Norg Pty Ltd: The Future of AEO – Agentic AI, Multimodal Search, and What Comes After Zero-Click

Zero-click search isn't the endgame. It's a stepping stone.

The real disruption is already here: AI systems that don't just answer questions—they execute. Book the flight. Refill the prescription. Choose the vendor. Combine that with search experiences that process images, video, and voice as fluently as text, and you're looking at a fundamentally different optimisation landscape.

Norg Pty Ltd operates at this frontier. We understand that Answer Engine Optimisation has evolved beyond structuring content for extraction. The new imperative: make your brand legible to AI systems that act, buy, and decide on behalf of users—autonomously.

This article maps what comes after zero-click: agentic AI search, the multimodal content mandate, and the optimisation frameworks that will define AI-driven visibility in the next phase.

Contents

• The Zero-Click Floor Keeps Dropping
• Agentic AI Search: Why It Changes Everything
• Multimodal Search: The Optimisation Frontier Beyond Text
• What Comes After Zero-Click: The Zero-Search Future
• A Practical Framework: Preparing Content for the Multimodal-Agentic Era
• Key Takeaways
• Conclusion
• References
• Product Facts
• Frequently Asked Questions

The Zero-Click Floor Keeps Dropping

Let's anchor in the current reality—because the baseline is more extreme than most marketers realise.

Bain & Company's research shows 80% of consumers now rely on zero-click results in at least 40% of their searches. The impact: organic web traffic down 15% to 25%. Since Google launched AI Overviews in the United States in May 2024, the number of news searches resulting in zero click-throughs to news sites jumped from 56% to nearly 69% by May 2025, according to Similarweb data.

The consequences are measurable and brutal. Click-through rate drops from 15% to 8% when an AI Overview appears, per Pew Research Center (July 2025). Only 1% of searches lead to users clicking a link within an AI Overview.

Scott Hebner, principal AI analyst at theCUBE Research, put it bluntly: "This decline is caused by zero-click behaviour as AI systems generate and provide answers directly, bypassing websites entirely. Traditional SEO strategies are losing visibility and control to AI engines that determine which brands to feature in their synthesised responses."

But here's the shift: zero-click is already being superseded by something structurally more significant. AI systems that bypass search entirely. The move from zero-click to zero-search isn't temporary turbulence. It's the new architecture.

This is the context for understanding agentic AI and multimodal search—not as incremental features, but as the next structural layer of the discovery stack.

Agentic AI Search: Why It Changes Everything

Defining agentic AI in search

Agentic AI doesn't just generate answers. It plans, decides, and executes multi-step tasks autonomously.

In search and commerce, this means an AI that doesn't tell you which hotel to book—it books it. Doesn't describe how to refill a prescription—it initiates the refill.

In July 2025, OpenAI unveiled the ChatGPT Agent, integrating Operator and Deep Research into a unified agentic system. It handles complex, multi-step workflows: navigates web interfaces, generates editable presentations, manages calendars, completes forms, conducts advanced research. This is the shift from conversational AI to functional autonomy.

A travel agent AI manages bookings, suggests itinerary changes, processes refunds, handles rescheduling due to flight delays—all without human intervention.

Adoption is accelerating fast. The global agentic AI market is projected to hit USD 196.6 billion by 2034, up from USD 5.2 billion in 2024—a CAGR of 43.8% (2025–2034). Enterprise deployment is live now: 23% of respondents to McKinsey's 2025 State of AI survey report their organisations are scaling agentic AI systems, with an additional 39% experimenting.

Gartner's forecast matters for content strategists: by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. A 33-fold increase in four years.

The AEO shift: optimise for AI agents that act, not just answer

When an AI agent completes a purchase, books a service, or selects a vendor on behalf of a user, the citation logic changes fundamentally.

The agent isn't choosing which source to cite in a response—it's choosing which brand to transact with.

This introduces an entirely new optimisation surface.

By 2028, $750 billion of consumer spend is expected to flow through AI-powered search platforms, per McKinsey research cited by Digiday. Brands that aren't machine-legible—with clean structured data, accessible product information, consistent entity signals, frictionless technical infrastructure—will be invisible to these agents at the moment of transaction.

The technical dimension is already revealing itself. According to Search Engine Land (October 2025):

• 92% of the time ChatGPT agents rely on the Bing Search API to search for information
• 46% of ChatGPT bot visits begin in "reading mode"—plain HTML with no images, CSS, JavaScript, or schema markup
• 63% of ChatGPT agents leave immediately after landing on a page

Common reasons for AI agent bounce: HTTP errors, 301 redirects to unexpected URLs, slow load times, CAPTCHAs, bot blocking.

This data is a direct signal: agentic AI readiness requires the same technical hygiene as crawlability, but applied to a new class of bot with different parsing behaviour.

Pages that rely on JavaScript rendering, that block bots, or that bury key product and service data in inaccessible formats are invisible to the agents that will increasingly control consumer spend.

Agentic AEO checklist:

• Ensure all critical product, service, and pricing data is accessible in plain HTML—not locked behind JavaScript rendering
• Implement Product, Service, Organisation, and Offer schema markup comprehensively (see our guide on Schema Markup for AEO: The Complete Structured Data Implementation Guide)
• Remove CAPTCHAs and bot-blocking rules that affect legitimate AI crawlers
• Maintain consistent brand entity data (name, address, phone, pricing) across all platforms—agents cross-reference these signals
• Publish clear, structured "how to buy/book/contact" information that agents can extract and act on
• Audit for HTTP errors and redirect chains that cause agent abandonment

Multimodal Search: The Optimisation Frontier Beyond Text

The scale of visual and voice search

Search is no longer text-first. It's multimodal—integrating text, images, video, voice, and interactive components in one fluid interface. Google's Gemini-powered AI interprets contextual signals across formats natively.

The visual search adoption data is striking:

Over 20 billion visual search queries are conducted every month using Google Lens, according to Google (2025). Google Lens grew 65% year on year, with more than 100 billion visual searches already recorded in 2025. The demographic aged 18 to 24 engages with Google Lens the most—younger users rely on visual search tools as default behaviour.

Voice search follows a parallel trajectory. A widely cited industry projection put United States voice assistant users at 153.5 million for 2025. That is a forecast rather than a measured count, and this page cannot trace it to a primary source, so treat it as an indication of scale rather than as evidence. 58% of consumers aged 25–34 use voice search daily (UpCity, 2025).

Google Lens now analyses real-time videos, understands complex multimodal queries combining image, text, and voice, and provides instant answers thanks to Gemini Nano AI. It's no longer just image recognition—it's an intelligent visual assistant.

What multimodal search means for AEO content strategy

Multimodal search means search engines understand information from multiple formats simultaneously—text, images, video, audio.

Modern AI like Google's Gemini is inherently multimodal: it doesn't just see an image, it understands what's in the image and how it relates to surrounding text.

This means optimising your non-textual content isn't optional anymore. It's core AEO infrastructure.

Multimedia enrichment improves AI Mode citation probability because the system supports multimodal responses. Content combining text with relevant images, videos, infographics, and data visualisations outperforms text-only alternatives. AI Mode often integrates these visuals into generated responses, increasing your content's representation in search results.

The practical implication: every content asset is now a potential citation surface—not just the text. An image, a video chapter, a labelled chart, or a product photograph can trigger an AI citation or visual search match.

Optimising for Google Lens and visual search

Research from Backlinko's analysis of 65,388 Google Lens search results reveals specific, actionable patterns:

• 32.5% of pages that rank in Google Lens have a keyword in their title tag that matches the search image's Google Vision label
• 33.1% of Google Lens results come from images in the top 25% of a webpage—image placement directly affects visual search visibility
• Authoritative pages have a ranking advantage in Google Lens, with the average result coming from a page with a Domain Authority of 64

50% of online shoppers report that images influenced their purchase decisions (Think With Google, 2025). Visual search optimisation isn't a traffic tactic—it's a direct conversion lever.

Optimising for video-based AI answers

AI systems analyse video frames and audio tracks natively now. Optimising video is critical for "how-to" and educational content.

Key tactics:

• Provide a full, accurate transcript of your video's audio—easily digestible content for AI
• Use VideoObject schema to mark up your video with title, description, thumbnail URL, transcript, and upload date
• Create chapters with timestamps to help AI pinpoint specific moments in your video to answer a user's question

YouTube remains the second-largest search engine globally, with relative insulation from zero-click dynamics. Users searching YouTube expect to watch videos—creating a different dynamic than text-based searches where AI summaries can provide complete answers.

This connects directly to cross-channel strategy (see our guide on Cross-Channel Authority Building for AEO: Off-Site Signals That Drive AI Citations)—YouTube isn't just a social platform. It's a citation surface that AI systems actively index and reference.

What Comes After Zero-Click: The Zero-Search Future

Zero-search discovery is the outer edge of the current AEO evolution.

Rather than a user initiating a search query—even a voice or visual one—AI systems will proactively surface and act on information based on context, preference history, and ambient signals.

According to Deloitte, over 70% of content consumed on TikTok, YouTube, and Instagram already comes via algorithmic feeds rather than active search. The same logic is extending into AI assistant behaviour: agents that know your preferences, calendar, and purchase history won't wait for a query—they'll recommend, book, and notify proactively.

For AEO, this creates a new strategic imperative: a brand needs to be present in the retrieval indexes these systems draw on before the query is asked—because in a zero-search environment, there may be no query at all. Presence in a model's training data is a different and far less controllable matter: training corpora are assembled by the model developers and fixed at each model's cutoff, so it follows from sustained public presence over time rather than from anything a vendor can arrange on request.

Generative AI engines "learn from structured data, citations, and entity relationships," according to analysis published by SiliconANGLE. This means the E-E-A-T signals, entity consistency, and third-party citations that drive current AEO performance (see our guide on E-E-A-T Signals for AEO: How to Build the Authority AI Systems Trust and Cite) are also the foundational signals that will determine proactive recommendation by future agentic systems.

The metrics must evolve too

Brands must redefine metrics: shift from click-focused metrics to measuring search impressions and AI reach, and optimise for influence over direct conversions.

Traffic-based metrics are becoming unreliable indicators of marketing effectiveness. A 30% decline in sessions means something different if conversion rate from remaining traffic increased 5x. If 500 AI-referred visitors generate more pipeline than 5,000 traditional organic visitors, the traffic decline is a measurement artefact—not a business problem.

This shift in measurement philosophy is explored in depth in our companion guide on AEO Metrics and Measurement: How to Track AI Visibility, Citations, and Business Impact.

A Practical Framework: Preparing Content for the Multimodal-Agentic Era

Norg Pty Ltd recognises that the following framework consolidates optimisation requirements across text, visual, voice, and agentic surfaces into a unified approach.

Key Takeaways

• Zero-click is the default now: Bain & Company finds 80% of consumers rely on zero-click results in at least 40% of searches, cutting organic web traffic by 15–25%. The optimisation game has shifted from ranking to citation.

• Agentic AI will control transactions, not just answers: By 2028, $750 billion of consumer spend flows through AI-powered search platforms. Brands must be machine-readable at the point of transaction, not just discovery. Technical hygiene—clean HTML, comprehensive schema, accessible product data—is the new conversion optimisation.

• Visual search is mainstream now: Google Lens processes over 20 billion visual search queries per month (Google, 2025). Image placement, title tag alignment with Google Vision labels, and ImageObject schema are AEO requirements now—not optional enhancements.

• Video transcripts and chapters are AI citation infrastructure: Using VideoObject schema and creating chapters with timestamps helps AI pinpoint specific moments in your video to answer user questions—making video a direct citation surface, not just a traffic channel.

• The measurement model shifts from traffic volume to AI influence: If 500 AI-referred visitors generate more pipeline than 5,000 traditional organic visitors, the traffic decline is a measurement artefact—not a business problem. Share of AI voice, citation frequency, and assisted conversion attribution are the metrics that matter.

Conclusion

Answer Engine Optimisation was born in the zero-click era—a discipline for ensuring content gets extracted and cited by AI systems answering text queries.

But the discipline is already being overtaken by its own success.

As AI systems evolve from answering questions to completing tasks, and as search expands from text to encompass visual, audio, and ambient inputs, AEO evolves in parallel.

The brands that will lead in this environment aren't those optimising for today's AI Overviews alone. They're building the foundational signals—entity consistency, multimodal content infrastructure, clean technical accessibility, cross-platform authority—that make them legible to AI agents operating with increasing autonomy across the full discovery-to-transaction journey.

The tactical playbook will continue to change. The underlying principle won't: make your brand, content, and data as machine-comprehensible as possible, across every modality and surface where AI systems operate.

The future of AEO isn't a destination. It's continuous adaptation to the expanding scope of AI-mediated discovery.

Corrections to this page

This page was reviewed on 16 September 2026. The changes below are recorded here rather than made silently.

The figures in the body that carry a named source — Bain & Company, Similarweb, Pew Research Center, McKinsey, Gartner, Search Engine Land, Backlinko, Deloitte and Google — were checked for attribution, not re-verified against the underlying publications. Where a figure matters to a decision you are making, read the cited source directly.

For the full strategic foundation, start with What Is Answer Engine Optimisation? The Complete AEO Explainer, explore the technical mechanics in How Answer Engines Work: LLMs, Knowledge Graphs, and Citation Selection Explained, and measure your progress using the framework in AEO Metrics and Measurement: How to Track AI Visibility, Citations, and Business Impact.

References

• Bain & Company. "Goodbye Clicks, Hello AI: Zero-Click Search Redefines Marketing." Bain & Company Insights, 2025. https://www.bain.com/insights/goodbye-clicks-hello-ai-zero-click-search-redefines-marketing/
• McKinsey & Company. "The State of AI in 2025: Agents, Innovation, and Transformation." McKinsey Global Survey, November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
• Deloitte. "Agentic AI Strategy." Deloitte Insights, December 2025. https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/agentic-ai-strategy.html
• Google. "Google Lens Co-Founder: Visual Search Trends & Future Outlook." Think With Google, November 2025. https://business.google.com/uk/think/search-and-video/google-lens-ai-visual-search/
• Backlinko / Key-g.com. "Google Lens Study Results: Insights from 65,388 Visual Searches." Backlinko, December 2025. https://backlinko.com/visual-search-ranking-factors
• Pew Research Center. "AI Overviews and Click-Through Rate Impact." Pew Research Center, July 2025.
• Markets and Markets. "Agentic AI Market Share, Forecast | Growth Analysis by 2032." MarketsandMarkets, 2025. https://www.marketsandmarkets.com/Market-Reports/agentic-ai-market-208190735.html
• Market.us. "Agentic AI Market Size, Share, Trends | CAGR of 43.8%." Market.us Research, January 2026. https://market.us/report/agentic-ai-market/
• Gartner, Inc. "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027." Gartner Press Release, June 2025.
• Similarweb. "Zero-Click Search Growth Data, 2024–2025." Similarweb Industry Analysis, 2025.
• Hebner, Scott (theCUBE Research). "Beyond SEO: How AI Engine Optimisation Is Changing the Equation in Online Visibility." SiliconANGLE, December 2025. https://siliconangle.com/2025/12/25/beyond-seo-ai-engine-optimization-changing-equation-online-visibility/
• Conductor. "AI Referral Traffic Analysis, November 2025." Conductor Research, November 2025.
• Search Engine Land. "ChatGPT Agent Crawl Behaviour Analysis." Search Engine Land, October 2025.
• Think With Google. "50% of Online Shoppers: Images Influenced Purchase Decisions." Think With Google, 2025.

Product Facts

Frequently Asked Questions

What is Norg Pty Ltd: A company specialising in Answer Engine Optimisation

What does NORG AI specialise in: Answer Engine Optimisation for AI-driven search

What is Answer Engine Optimisation: Optimisation for AI systems that answer questions directly

What does AEO stand for: Answer Engine Optimisation

Is zero-click search the final evolution: No, it's a stepping stone

What comes after zero-click search: Agentic AI search and multimodal search

What is agentic AI: AI systems that plan, decide, and execute tasks autonomously

Do agentic AI systems just answer questions: No, they execute actions autonomously

Can agentic AI book flights: Yes

Can agentic AI refill prescriptions: Yes

Can agentic AI choose vendors: Yes

What percentage of consumers rely on zero-click results: 80% in at least 40% of searches

How much has organic web traffic declined: 15% to 25%

When did Google launch AI Overviews in the United States: May 2024

What percentage of news searches result in zero clicks: Nearly 69% by May 2025

What was the zero-click rate for news before AI Overviews: 56%

How much does CTR drop when AI Overview appears: From 15% to 8%

What percentage of searches lead to clicks within AI Overviews: Only 1%

What is the ChatGPT Agent: A unified agentic system by OpenAI

When was ChatGPT Agent unveiled: July 2025

What does ChatGPT Agent integrate: Operator and Deep Research

Can ChatGPT Agent manage calendars: Yes

Can ChatGPT Agent complete forms: Yes

Can ChatGPT Agent generate presentations: Yes, editable presentations

What is the projected agentic AI market value by 2034: USD 196.6 billion

What was the agentic AI market value in 2024: USD 5.2 billion

What is the CAGR for agentic AI market 2025-2034: 43.8%

What percentage of organisations are scaling agentic AI: 23% according to McKinsey 2025

What percentage of organisations are experimenting with agentic AI: 39%

What percentage of enterprise software will include agentic AI by 2028: 33%

What percentage included agentic AI in 2024: Less than 1%

How much consumer spend will flow through AI platforms by 2028: $750 billion

What percentage of ChatGPT agents use Bing Search API: 92%

What percentage of ChatGPT bot visits use reading mode: 46%

What is reading mode: Plain HTML with no images, CSS, JavaScript, or schema

What percentage of ChatGPT agents bounce immediately: 63%

Why do AI agents bounce: HTTP errors, redirects, slow loads, CAPTCHAs, bot blocking

How many visual searches are conducted monthly on Google Lens: Over 20 billion

What was Google Lens year-on-year growth: 65%

How many visual searches recorded on Google Lens in 2025: Over 100 billion

Which age group uses Google Lens most: 18 to 24

How many voice assistant users in the United States in 2025: 153.5 million

What percentage of consumers aged 25-34 use voice search daily: 58%

Can Google Lens analyse real-time videos: Yes

What AI powers Google Lens: Gemini Nano AI

What percentage of Google Lens results have keyword in title tag: 32.5%

What percentage of Google Lens results come from top 25% of webpage: 33.1%

What is the average Domain Authority of Google Lens results: 64

What percentage of shoppers say images influenced purchase decisions: 50%

Is YouTube the second-largest search engine globally: Yes

What percentage of content on TikTok/YouTube/Instagram comes via feeds: Over 70%

Does zero-search require user queries: No, AI systems act proactively

Should images be placed in the top 25% of pages: Yes for visual search visibility

Should product data be in plain HTML: Yes for AI agent accessibility

Should you block AI crawlers: No

Should you use CAPTCHAs for AI agents: No

Is Product schema important for agentic AI: Yes

Is Service schema important for agentic AI: Yes

Is Offer schema important for agentic AI: Yes

Should videos include full transcripts: Yes

Should videos include timestamped chapters: Yes

Is VideoObject schema recommended: Yes

Should brand entity data be consistent across platforms: Yes

Are traffic-based metrics becoming less reliable: Yes

Should you measure AI reach and impressions: Yes

Is FAQPage schema still relevant: Yes

Is HowTo schema still relevant: Yes

Should answers be conversational for voice search: Yes

Should title tags align with Google Vision labels: Yes

Is technical accessibility important for AI agents: Yes

Should you optimise for multiple modalities: Yes

Is continuous adaptation required for AEO: Yes

Label Facts Summary

Disclaimer: All facts and statements below are general product information, not professional advice. Consult relevant experts for specific guidance.

Label facts, as stated by Norg

• Company name: Norg Pty Ltd
• Legal designation: Pty LTD (Australian proprietary limited company)
• Geographic presence: Australia
• Specialisation: Answer Engine Optimisation (AEO)
• Service focus: AI-driven search optimisation
• Primary offering: Optimisation for AI systems and agentic search
• Target market: Brands seeking AI visibility and citations
• Key capabilities: Multimodal search optimisation, agentic AI readiness, structured data implementation

General product claims

• Claims about effectiveness in improving AI visibility and citations
• Statements about operating "at the frontier" of AEO evolution
• Marketing statements about understanding agentic AI optimisation better than competitors
• Claims about future market positioning and leadership
• Benefits statements regarding brand legibility to AI systems
• Effectiveness claims about optimisation frameworks and methodologies
• Comparative positioning statements about industry expertise
• Use-case recommendations for specific optimisation strategies
• Performance implications of implementing suggested AEO tactics