Search is changing faster than ever – and 2026 may be the year it fully breaks from the past.
Over the last year, AI has reshaped how people discover, decide, and convert, collapsing the traditional customer journey and cutting touchpoints in half.
AI-powered assistants and large language models (LLMs) will handle roughly 25% of global search queries by 2026, per Gatner, replacing many traditional search interactions.
We’re already seeing the effects. Traffic from LLMs is climbing at a hockey-stick pace, signaling a massive shift in how users find information.
To stay competitive, marketers need to build strong content and experience flywheels, as answer engine optimization (AEO) and generative engine optimization (GEO) become critical priorities.
Bruce Clay, founder and president of Bruce Clay Inc., predicted:
“AI-powered search is expected to cause traffic to continue to drop for many sites, creating a disturbance in the force.”
Adopting AI isn’t optional – it’s foundational.
Yet most marketing systems weren’t designed to operate in an AI-first world.
Disconnected tools and data silos make orchestration difficult and create inconsistencies that derail performance.
To succeed in 2026, brands will need integrated, cross-functional, omnichannel systems that connect data, content, and customer experience.
Building a resilient digital presence for 2026
Preparing a brand’s digital presence for an AI-driven world means rethinking data, tools, and customer experiences while presenting a clear, consistent brand story.
The goal is to deliver personalized content and be ready for agentic experiences, where AI assistants act on behalf of users.
This shift begins with the evolution of search itself.
The biggest change is moving away from a simple query-and-response model to a more dynamic, reasoning-driven conversation.
Traditional search was like a game of chess – discrete and predictable. AI search, on the other hand, is more like a jazz concert – continuous and fluid.
The experience has shifted from browsing lists and visiting websites to receiving direct, synthesized answers.
Instead of matching keywords to an index, AI uses query fan-out, which involves:
Breaking queries into components.
Analyzing multiple sources.
Delivering a single, comprehensive answer based on consistent patterns.
With AI, the traditional marketing funnel is shrinking. AI search can move directly from intent to conversion in minutes, dramatically accelerating the process.
We’re already seeing three- to eight-times higher conversion rates from traffic originating in AI search.
According to Crystal Carter, head of AI search and SEO communications at Wix:
“Traffic from LLMs (like ChatGPT and Perplexity) is becoming increasingly distinct from Google search traffic, requiring separate optimization and analysis strategies.”
New types of intents, like “generative” (e.g., “create an image”) and “no intent” (e.g., “thanks”), now make up almost half of all LLM interactions and don’t require a website visit.
Search is becoming action-oriented.
As AI systems start booking tables, making appointments, and completing purchases, even transactional journeys may no longer end on your website.
Search ‘everywhere’ optimization: The new SEO
For brands, the goal is no longer to be a single destination. It’s to be present wherever your audience is.
That means becoming a trusted data source that powers the new, agentic ecosystem.
AI systems prioritize clarity, consistency, and patterns, so channel silos must give way to a well-integrated, omnichannel approach.
Ideally, AI agents should be able to access all your brand data and deliver complete, contextually accurate results based on user intent.
As Bill Hunt, president of Back Azimuth Consulting, explained:
“AI agents like ChatGPT will shift from answering questions to completing transactions. Both the Shopify connectors and feeds, as well as Walmart and Amazon saying they are Google killers. Being ‘callable’ through APIs and integrations will be as critical in 2026 as being crawlable was in 2010.”
In this new paradigm, websites are evolving from sales destinations to data and information repositories – built not just for human visitors, but for AI systems that retrieve, interpret, and act on that data.
Dig deeper. Search everywhere optimization: 7 platforms SEOs need to optimize for beyond Google
7 key focus areas shaping marketing and search in 2026
To compete in 2026 and beyond, brands must optimize for visibility across every relevant platform.
Here are seven key priorities and emerging trends shaping the future of search and martech.
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1. Strengthen technical SEO foundations for AI retrievability
The foundation of search is shifting from traditional crawlability to GEO.
The core principle of GEO is retrievability – ensuring that high-quality content is not only discoverable but also easily accessible and understood by AI models.
To prepare for this shift, your website should serve as a centralized data hub for your content and digital assets, enhancing the experience for both humans and AI systems.
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Make sure to grant access to AI crawlers in your robots.txt file, use server-side rendering (SSR) for core content, and adopt progressive indexing protocols like IndexNow, used by Bing.
2. Build localized visibility in AI-driven environments
Local SEO has evolved – from data accuracy in its 1.0 phase, to profile completeness and engagement in 2.0, to personalized experiences in what’s now emerging as Local 3.0.
AI models, particularly Google’s AI Mode, increasingly cite local business information from sources like Google Maps and online directories.
That makes core local SEO practices – NAP consistency and Google Business Profile optimization – critical for maintaining AI visibility.
Pages with robust schema markup also tend to earn higher citation rates in AI Overviews, reinforcing the importance of structured data for local relevance.
Dig deeper: AI and local search: The new rules of visibility and ROI in 2025
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3. Develop an AI-assisted content flywheel
The biggest challenge today isn’t just creating content – it’s creating a connected experience.
As companies integrate AI into their digital experience platforms (DXPs), the focus must shift from producing siloed assets to building a connected content flywheel.
That begins with a deep understanding of who your customers are and what they need, allowing you to fill content gaps in real time and stay present at every critical touchpoint.
DXPs are no longer static repositories. They’re evolving into intelligent, AI-native engines that proactively shape user experiences.
The ideal platform uses AI to create quality content at scale, powering a flywheel that delivers personalized, efficient, and well-governed customer journeys.
This is especially important for large brands and multilocation businesses, where updating hundreds of pages still requires manual, repetitive effort.
Here are the key steps to creating quality content and building a content flywheel.
Insights: Identify customer intent and content gaps
Your content strategy should be guided by real-time customer needs.
Use AI-powered tools to uncover the questions and challenges your audience is trying to solve.
Then analyze your existing content to identify gaps where your brand isn’t providing the right answers.
Creation: Develop deep, AI-structured content
To create content that performs well in AI search, start by assessing AI visibility and user sentiment.
Use AI to scale the development of deep, comprehensive content – always with a human in the loop.
Since AI engines draw from text, images, videos, and charts, your content must be equally diverse.
Just as important, it must be machine-readable so AI systems can synthesize and reason with it.
Prioritize an entity-based SEO strategy to build topical authority, and use comprehensive schema markup to help search engines understand your brand and content context.
Clearly structuring your data also prepares your site for advanced conversational search.
It ensures visibility in the next generation of AI-powered answer engines and readiness for NLWeb, the open protocol spearheaded by Microsoft to make websites conversational.
Dig deeper: Chunk, cite, clarify, build: A content framework for AI search
Distribute
Establish a human-in-the-loop workflow to review, update, and refresh content regularly, keeping it accurate, relevant, and effective in answering user queries.
Publish from a centralized source to maintain consistency across owned channels, and adopt rapid indexing protocols like IndexNow to accelerate discovery and visibility.
Monitor and iterate
Continuously track visibility and performance within AI models by testing target prompts.
Deploy an agile strategy – as you distribute content, monitor results, experiment with new approaches, and refine continuously, the flywheel becomes self-sustaining.
Each cycle feeds fresh insights back into the system, helping your content strategy stay adaptable and build momentum over time.
“AI search engines synthesize across ecosystems, not just pages. Marketing leaders must ensure their digital footprint works as a unified system, not isolated campaigns,” Hunt said.
Businesses must maintain consistent, clear information across every channel.
Traditional SEO is giving way to relevance engineering – a discipline centered on systematically creating and structuring content for semantic relevance.
This approach helps brands navigate today’s increasingly complex query landscape.
4. Create a consistent, data-driven experience flywheel
While the content flywheel attracts visitors, the experience flywheel converts them – a critical function in an era of zero-click searches. It operates on a continuous feedback loop.
Strategy: Building an experience strategy starts with unified data from every customer touchpoint and channel. AI can segment this data to reveal audience expectations and friction points, helping shape a strategy grounded in real behavior.
Experience: AI can then put this data to work – connecting audience intent, personas, desired outcomes, and business goals to generate predictive insights that drive personalized and agentic experiences dynamically.
Conversion: AI also helps track the customer journey through the funnel across channels and touchpoints. Dynamic A/B testing and conversion rate optimization (CRO) can then be done at scale, tailored to audience segments and intent.
Iteration: The goal isn’t perfection but agility. Monitoring performance alone isn’t enough – iteration matters. Use data to make real-time pivots, refining your strategy with every new learning.
The experience flywheel becomes a self-reinforcing engine that continuously drives engagement, builds loyalty, and accelerates growth.
5. Use AI agents to orchestrate journeys and workflows
As AI-driven search becomes increasingly agentic, it establishes a new standard for the seamless digital experiences customers expect.
To meet this demand, brands must use journey orchestration and workflow automation powered by AI agents that guide users through connected, intuitive experiences.
The key is to deploy specialized vertical AI agents trained on your business data.
By orchestrating these agents across the customer journey, you can deliver hyper-personalized, omnichannel experiences.
This is only possible if your website and systems are ready to interact with AI agents.
For internal teams, AI agents also offer major opportunities to automate manual workflows across the entire marketing landscape.
Dig deeper: How AI agents are revolutionizing digital marketing
6. Redefine KPIs for an AI-first performance model
As AI satisfies user intent more directly within search results, traditional metrics like rankings and traffic are losing relevance.
This shift means citation is the new rank, pushing teams to optimize content for retrievability rather than rankability.
As metrics like click-through rate decline in importance, new success indicators are emerging – including LLM visibility score, AI citation count, share of voice, and sentiment.
Success now depends on query diversity, or the ability to answer multiple related long-tail queries effectively.
According to Ray Grieselhuber, CEO of Demandsphere:
“Traditional metrics like impressions, clicks, and click-through rates are becoming much more difficult to rely on as KPIs. They are still useful to look at, but marketers should renew their focus on human behavior. Share of Voice is one of the best KPIs to measure this new behavior. Companies that ignore visibility in AI-driven responses risk ‘feeding that territory’ to their competitors.”
7. Integrate systems and data to power a unified marketing infrastructure
A fragmented marketing tech stack with siloed tools creates inefficiencies and hidden costs.
Data fragmentation and manual processes increase operational expenses and derail integration efforts.
Shifting focus to an integrated marketing platform – and evaluating total cost of ownership – helps overcome these challenges.
An integrated solution provides the consistency, clarity, and unified data needed to keep your digital presence adaptive and competitive.
Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement
The next phase of search and the customer journey
As we move into 2026, AI is not just another tool – it’s rebuilding the customer journey from the ground up.
With AI assistants expected to handle a quarter of all search queries, the traditional marketing funnel is shrinking.
The new landscape is defined by agentic, action-oriented interactions that can bypass websites entirely, demanding a fundamental strategic shift from every brand.
To stay visible and relevant, businesses must evolve from being destinations to being trusted data sources for AI.
That begins by fueling a content flywheel with deep, structured content accessible across every channel.
Once this flywheel attracts an audience, an experience flywheel – powered by unified customer data and an integrated, AI-native platform – takes over to drive conversion through deep personalization.
Ultimately, the brands that succeed will be those that embrace this new ecosystem.
They’ll replace outdated metrics, such as traffic, with new KPIs focused on AI visibility, tear down silos through integration, and prioritize delivering seamless, omnichannel experiences.
Thank you to Bill Hunt, Ray Grieselhuber, Bruce Clay, Crystal Carter, David Banahan, and Tushar Prabhu for their insights and contributions.