Online discoverability is undergoing one of its most fundamental shifts since the advent of search. A world previously dominated by classic search engine optimization (SEO) and keyword-driven paid marketing is now evolving into something far more complex and conversational.
The days when consumers had to type keywords into a search bar, scroll through links, and click offsite to fulfill their mission are ending. People will still “speak” to the internet, but for the first time, the internet will start talking back. Thus far, search engines have brought consumers to the web. Moving forward, AI engines will bring the web to them. From ChatGPT to Perplexity, Gemini, Claude, Amazon Rufus, and emerging agent ecosystems, this is not just a technical transition — it’s a full-on platform shift. And for marketers, it demands a new operating model for visibility, measurement, and strategy.
As demand for generative search continues to grow, companies need to hone their ability to use AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization), collectively known as GXO (Generative Experience Optimization), to capture consumer attention. AEO is about optimizing content for answer engines that deliver concise, trusted, and structured responses. GEO focuses on generative platforms, ensuring content is suitable for synthesis, semantic search, and conversational contexts. Both rely on signals like authority, trustworthiness, structured data, and freshness — not just keywords and backlinks. (For background on these engines, see “ The Future of Discoverability .”)
In this piece, we explore three ways brands can win in this new environment: paid visibility in answer engines, earned presence in generative models, and participation in agent and feed ecosystems that connect platforms, consumers, and brands.
1. Paid Visibility in a Generative World: Emerging and evolving ad formats
As SEO gives way to GXO, we are also entering a new era of generative paid media opportunities — though exactly what this means is still evolving. Major players are exploring monetization models, and paid placement within generative interfaces is still in its early stages. Still, change is coming. Google’s AI Overviews are testing sponsored integrations. Perplexity’s Explore Feed offers premium content visibility. OpenAI’s ChatGPT plugins and browsing features open pathways for brand engagement, albeit with limited ad control, while Amazon Rufus integrates shopping queries within its AI assistant.
What’s likely to emerge are conversational ad formats: sponsored answers, embedded product recommendations, AI agent hand-offs, and transactional prompts within dialogues. These context-aware, intent-driven experiences will redefine performance marketing, moving from click-based attribution to interaction summaries and intent graphs. Brands will need to adapt to journeys that are nonlinear, multi-agent and, often, closed-loop within a single AI interaction.
Measurement will mature alongside these formats, much as clickstream analytics gave rise to today’s attribution models. Expect intermediary platforms to simplify complexity. Just as search and social powered the last era of ad platforms, AEO and GEO will fuel the next wave.
“Brands today have to win over the AI agent before they can even reach and inspire real shoppers,” says Purva Gupta, CEO and Co-founder of Lily AI. “But the opportunity goes far beyond AEO and GEO visibility. Lasting success demands context-rich, AI-friendly product content that truly connects. The most forward-thinking brands know this and are already optimizing revenue-driving channels like Google and Meta where success also depends on clear, structured, and machine-friendly product data, starting with flawless feeds in Google Merchant Center and Meta Commerce Manager. Optimizing product content for these channels is just as critical for maximizing visibility and results.”
2. Earned Discoverability: From Keywords to Questions to Answers
Generative engines are redefining earned discovery. While traditional SEO focuses on keywords, GXO demands optimization for natural language, semantic richness, authority, and factuality. The shift is clear: from keywords, to questions, to answers, to outcomes.
Today, users search, click links, and navigate off-platform. Soon, answer engines will fulfill missions within the interface itself — delivering reviews, pricing, ratings, social proof, and purchase options all within the chat. Generative engines are evolving into decision engines: informing, validating, and closing the loop from inquiry to acquisition.
For brands, this means designing content not just for humans but for bots and agents. Success will rely on creating structured, machine-readable content that retrieval-augmented generation (RAG) systems can ingest. This includes enabling crawler access, aligning with retrieval protocols, and possibly producing modular content at scale — like high-frequency trading for brand narratives. Content operations will become continuous, scalable, and optimized not for clicks, but for AI-driven answers.
As Andrew Yan, CEO of GXO startup AthenaHQ, puts it, “GenAI Search parses an order of magnitude more sources than humans. While a person might stop after three sources, GenAI can integrate insights from over 30 — a number that will only grow. Brands must track and optimize across a far broader digital landscape.”
3. Agent and Feed Ecosystems: The Rise of the Omnipresent Brand
Beyond paid and earned visibility lies a third frontier: agent ecosystems and personalized feeds. Discovery is shifting from one-off search moments to a persistent, omnichannel presence informed by a user’s digital life.
For younger consumers, discovery often starts in social feeds — a TikTok video sparks a question to their AI agent, which delivers a synthesized recommendation, followed by a seamless purchase. In this world, social visibility seeds intent, AI agents drive recommendation, and commerce completes within or through the agent interface. The future will likely feature agent-to-agent commerce, where consumer agents interact directly with brand agents to negotiate, recommend, or transact.
This requires brands to create AI-friendly, context-rich product data and to deploy autonomous brand agents that can interact, represent, and transact. The most forward-thinking companies are already optimizing structured product feeds in Google Merchant Center, Meta Commerce Manager, and emerging AI shopping surfaces.
However, agentic commerce also introduces new challenges: interoperability standards, identity verification, and consent-driven data sharing. Success will require brands to align with emerging protocols for agent interactions, respect user data sovereignty, and design experiences for conversational commerce flows — including AI-driven checkout and seamless handoffs.
This marks a full platform shift: discoverability will depend on how effectively a brand’s content — and its autonomous agents — can be found, trusted, and understood by both AI systems and human users.
How to Hop a Ride on the Bullet Train
This shift isn’t a distant future. For some brands, referral traffic from generative platforms like ChatGPT is already experiencing double-digit growth — per month. AI platforms are moving quickly to integrate shopping, reviews, and transactions. Together, these two trends alone signal one of the most profound changes in digital marketing history.
If you’re a brand, agency, or platform leader, start by auditing your AI visibility today. Prepare your content for retrieval, not just ranking. Understand how social channels influence AI intent, how agents might close the sale, and what paid formats are emerging in conversational ecosystems. Most importantly, rethink what discoverability means in a world where users no longer browse: They ask.
Success in this new era means optimizing for retrieval, not just ranking. It’s about being present in vector spaces, answer engines, and agent ecosystems with trustworthy, structured, and conversationally relevant content that can be surfaced, shared, and acted upon by both humans and machines.
Building this capability will require more than just a few tactical adjustments. It will demand a deliberate investment in team structure, operating model, workflows, and measurement frameworks tailored to the generative era. Brands must assemble cross-functional teams that blend content creation, data science, AI engineering, and performance marketing expertise. Workflows will need to support continuous content production, structured data optimization, and seamless collaboration between human and AI agents. Operating models must shift from campaign-based execution to always-on content and agent management. And measurement frameworks will need to evolve from clickstream and conversion tracking to intent graphs, AI visibility audits, and multi-agent attribution models. Simply put, to compete in the new world of generative discoverability, organizations must build both the structural and the operational muscle they will need to play — and win — in this emerging landscape.
