How to prepare your brand and products to be recommended by AI

For years, digital growth had a clear logic: buy clicks, measure conversion, optimize acquisition cost. Brand was top of funnel; performance was bottom. Companies that dominated both in parallel silos did well. I know how that game worked because I did SEM early in my career. And I know it has changed — because search itself has changed.

By January 2026, the volume of searches with more than four terms had grown 15-fold. In Google's AI Mode, users run queries two to three times longer than traditional ones. Search has become conversation, and whoever answers best wins.

There is a growing asymmetry between human search and agent-mediated search. Humans get tired, compare three or four options, choose based on what's most visible. Agents process dozens of sources in seconds, weigh reputation, cross-reference contract terms, and return a recommendation.

If a human were shopping for a digital camera, they might visit five websites. Their agent will visit a thousand times more. It could hit five thousand sites.

That's a thousand times more traffic to answer the same question. In 2024, for the first time in a decade, machines surpassed humans: 51% of global internet traffic is now bots, according to Imperva's annual report. Today's internet is being built for machines, and data structures need to be ready for machines too.

Consumers are migrating more and more decisions to the conversational interface. Recent McKinsey research shows that financial institutions that mastered optimization for these models — a new discipline called Generative Engine Optimization, or GEO — saw up to sixfold growth in unpaid organic traffic.

Google itself has publicly acknowledged what this shift implies.

Brand and Performance have never been so close.

It is the polite way of saying something more uncomfortable: brands without substance cease to exist for AI. And as more customers reach purchase decisions mediated by AI, brands without substance cease to exist, period.

The right question is no longer "does my brand have recognition?". It is "does my brand have material that an AI can read, attribute, and recommend with confidence?". Google's practical recommendation organizes around three pillars: dense proprietary content, actively built external authority, and active reputation management.

Pillar 1: Structured proprietary content

Models need information that explains what your company does, for whom, under what conditions. For a bank or fintech, that means clear product pages with objective eligibility criteria, transparent pricing tiers, use cases explained in plain language. Google emphasizes that multimodal content — video, image, in-depth text — carries more weight than short generic copy. It is usable documentation, written to answer real customer questions.

Actionable: map your brand's proprietary content and evaluate it through the lens of AI usefulness. Vague institutional pages don't serve. What serves are pages that directly answer "who is this for", "how much does it cost", "how does it work in three months". In multimodal format where possible.

Pillar 2: Verifiable external authority

Language models infer trust from the network of sources that talk about a brand. Google recommends investing in blogs, opinion pieces, podcasts, and webinars that position the brand as an authority. Customer reviews, collaborations with specialized creators, and mentions in reputable outlets become fuel. In financial services specifically, the brand that produces public analysis about its own market has a double advantage: it appears in more AI results and gains organic credibility that paid media cannot buy.

Actionable: identify three types of external production your brand can sustain with quality — not ten. It could be regular appearances on industry podcasts, an owned blog with consistent cadence, or ongoing partnerships with relevant creators. Volume matters less than consistency and density.

Pillar 3: Active reputation management

Google literally cites Reclame Aqui and social networks as monitoring points. Tracking what is being said about your brand across platforms has stopped being a reactive communications function and become essential practice for preserving organic visibility in AI.

Here is the point most marketing committees haven't internalized yet: customer experience became an acquisition lever. The logic is direct. If a customer complains publicly about your bank, that complaint becomes indexable text. If AI trains on that text, it learns to associate your brand with specific problems. When another potential customer asks "which is the best bank for X," the model weighs observed reputation.

This reorganizes unit economics math. Before, a poorly served customer cost the lost LTV of that customer, plus negative word-of-mouth in a small network. Now a third cost is added: degraded organic visibility in AI for new potential customers. The CAC of the next customer goes up because the previous one's experience was bad.

And bad experience rarely starts at the support desk. It starts in the product that doesn't work as promised, in the journey with unnecessary friction, in the internal process that bounces the customer's problem from area to area. Customer support is where the customer arrives after something has already failed. Sizing the support team well is necessary, but treating the symptom without addressing the cause only delays the problem — and in the AI era, it delays it visibly, because every bad interaction becomes public data that teaches the model what your brand represents.

For financial services, this is especially urgent. Banks and fintechs are among the most complained-about companies in Brazil by construction: high transaction volume, emotionally charged situations, regulatorily dense journeys. The argument that "investing in customer experience has no clear ROI" has lost validity. The ROI now lives in two places simultaneously: in retaining the current customer, and in organic positioning with AI models.

Actionable: look at customer experience as a system, not as a function. A well-sized support desk matters, but it is the last layer. The previous layers — product, journey, internal process — are where most public complaints originate. In financial services, mapping the five most frequent causes of complaint on Reclame Aqui and addressing them at the root is probably the growth investment with the highest marginal return over the next six months. And possibly the only one whose return shows up in two P&L lines at once.

The cultural shift

The change this requires is cultural, not tactical. Companies used to buying clicks had a luxury: the acquisition channel was separate from the relationship channel. You could have excellent paid media and mediocre service, and the math worked in the short term.

That divorce is over. Brand, performance, content, and service have become four faces of the same lever: how well your company is understood, cited, and recommended by language models that increasingly mediate purchase decisions. For financial services, where trust is the product and reputation is part of what's sold, this alignment is digital infrastructure finally reflecting what was always true.

Preparing brand and products to be recommended by AI is the new work of those who care for growth, brand, and relationship at the same time.