Beyond GEO: How AI Agents Will Change Brand Discovery, Marketing and Digital Experiences

For the last few years, much of the conversation around AI and marketing has focused on content. First came the impact of generative AI on producing content. Then came a new question: what happens when people stop searching through ten blue links and start asking ChatGPT, Gemini or another AI assistant for an answer?

That shift gave rise to concepts such as Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Companies began asking a new kind of question.

That question is still important. But developments announced at OpenAI DevDay 2026 point toward something potentially much bigger. The next generation of AI systems will increasingly do more than answer questions. They will take action.

OpenAI introduced Dots, persistent AI agents designed to work on behalf of users, connected to applications and equipped with their own cloud computer and browser. The company demonstrated agents monitoring information, working inside software, modifying code, communicating through messaging tools and completing tasks with relatively little user involvement.

This suggests a significant change. We may be moving from an Internet designed primarily for humans navigating websites to one where humans express an intention and AI navigates the digital world on their behalf. And that changes the marketing question considerably.

From search to answers to actions

For most of the Internet era, a consumer searching for a restaurant, hotel, insurance provider or professional service would begin with a search engine. Google identified relevant destinations, but the user still did most of the work: opening websites, comparing alternatives, completing forms, calling businesses, making reservations or purchasing products.

Generative AI changed the first part of that journey. Instead of presenting a list of pages, an assistant can evaluate available information and synthesize an answer. Ask for the best family-friendly hotels near Montreal, for example, and it may deliver a shortlist without ten site visits. That is the world GEO and AEO are beginning to address.

The AI is no longer simply influencing discovery. It is participating in the transaction. That transition is fundamental.

The next marketing problem may be selection

One of the most interesting observations in Greg Eisenberg's analysis of DevDay is that a company could eventually be, in his words, “hired before the customer knows your name.” A user expresses a need, the AI interprets the job and then chooses the tool, service or application capable of accomplishing it.

Today, businesses compete for rankings. Increasingly, they also compete for citations and recommendations inside AI-generated answers. Tomorrow, some businesses may compete for something more consequential: being selected to execute the task.

Visibility still matters because an agent needs information before it can evaluate a company. But visibility alone may no longer be enough. A business could be well represented in AI answers and still lose the transaction if another provider is easier for an agent to understand, access and interact with.

What does an agent-ready business look like?

Most websites were designed around a simple assumption: a human being is looking at the screen. Navigation, calls to action, landing pages, menus and conversion funnels all follow from that assumption. AI agents introduce a second audience.

Machines do not necessarily need the same interface humans do. They need clear information, reliable structure, defined capabilities and ways to take action. A hotel needs accurate information about rooms, policies, pricing and availability in forms AI systems can understand. If agents are expected to complete reservations, they also need a reliable mechanism to do it.

The same applies across industries. An insurance company might expose quoting capabilities; a restaurant, reservations and menus; an automotive company, inventory and service availability; and a professional-services business, scheduling and qualification workflows.

OpenAI's new Agents API makes this direction particularly clear. Agents can operate through tools and APIs, but they can also use hosted browsers to interact with existing websites and software. This does not mean every business must suddenly rebuild itself as an API. It does mean companies should ask whether their digital infrastructure is sufficiently structured and accessible for humans and automated systems.

GEO is not disappearing. It is becoming infrastructure.

Before an agent can select a business, it needs to understand it. What does the company do? Where does it operate? What does it cost? Who is it for? What evidence shows it is trustworthy? What distinguishes it? What policies, limitations and conditions apply?

The answers come from websites, structured data, reputable third-party sources, reviews, documentation, product feeds, APIs and other digital assets. AI discoverability becomes the foundation for AI selection.

The optimization target expands. It is no longer enough to ask whether ChatGPT mentions the brand.

The analytics layer will change too

The web created an entire measurement ecosystem around traffic and behavior. Google Analytics, Mixpanel, Amplitude and many others helped companies understand where users came from and what they did next. GEO platforms are now beginning to measure visibility inside AI responses.

If autonomous agents become an important intermediary between businesses and customers, companies will need another category of measurement: which services agents select for different intentions, whether the chosen tool completes the task and which descriptions or technical configurations improve selection.

The objective is no longer simply getting someone to the website. It may sometimes be enabling the transaction without the customer visiting the website at all.

The interface itself may start disappearing

In its DevDay demonstrations, OpenAI repeatedly showed AI operating outside the traditional chat interface. Dots could work through connected applications, collaborate inside ChatGPT Space, receive delegated work from Slack and communicate through messaging and voice. In one demonstration, an agent picked up a reported software issue, investigated it and opened a proposed fix.

AI may increasingly become embedded in workflows rather than visited as a destination. Marketing teams could delegate campaign monitoring; customer-service teams, triage; sales organizations, research and lead qualification; operations teams, repetitive reconciliation; and management teams, continuous monitoring that surfaces exceptions.

The important distinction is between automation and delegation. Traditional automation says, “when X happens, do Y.” An agentic system can understand the situation, determine what should happen, use available tools, take permitted actions and escalate when human judgment is required.

An invoice arrives. Inventory drops. A customer submits a request. A prospect completes a form. A campaign begins underperforming. The system interprets the situation, takes an action and receives the result as feedback.

OpenAI's Decisions API, announced in limited preview, is aimed at part of this architecture. But the business opportunity is not the API itself. It is learning to identify these loops inside a company. There are hundreds of them, and many currently live across email, spreadsheets, WhatsApp messages, CRM updates and people manually moving information between systems.

The value moves from the model to the workflow

For the last few years, much of the perceived differentiation between AI products came from the model itself. That advantage is becoming harder to sustain as frontier models improve, costs fall and infrastructure becomes easier to access.

Differentiation moves toward what competitors cannot obtain by simply calling the same model: proprietary information, specialized workflows, access to external systems, collaboration, auditability, networks of people or suppliers and measurable business outcomes. Eisenberg describes this as selling workflows rather than tokens.

The model is only one component. The workflow becomes the product.

Smaller, more specialized software becomes possible

OpenAI also introduced Sign in with ChatGPT. Eligible integrations can request permission for some users to use their existing plan for certain AI requests. That can reduce friction when trying AI-enabled applications, although it remains an emerging capability with eligibility and rollout restrictions.

The strategic direction is interesting: an AI application does not need to become a massive horizontal platform. It can solve one narrow but valuable workflow exceptionally well - cleaning a particular catalog, analyzing one kind of contract, preparing a specific report, coordinating a recurring marketing process or validating a category of document.

For businesses in markets such as Costa Rica and Latin America, that could be particularly relevant. The opportunity is not always creating the next global software platform. Thousands of operational problems are too specific for the largest technology companies to solve individually, but valuable enough for an industry, company or regional market to pay to solve well.

Distribution is becoming part of the AI platform

OpenAI also announced an enterprise marketplace allowing qualifying customers to apply part of existing OpenAI commitments toward participating partner products. It matters because AI platforms are becoming ecosystems: identity, agents, applications, plugins, marketplaces and distribution begin to converge.

We have seen similar transitions with mobile app stores, social platforms and search engines. Whenever a new platform becomes an intermediary between businesses and customers, organizations eventually need to understand how to participate. The rules are still being written. That is precisely why businesses should start paying attention now.

The website is not dead. Its role is changing.

AI will not eliminate websites. Humans will continue visiting them to explore, compare, understand, build trust and experience brands. For many purchases, the emotional and visual experience remains essential.

But a website may increasingly serve two audiences simultaneously.

Those objectives are not contradictory. In many cases, improving one improves the other. A confusing website is usually confusing because the underlying business information is confusing too.

The bigger transition

The most important message is not that every company suddenly needs an AI agent. It is that the relationship between people, software and businesses is beginning to change.

For marketing and digital teams, the next few years will involve much more than producing content with AI. They will involve redesigning how customers discover businesses, how digital experiences expose information, how software interacts with organizations and how work gets executed behind the scenes.

GEO is part of that future, but it may only be the beginning.