missing is a two to three minute video which tells a prospective customer exactly what [the tool] is.” – Marketer & Entrepreneur
Without these, users struggle to imagine themselves using the product and that uncertainty usually leads them to bounce.
Real-world proof turns a vague promise into a believable product story. The more angles you provide, the faster AI scepticism drops.
5. Integration — Can they easily see how the tool fits into their existing workflows?
Even when a product looks promising, scepticism often comes down to a simple question: will this work for me?
Users want to know whether an AI tool will slot neatly into their current systems, processes, and tech stack or if it will create more friction than it solves.
In our study, participants consistently asked about integrations with the tools they already use, from CRMs and CMSs to APIs and automation frameworks. The appeal of AI is efficiency, but that value is lost if adoption means overhauling existing workflows.
“Does it integrate with all platforms? How easy or how hard is it?” – Website Marketing Manager
“How do you get from concept to the finished product? When I sign up for $500 a month, how do I actually use [it]?” – Computer Application Specialist
Participants also responded positively when messaging was clear about integrations.
Lovable’s landing page, for instance, resonated with developers because it showed API-level connections and backend integration:
“I also like the integration on the API level and the back end side. This makes it very clear to me what exactly you are about.” – Developer
Buyers want to see upfront whether your tool will fit into their existing workflows without disruption.
Highlight supported platforms, automation capabilities, and real examples of end-to-end usage. The clearer this picture, the easier it is for someone to imagine your tool as part of their daily work.
Conversion Levers When Marketing AI Features: From Confusion to Clarity
The AI gold rush is over. Users are no longer dazzled by “AI-powered” promises, they’re overwhelmed by them.
What they want now is clarity: clear proof that your tool solves their problems, fits their workflows, and delivers value they can trust.
Our study showed that scepticism isn’t going away anytime soon. People question believability and 97% of them desire more clarity.
The good news? Each of these objections is an opportunity. You can move your messaging from noise to clarity by addressing them directly with these 5 AI marketing rules:
- Dial back hype and highlight believable outcomes
- Explain how things work in plain language
- Sharpen your positioning against competitors
- Provide real-world examples and demos
- Show clear integration into existing workflows
AI tools don’t need more mystique; they need more trust.
And the companies that win in this next era will be the ones that turn abstract claims into concrete stories users can see, understand, and believe.
