NORG AI 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 AI 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

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But here's the shift: zero-click is already being superseded by something structurally more significant.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):

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:

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. Voice assistant users have reached 153.5 million in the United States alone during 2025. 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:

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:

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](Not specified by manufacturer))—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: brand presence must be established in AI training data and retrieval indexes before the query is asked—because in a zero-search environment, there may be no query at all.

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](Not specified by manufacturer)) 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](Not specified by manufacturer).

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

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

Content Layer Current AEO Requirement Multimodal/Agentic Extension
Text 40–60 word answer blocks, Q&A H2s, FAQ schema Conversational phrasing for voice; transcript text for video AI parsing
Images Descriptive alt text, keyword-aligned filenames Placement in top 25% of page; title tag alignment with Google Vision labels
Video VideoObject schema, thumbnail optimisation Full transcripts, timestamped chapters, YouTube presence for AI citation
Structured Data FAQPage, HowTo, Article schema Product, Offer, Service schema for agentic transaction readiness
Technical Crawlability, page speed, canonical signals Plain HTML accessibility for reading-mode AI bots; no bot-blocking of AI crawlers
Entity Signals Consistent NAP, author credentials, brand mentions Cross-platform entity consistency for agentic cross-referencing

Key Takeaways

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.

For the full strategic foundation, start with [What Is Answer Engine Optimisation? The Complete AEO Explainer](Not specified by manufacturer), explore the technical mechanics in [How Answer Engines Work: LLMs, Knowledge Graphs, and Citation Selection Explained](Not specified by manufacturer), and measure your progress using the framework in [AEO Metrics and Measurement: How to Track AI Visibility, Citations, and Business Impact](Not specified by manufacturer).

References

Product Facts

Attribute Value
Company name NORG AI Pty LTD
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
Geographic presence Australia (Pty LTD designation)

Frequently Asked Questions

What is NORG AI 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

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