Even the smartest AI feature fails if users never see it.
That’s the paradox many businesses face today. They’ve built powerful AI capabilities, but no one’s clicking, using, or noticing them. With the rapid rise of AI in recent years, product teams are now designing for two audiences at once:
- Real users, who need intuitive, trustworthy in-product experiences.
- AI systems, which extract and summarize content in search results.
The rules of discoverability have changed. So have the rules of design. At Eleken, we’ve seen firsthand how this shift opens up opportunities and introduces risks. Based on these insights, we’ll show how to design AI features users actually notice.
What “discoverability” really means
When it comes to UX terms, it’s easy to confuse discoverability with findability. They sound similar, and at a glance, they seem to describe the same thing. But they’re not. Here’s a quick reminder of how they differ:
- Findability means users can easily locate a feature if they’re looking for it.
- Discoverability means users notice, recognize, and understand a feature.
In a series of qualitative usability tests, Nielsen Norman Group found that most AI features failed on both fronts. Participants didn’t expect to find AI features, didn’t notice them, and ultimately didn’t use them.
In many cases, they didn’t even realize such features were available.
To close this gap between what’s built and what’s seen, you need to think like your users. The feature needs to show up in the right place, match the user’s mental model, communicate what it does, and inspire confidence to interact with it.
At Eleken, we’ve tackled this exact challenge. When myInterview, a video interviewing platform, came to us, they were facing a 90% user churn. Among other issues, our task was to introduce a new AI feature without increasing the drop-off.
For design, we used familiar UI patterns and a step-by-step flow to make the feature intuitive. And we didn’t forget about discoverability. We added it to the dashboard and embedded it inside Microsoft Teams, where recruiters were already working.
This subtle shift made a difference.
Strategies for better AI features discoverability
In theory, you might create a feature that adds real product value and solves a meaningful problem. But in practice, users may not find it at all. Most people don’t expect AI to show up in every app, so they tend to overlook unclear UI elements.
If you want to avoid that trap, you need to design AI feature discoverability from the start. Below are four practical ways that can help you with that.
Visibility
To improve visibility, start with where users look and how they look. For example, placing a chat assistant in the bottom‑right floating bubble makes sense because that’s the spot many users already associate with conversational help.
But if you bury the same feature behind an obscure icon or menu, it becomes invisible by default.
Consistent use of familiar icons, placement, and design conventions reduces cognitive load and aligns the interface with users’ mental models. Using an unconventional icon or hiding the AI feature risks being entirely ignored.
So, when designing for visibility:
- Use familiar design patterns so the feature doesn’t feel foreign.
- Position features where users naturally expect them.
- Make the affordance clear with visual cues or micro-interactions.
- Avoid relying on novelty alone and bring new features into existing flows.
We followed this same logic while working on Siena, a go-to commerce agent for CX teams. As they expanded their product capabilities, our task was to design a new AI feature that helps teams manage customer interactions through automation.
To ensure it was seen and used, we placed a prominent “Create a new automation” button in the top-right corner. Set against a neutral background, the gradient styling made the button stand out, while its label told users what they could do next.
To reinforce the affordance, we added a small arrow that expands the menu and allows users to build an automation with AI, start from scratch, or use a template.
Expectation
Users come to your product with assumptions about how things work. If an AI feature doesn’t fit those mental models, they may not even recognize it’s there.
That’s why naming and labeling your AI features clearly is vital. If you brand an AI capability with a quirky internal codename like “Rufus” or hide it behind an icon that doesn’t say what it does, you risk losing users before they even press the button.
Instead, choose plain, descriptive names, like “Ask AI” or “Smart Summary”. These labels mirror what users think and help them quickly recognise the purpose of a new AI-driven tool. That recognition triggers a higher chance of interaction.
To apply this in your design work:
- Choose names that describe the action or benefit.
- Keep labels consistent across UI, tooltips, and communications.
- Avoid hiding the feature under vague menu labels or mysterious icons.
- Use contextual cues to reinforce what the feature is and why it matters.
As a great example, you can take a look at our work on Frontend AI, an open-source software project. This tool uses artificial intelligence to generate UI components from simple prompts, allowing users to build interfaces faster.
Our task was to design the moment when users engage with the AI, and we decided to keep the entry point simple. In the left sidebar, we added an “Ask AI” option. Clicking it opens the AI assistant along with a prompt window.
From there, users can paste text, upload images, view their generation history, and resend prompts, all without confusing labels or unnecessary friction.
Understanding and trust
Once users spot your AI feature (visibility) and it feels expected (expectation), the next challenge is to help them believe in it. For AI features, especially, understanding and trust are the hidden gates to adoption.
Most users approach new features with skepticism, questioning what the feature really does, whether it will save them time, and if they can trust the output. If you don’t address those concerns early, the feature will likely go unused.
Your job as a designer is to bring clarity through UI and friendly AI feature onboarding, and build trust through gentle transparency and user control.
Here are a few practical ways to support that:
- Show a short “How it works” explanation near the feature.
- Let users undo or edit the AI’s output.
- Include example input/output to set expectations clearly.
Our team at Eleken used this approach while designing MODIA, an AI content creation and publishing platform. Because the product is one-of-a-kind with no direct competitors, we knew we had to onboard users carefully.
To build trust and reduce friction, we implemented a series of contextual hints explaining how the platform works, and specifically, how to use the AI feature. Importantly, we gave users the ability to skip the training entirely.
We also designed the input field for the AI interaction to feel familiar. There are no surprises, and that’s exactly what makes the platform effortless to use.
Feedback
Just like you would usability‑test any major feature, you must test your AI feature and determine if users need it at all. Early exploratory research helps you find where in the user journey a feature naturally fits, and what users expect it to do.
Later, evaluative testing and usage analytics reveal whether users are noticing and using it, or ignoring it altogether.
If you see low usage or poor find‑rates, don’t assume the problem is the feature. It may be suffering from wrong placement, misleading iconography, or an unclear name. What seems obvious to the design team may be invisible to a busy user.
To build effective feedback loops, you should:
- Define metrics like activation rates, time-to-first-use, and repeat usage
- Monitor how users interact with the feature across devices.
- Conduct regular usability tests to improve AI feature discovery.
Recognizing the importance of feedback, we took this approach when designing Zaplify, a customer relationship agent. We started from zero, focusing on what a good outbound flow actually feels like for a salesperson.
We interviewed sales reps, watched user sessions, and pulled in inspiration from similar tools. Based on the gathered info, we designed an AI-powered workspace that felt structured, human, and built for productivity.
The interface combined a natural chat window with AI-generated messaging, personalization options, and a side panel with contextual prospect information.
How AI search changes content visibility
Previously, traditional Google search ranked pages based on relevance, backlinks, and keyword optimization. The goal was to get to the top of page one.
But AI systems work differently. They synthesize answers, pulling snippets, quotes, or structured data from across the web to form a complete response. Now, it all comes down to which pieces of content are easiest for the AI to understand.
That’s why there’s a growing gap between what ranks in classic SEO and what gets surfaced in AI-generated answers. In fact, AI referrals to websites jumped 357% year-over-year in June 2025, making it clear that this is a major traffic driver.
AI assistants and answer engines work more like researchers than ranking systems. They:
- Parse the structure of your content, looking for clear sections, headers, lists, and FAQs.
- Identify entities like names, products, companies, and people that are relevant to the query.
- Evaluate clarity and authority, favoring concise, trustworthy content over marketing-heavy pages.
- Select and synthesize, choosing the most relevant text spans or code snippets to build a complete response.
This means your high-quality content is no longer competing to be the first link. It’s competing to be included in the answer itself.
If you’re building AI features inside your product, this shift matters more than ever.
Users like discovering tools through AI-generated answers. If your product’s AI capability isn’t mentioned, structured, or explained clearly enough to be picked up by LLMs, it may never show up in ChatGPT, Perplexity, or Google’s AI Overviews.
Optimizing content for external AI visibility
Building and launching a powerful AI feature is a big step. You can spread the word through social media posts, blog articles, newsletters, or usage guides. Every piece of content you publish is a chance to get picked up by AI assistants.
The trick is to write about your feature the right way, so AI can actually find it and include it in its answers. In the next section, we’ll cover how to do exactly that.
Use clear titles and descriptions
Your page’s title tag and meta description are the first things that tell readers and AI engines what your content is about (and whether it’s worth opening).
In the age of AI‑powered search and answer engines, metadata is a visibility signal. At Eleken, we spot that missing or vague titles and descriptions cause AI systems to misclassify a page or skip it entirely. You may simply become invisible.
If you want to create good titles and descriptions, here are some tips:
- Match your title to the question or task users are interested in.
- Keep titles short (50–60 char) and descriptions brief (120–160 char).
- Align your H1 with the title so users and AI get consistent signals.
- Write descriptions that clearly explain what the page solves.
AI engines often parse structured metadata first. For better discoverability, you might include intent‑rich language. Words that reflect tasks (“how to,” “step‑by‑step,” “guide,” “setup”) help clarify content purpose.
Structure content with headings
A structured article gives readers and AI systems a map of what each section is about. Using proper heading hierarchies (H1 → H2 → H3) helps large language models and answer engines understand the flow and context.
In practice, we recommend using one H1 (your main title), H2s for major topic sections, and H3s for subsections. This hierarchy signals topic shifts and scope. When writing headings, make them match the intent and avoid abstract phrasing.
As well, keep paragraphs short and focused. Long blocks of text under a generic heading make it harder for AI to extract relevant chunks.
Include Q&A formats
When users ask questions in conversational language, you want your content to anticipate them. A Q&A format is one of the most effective ways to do that. It structures your answers in a way that people and AI engines can easily understand.
By framing a section as a question and giving a direct answer, you also increase the chances of being included in a generative AI summary or snippet.
To implement Q&A formatting effectively, follow these practices:
- Use questions as headings (H2 or H3) that reflect real user queries.
- Provide a concise answer right beneath the heading.
- Embed the Q&A where it’s most relevant.
- Consider pairing each Q&A with FAQ schema markup.
If you’ve launched a new AI tool in your product, turn user pain points into questions. You can structure and present them in any visually appealing way as long as your users and the AI algorithm can understand the feature’s value and behavior.
Present information in lists and tables
Everyone prefers clearly structured information, and nothing says structure like bullet points, numbered steps, and tables. Content arranged in these formats is significantly more likely to be parsed, understood, and cited by answer engines.
Structured formats also reduce cognitive load for readers and improve scanability. With that in mind, use lists and tables wherever they naturally fit.
Use these content blocks for steps, tips, pros and cons, or feature sets. But when implementing, be intentional. Leep lists of 5–9 items and tables readable with no more than 4–6 columns, so they remain easy to process.
Add schema markup
Structured data, often called “schema markup,” is the code you add to your pages to help AI systems and search engines understand what your content is. This helps you move from simply being visible to being understood and cited.
A correct schema setup keeps your content machine-readable, improving how confidently AI systems can cite your brand and your feature.
Here are some strategies to implement for the best results:
- Choose the right schema type for your content.
- Use JSON‑LD format, as it’s preferred by major platforms.
- Validate your markup using tools like Google’s Rich Results Test.
- Ensure all properties clearly connect your content, authorship, and brand.
If you’re writing about a new AI feature in your product, you might use Feature, SoftwareApplication, or HowTo schema to label it clearly. For a Q&A section, apply the FAQPage schema to help AI systems identify and extract your answers.
Optimize for natural queries
When searching for specific information, users are typing long-tail keywords, asking full questions, and describing tasks. To stay visible in AI‑powered search, you need to create high-quality content that reflects that shift.
You can turn this into an advantage. Look up the queries users most often search for around your topic, and use them as question-style headings or subheadings. This will improve your chances of ranking and help users quickly find the answers.
Also, include synonyms and alternative phrasing so AI systems can recognize different expressions of the same intent.
Demonstrate E-E-A-T
Credibility is what sets top content apart. E‑E‑A‑T, which stands for Experience, Expertise, Authoritativeness, and Trustworthiness, helps you build that credibility. This framework is used by Google and other LLMs to judge content quality.
In simple terms, the stronger your E‑E‑A‑T signals, the more likely your content is to be surfaced, cited, and trusted by AI.
You can build robust E‑E‑A‑T for your AI‑feature content in these ways:
- Share case studies, screenshots, or user stories.
- Include author bios or team expert profiles who understand the domain.
- Earn references/links from other trusted sites, forums, or industry blogs.
- Keep your content fresh, trustworthy, and clearly attributed.
By embedding E‑E‑A‑T signals into your content, you’re making it more believable to AI systems. This, in turn, boosts the likelihood that your content (and by extension your AI feature) will be cited, referenced, and recommended.
AI discoverability loop to follow
After covering so much information, your head might be spinning, unsure of what to actually do next. That’s expected. However, there’s no shortcut on how to make AI features discoverable. It’s a cycle of learning, testing, and improving.
Based on everything we’ve talked about, here’s what that loop looks like:
1. Define whether users actually need your AI feature.
If you’ve brainstormed an exciting idea, don’t rush to build it. Validate the need by talking to users, observing their workflows, and figuring out whether this feature solves a real problem. Only then should you move into design.
2. Design with users in mind.
Once you’ve confirmed the need, design around it. Keep in mind that people follow familiar patterns and don’t always expect AI to be there. That’s why intuitive UX matters. Many of the practices we shared above can help you get started.
3. Test the design with real users.
You may love the design, but your users might not notice the feature or misunderstand it. At this stage, testing can reveal blind spots that aren’t obvious. Observe how real users interact with the interface and adjust accordingly.
4. Make it visible to AI.
With the design in place, it’s time to promote the feature to users and AI systems. Write articles, usage guides, or blog posts, and apply the AI-optimized content strategies we outlined earlier. If you’ve built something great, don’t let it stay hidden.
5. Measure, learn, and loop back.
Once the AI feature is live, your job isn’t done. Measure how many users interact with it, where they drop off, what they click, and what they ignore. That feedback becomes your roadmap for the next iteration.
Wrapping up
AI features are already part of how many people work and interact with digital products. This means that sooner or later, you’ll likely need to build one too. And when that time comes, it’s worth doing it right from the beginning.
Learn from the best practices out there, listen closely to your users, and treat AI discovery as an ongoing process. Of course, it takes time, iteration, and thoughtful UX, but the results speak for themselves.
At Eleken, we design with those details in mind. Our team often works on AI-driven products across different industries, helping companies bring their ideas to life. If you’re building something in SaaS, contact us to make it work.
