Embracing AI: The future of search and its impact on business and design
The rules of the internet are changing faster than most businesses can keep up.
In the latest episode of the UXRx podcast, Alessandro Fard and Zachary Newton from Create Ape unpack how artificial intelligence is not only automating workflows it’s rewriting the very logic of search, design, and interact online.
This isn’t about robots replacing people. It’s about a deeper transformation in discoverability, digital design, and user experience.
For over twenty years, the web has been organized around pages, keywords, and links. But AI has introduced a new model: the conversation as the search engine. Users no longer “look things up.” They ask, and expect context-aware, curated answers in return.
This article expands on their conversation and explores what this means for businesses, designers, and the future of discoverability.
AI vs. traditional search engines
To understand how dramatically AI is transforming search, it helps to remember where it all began.
In the early days of the web think the late 1990s and early 2000s search engines like Yahoo, AltaVista, and eventually Google organized the internet around a simple goal: retrieve information quickly and accurately. Algorithms like PageRank revolutionized discovery by ranking pages based on backlinks and relevance. For the first time, anyone could type a question and instantly access knowledge from across the globe.
This model worked brilliantly for nearly two decades. Yet as the web grew exponentially, search engines became commercialized. Sponsored listings, SEO manipulation, and algorithmic changes gradually shifted focus from user satisfaction to monetization and ad visibility.
By the mid-2010s, users began noticing the cracks more ads than organic results, and more friction in finding genuinely helpful information. Search was no longer about answers; it was about competition for attention.
That’s the void AI is now filling.
Unlike traditional search, AI doesn’t index and rank it interprets and synthesizes. Tools like ChatGPT, Anthropic’s Claude, and Google’s Gemini analyze intent, pull data from multiple trusted sources, and respond in natural language. The result feels less like searching and more like conversing with an expert.
As Alessandro Fard points out, “modern search results often feel like ad engines, not answer engines.” AI, in contrast, delivers context before content filtering the noise, summarizing insights, and prioritizing usefulness over monetization.
This evolution has enormous business implications. According to McKinsey’s 2024 Digital Trends Report, more than 60% of users already rely on conversational AI for research or decision-making. That means fewer people are landing directly on websites through Google, and more are relying on AI interfaces as their primary source of truth.
For companies, this introduces a profound challenge: visibility now depends less on keywords and more on comprehension. Your digital presence must be structured, credible, and semantically clear enough for AI to understand and trust.
As Zachary Newton puts it best, “The real question isn’t if AI replaces my job—it’s how it changes how customers find me.”
Answer Engine Optimization is emerging as the next stage of search
To understand where we’re going, we first need to understand what’s changing.
Search Engine Optimization (SEO) has long been the backbone of digital visibility. Its goal is to make content discoverable by search engines organizing information so algorithms like Google’s can crawl, index, and rank it. Success in SEO depends on keywords, backlinks, and on-page elements such as titles, metadata, and load speed. It’s about signaling relevance to a machine that matches queries to web pages.
Answer Engine Optimization (AEO), on the other hand, focuses on understanding rather than ranking. Instead of optimizing for an index of links, AEO optimizes for how artificial intelligence models interpret meaning. These systems don’t just scan for keywords they look for context, clarity, and authority to generate trustworthy answers. In other words, AEO ensures that your content can be read, reasoned with, and quoted accurately by AI.
Breaking down the core principles of AEO
To make that happen, three foundational elements guide how AI reads and evaluates information:
Structured data
Structured data refers to the hidden “blueprint” that organizes content behind the scenes. It uses standardized tags (like schema markup) to tell machines what’s what: a product, a review, a location, or an author. For example, when Google displays event times or recipe ratings directly in results, that comes from structured data. In an AI context, this structure helps models map relationships understanding not only what your page says, but how it connects to other topics.
Semantic hierarchy
This is the logical order and clarity of language within your content. Just as humans follow a story with a beginning, middle, and end, AI follows patterns of meaning. Clear headings, short paragraphs, and well-labeled sections help it identify which ideas are primary and which are supporting. Before, search engines mostly counted keywords; now, AI measures comprehension. Writing conversationally and organizing thoughts coherently are part of optimizing for understanding.
Content credibility
Perhaps the most critical factor in the age of AI. Credibility is about factual accuracy, consistent tone, and verifiable sourcing. In the early SEO era, quantity often beat quality whoever published the most content or had the most backlinks tended to win. Today, AI models evaluate trustworthiness. They cross-reference data, detect bias, and learn from authoritative domains. A well-researched article with clear authorship, citations, and reliable claims is far more likely to be surfaced or summarized by AI than click-bait pages or keyword-stuffed text.
Designing for a new kind of audience
For marketers and designers, this evolution represents a profound mindset shift. In the traditional web era, you designed experiences for human visitors who arrived via search engines. In the AI era, your first “visitor” might be a machine an intelligent intermediary interpreting your content before presenting it to real users.
This means design now extends beyond aesthetics. It’s about clarity, structure, and meaning. Visual hierarchy, accessible layouts, and intuitive navigation not only improve human usability they help AI systems understand intent and value.
We’ve moved from designing for clicks to designing for comprehension.
In practical terms, the brands that succeed in this new landscape will be the ones that speak fluently to both audiences: humans seeking genuine experiences, and machines translating those experiences into the language of discovery.
AI is changing what design means for modern information businesses
Artificial intelligence isn’t just changing how we find information it’s changing how that information looks, feels, and functions when we experience it.
In the traditional web era, design was the first impression. Every brand had its own aesthetic language colors, layouts, animations that shaped trust and recognition. But AI-driven systems are starting to flatten that landscape. Most AI tools deliver results through the same minimalist interfaces: a white screen, a text field, and a scrolling chat. Everything funnels through identical design templates.
Alessandro Fard explains it clearly: “AI search really abstracts design from anything- it filters everything through the same visual style.”
That doesn’t mean design no longer matters- it means design’s purpose is evolving. Designers now have to think about how information is interpreted and delivered rather than just how it looks on a screen.
1. Crafting data structures that AI can interpret
Behind every beautiful website or app is data- images, text, and metadata that describe what each element represents.
In the old SEO world, this structure helped search engines understand what a page was about. Today, that same structure helps AI models decide how and when to use your content to answer a user’s question.
For example, when a user asks, “What’s the safest sunscreen for sensitive skin?” AI pulls data from multiple sources. If your skincare brand’s site has clearly labeled product details, ingredient lists, and verified reviews structured in a way machines can easily read your information is far more likely to appear in the AI’s response.
This is why design teams now work closely with developers and content strategists. They’re not only designing visual layouts- they’re designing information architectures that tell AI what matters most.
Think of it like translating human storytelling into a language that machines can understand. A clear hierarchy, consistent labels, and thoughtful tagging all make your content more “AI-friendly,” ensuring it stays discoverable in a world where few people actually browse websites page by page.
2. Maintaining brand distinctiveness in AI-Native environments
As AI interfaces become the new gateway to content, maintaining a sense of brand identity becomes a creative challenge. If every answer is delivered through the same chat window, how do you still sound and feel like you?
The answer lies in tone, clarity, and experience design.
Your brand’s voice- how you phrase answers, write micro-copy, or design visuals- now carries more weight than ever. For example, a medical platform can maintain trust by ensuring its content is medically reviewed and written with empathy, while a lifestyle brand might emphasize conversational, optimistic language.
Designers must think beyond the screen. Voice interfaces, AI assistants, and chatbots all need to reflect brand personality through tone and interaction flow. Even subtle cues- like how an AI-generated response cites your company, or how your logo appears in summarized cards- reinforce recognition.
So while AI might standardize the container, it’s still up to designers to differentiate the experience inside it. The brands that succeed will merge precision and personality, ensuring that what AI delivers still feels human, credible, and unmistakably theirs.
The new reality for design teams
AI has turned design into a cross-disciplinary practice. UX designers now collaborate with data engineers, SEO specialists, and writers to ensure every piece of content performs on two levels:
- It resonates emotionally with humans.
- It’s structured logically for AI.
For non-technical readers, the takeaway is simple: design isn’t just about visuals anymore- it’s about communication clarity. Whether you’re running a business website, publishing content, or building an app, your design decisions must now consider how both humans and machines understand what you create.
In this new landscape, the best design isn’t the one that looks the flashiest- it’s the one that remains intelligible, trustworthy, and meaningful, no matter who (or what) is reading it.
Practical steps for navigating the AI landscape
Transitioning into the AEO era doesn’t require reinventing your digital ecosystem- but it does demand intentional upgrades.
- Fortify SEO Foundations
Speed, security, and accessibility remain essential. AI systems still favor reliable sources with strong technical performance. - Adopt AEO Practices
Integrate schema markup, entity relationships, and structured metadata. Think of your site as training data for AI. - Broaden your digital footprint
Ensure your brand appears in knowledge bases, directories, and credible third-party platforms. AI pulls from community and authority sources, not just your website. - Design for clarity and trust
Simplify navigation, emphasize transparency, and reinforce brand voice. Clarity is currency in an AI-mediated world. - Experiment and observe
Test how your brand surfaces across AI tools- ChatGPT, Perplexity, Copilot, Claude- and adjust based on what’s missing or misinterpreted.
AI isn’t just a new layer on top of the internet, it’s becoming the interface itself.
Businesses and designers who adapt early to Answer Engine Optimization and AI-first UX will define the next decade of digital experience.
At Create Ape, we believe the goal isn’t to chase every new technology it’s to build experiences that keep humans at the center of it.
Stay tuned to the UXRx Podcast for more conversations at the intersection of design, technology, and human insight.