Recent Visa research shows businesses are already preparing for this shift. Seventy-one percent of businesses say they are willing to optimize products and offers specifically for AI agents, and more than half say they would allow AI agents to negotiate prices or terms directly with other AI systems. And there are early signs that AI search traffic converts at remarkably high levels, jumpstarting traditional acquisition funnels.

This leap to B2AI will rewrite much of what we know about customer acquisition. You won’t just be influencing people anymore. Increasingly, you will need to convince the AI systems that inform their decisions.

For all intents and purposes, you should treat AI agents as thought-partners to the customer. And the core tenets of how to approach any thought-partner still apply, as long as you transcreate them to agentic structure: Meet the partner where they are; educate them about your point of view; mesh with their movements; build trust then convenience; ensure you’re clear about your purpose.

1. Structure your data so machines can find — and trust — you.

AI agents rely on structured signals, not just marketing copy. Product specifications, pricing, availability, and attributes should be organized so machines can easily evaluate and compare options.

That means investing in structured product catalogs, consistent metadata, and standardized schemas that allow AI systems to interpret your offering accurately. If AI agents cannot clearly understand your product, they are unlikely to recommend it.

Consider this a form of machine-readable shelf presence: just as a consumer-packaged goods brand invests in packaging legibility and planogram placement in a physical store, your brand now needs equivalent legibility inside the data environments AI agents browse. The format has changed; the principle hasn’t.

2. Become a knowledge source, not just a product.

AI models don’t peruse, they ingest. As models evolve from retrieval tools into reasoning engines, the question is no longer whether an AI can find your product page. It’s whether it can think with your brand’s data. Product knowledge, FAQs, documentation, and brand facts should be structured so AI systems can easily parse, interpret, and reference them — and not just as lookup sources, but as reference material the agent can reason from when building a recommendation.

Think of it as building a library. A consumer reviewing your website needs to be persuaded. An AI agent ingesting your knowledge base needs to be informed. These are different design problems, and they require different investments, including structured knowledge graphs, well-tagged documentation, and brand facts organized for machine interpretation rather than human scanning.

3. Build for machine execution, not human navigation.

Infrastructure certainty is non-negotiable: agents don’t automatically disqualify you for missing features — they disqualify you for missing data.

AI agents are acutely sensitive to operational uncertainty in ways humans aren’t. Inconsistent inventory signals, ambiguous pricing, or missing delivery windows don’t frustrate an agent — they simply cause it to default to a competitor whose data it can execute against cleanly. Agents don’t tolerate ambiguity.

That means companies need to expose live pricing and availability through structured interfaces like model context protocols (MCPs) so AI systems can retrieve accurate, real-time data and complete transactions reliably. This isn’t a feature launch — it’s an OS rebuild for machine-to-machine interaction.

4. Trust is as important as convenience.

In an AI world growing increasingly scarce of trust, established companies and editorial brands become even more important. You must build verifiable trust signals into your platform.

As AI agents evaluate options, they will rely on signals that indicate credibility and reliability. Consistent brand data, transparent policies, secure payment infrastructure, and authoritative sources all influence whether an AI system recommends your product or moves on.

Trust will increasingly function as a ranking signal in AI-mediated commerce. Brands that have built genuine trust signals — third-party reviews, consistent data across platforms, authoritative sourcing — will be harder to displace than offerings that have merely optimized for visibility. Ultimately, trust becomes the infrastructure.

5. Purpose matters.

Brand purpose has always mattered to consumers. Now it matters to their AI agents, too.

AI systems don’t just retrieve information — they evaluate it. And the signals they weigh go beyond structured data and pricing. Increasingly, AI reasoning engines assess quality, coherence, and authenticity when deciding which brands to recommend. A brand with a clearly articulated purpose woven through its content, policies, customer interactions, and sourcing practices gives an AI agent richer, more coherent material to reason from. A brand without that architecture looks thin by comparison: technically present, but difficult for an agent to build a confident case around.

Think of it this way: when an AI agent evaluates two competing products — one from a brand with deep, consistent storytelling about why it exists and who it serves, and another from a brand optimized purely for volume and discoverability — the purpose-driven brand gives the agent more to work with. Its claims are substantiated across touchpoints. Its content has texture and specificity. Its reviews reflect a relationship with customers, not just transactions. The agent isn’t making a moral judgment; it’s making a quality assessment. And purpose, expressed as architectural consistency, reads as quality.

This means the work of brand purpose isn’t separate from B2AI strategy — it is B2AI strategy. Because even in a machine-mediated marketplace, meaning still matters. And only humans can find the deeper meanings that connect us.

English majors, rejoice.

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