How AI-powered search will change the way we find information
“Just Google it…”.
For over two decades, Google has been our go-to source for answering questions, hunting down facts, and making daily decisions. But that digital default is starting to shift fast. A new kind of intelligent search is emerging, powered by artificial intelligence, and it’s poised to fundamentally change how we engage with information.
At Otter Labs, we're constantly tracking changes in the way information is accessed, understood and used, especially among developers, product creators, tech leaders and digital businesses. And AI-powered search is one of the biggest game-changers we've seen in years.
In this article, we’ll explore why this shift matters, how AI is redefining search, and what you can do to future-proof your business and product strategies.
Why AI search matters now more than ever
Information Overload Meets Evolving User Expectations
The internet has exploded with information, 90% of all the data in the world has been created in the last two years alone. Users today don't just want accurate results; they expect contextual, quick, and conversational answers. Traditional keyword-based search engines were not designed to handle the nuance and complexity of today’s queries.
Enter large language models (LLMs) and generative AI. Tools like OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude are making it easier to ask complex questions and receive coherent, well-structured responses. The rise of AI-powered search is not just about better answers, it’s about reducing friction between intent and insight.
Google knows - and it’s shifting its strategy
Even Google sees the writing on the wall. Its Search Generative Experience (SGE) is a clear signal that it’s investing heavily in making search more conversational and AI-rich. Microsoft’s Bing AI, meanwhile, has already integrated ChatGPT-like functionality, bringing a fundamentally different experience to the table: more interactive, contextual, and tailored.
For creators, developers, and product leaders, adapting to this reality is no longer optional, it’s necessary to remain visible, relevant, and competitive.
Key trends powering AI Search transformation
Trend 1: From links to direct answers
Traditional search engines return links that users must sift through. In contrast, AI-enabled search delivers structured answers, drawing from various data points and offering a synthesis right upfront.
Example:
Ask a traditional engine “What’s the best programming language to learn in 2025?” and you’ll get 10 blog posts. Ask ChatGPT the same question, and you get a comparison of top contenders (Python, Rust, Go), pros and cons, and tailored recommendations based on your goals, all in one place.
Data Insight:
According to Gartner, by 2026, 30% of all searches will be done via AI-powered assistants, eliminating the need to browse through multiple web pages.
Trend 2: Conversational interfaces are becoming the norm
Beyond answers, users now expect an ongoing dialogue. They want to refine queries, ask follow-ups, and dive deep, all within the same session. AI models allow for multi-turn conversations that emulate a real assistant or tutor, not a list of links.
Case Study:
Otter Labs integrated an LLM-powered interface for internal database search and saw knowledge retrieval time reduce by over 60% for engineering teams.
Trend 3: Contextual and personalised results
AI can understand a user's intent and context much more effectively. If you search for “best databases for real-time apps” as a frontend developer, you'll get different recommendations than a data analyst would. This level of personalisation wasn’t possible with old search paradigms.
Trend 4: Multimodal search capabilities
AI models are not limited to text. Multimodal tools that understand images, voice, PDFs, and even videos are already here. The future of search is not text-only, it’s fully integrated across content types.
Why this is a big deal for builders and brands
SEO as we know it, is changing
With AI presenting answers directly (often without citing individual sources), traditional SEO strategies, like climbing to the top of Google’s Blue Link Mountain may no longer guarantee visibility. This raises important questions: How will your content stand out in AI summaries? How can you become a trusted source for these models?
Trust and E-A-T still matter
While AI reshapes search, trust factors (expertise, authoritativeness, trustworthiness) are more crucial than ever. AI models want reliable sources; brands that deliver depth, clarity, and consistency will be favoured in machine learning models as well as human judgment.
Customer support and product discovery are being rewritten
Imagine a user asking an AI assistant, “Which task management tool integrates best with my existing Slack workflows and suits a 5-person startup?” If your product fits and your data is available, you’ll be included in the AI’s shortlist. If not, you’re invisible.
The developer advantage
For engineers and developers, AI-augmented search is already making workflow more efficient. Platforms like GitHub Copilot are changing how code is written, while tools like Phind and Perplexity are optimising how developers look for documentation, solutions, and examples.
How to adapt: Practical strategies for thriving in the AI search era
Structure your content for machines and humans
Content needs to be detailed, high quality, and semantically rich. Use clear subheadings, schema markup, FAQs, and concise summaries. Think about how your content would look if a machine scanned it for a one-paragraph summary.
Tip: Treat every piece of content like it needs to inform both a reader and an LLM simultaneously.
Optimise for featured snippets and direct answers
Craft content that naturally answers questions in conversational yet precise language. Use numbered lists, tables, and bolded key points. AI is more likely to pull from well-structured, easy-to-parse content.
Get into the training data of models
If you’re producing high-quality content that’s well-cited, chances are AI models will reference your data. Publish thought leadership, contribute to heavily indexed platforms like Substack, Medium, and GitHub, and get cited by other reputable brands.
Rethink your SEO KPIs
Traditional search analytics (e.g., organic rankings, CTRs) may not tell the full story. Consider metrics like AI citation frequency, zero-click performance, and content share-ability within AI conversations.
Build proprietary engines and embedded assistants
If you’re a SaaS business with data-rich platforms, consider integrating AI search within your own product. This not only improves UX but also builds user loyalty and differentiates your offering.
Train teams for an AI-first search mentality
Equip marketing, support, and product teams to understand how AI changes information discovery. Encourage experimentation with AI tools and prompt crafting so teams learn how to query more effectively.
The road ahead: Embracing an AI-driven search future
We’re witnessing a fundamental shift in how we engage with information. The dominance of traditional search engines is giving way to AI-powered tools that understand, synthesise and interact.
This isn’t a moment to retreat, it’s an opportunity to innovate. Whether you’re building products, scaling content strategies, or competing in the attention economy, the rules are being rewritten. Learning to navigate this AI-first landscape gives you an edge now and positions you for leadership in the near future.
At Otter Labs, we help brands, builders, and teams prepare for what’s next in search, knowledge management and user experience. And right now, what’s next is already here.
Final takeaway
The future of discoverability, knowledge, and customer engagement will belong to those who embrace AI not just as a tool, but as a design principle.
Next Steps
Is your business AI Ready? Want to explore how AI search can enhance your strategy?
