In agency marketing, the product changes from perceived profit to guaranteed profit. Customers may choose brands based on emotion or identity, but their AI agents will evaluate those brands using measurable signals such as price transparency, fulfillment reliability, reviews, loyalty value, privacy practices, and service history.
This changes the way reliability is evaluated, and means that the first logical audience may be the software that runs on the customer’s authority.
Buyers are already outsourcing parts of the buying process to software. About 70% of consumers and 73% of B2B buyers use AI tools to evaluate purchases. At the same time, Bain predicts 25% of US ecommerce, or between $300 billion and $500 billion, will be driven by agent AI by 2030.
A brand needs to be machine-readable to agents while still connecting emotionally with consumers. Although the consumer may have good memories of the past or exposure to the product’s advertising, his agent needs to evaluate the price, availability, reviews, return policies, reliability value, delivery performance, privacy policies, and service history, with little consideration of the emotional aspects of the product.
In five years, a brand’s ability to deliver value on its product promise will be more important than its most compelling advertising.
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Brand trust becomes evidence-based
For a brand to mean anything, it has to mean trust. Customers choose generic brands because of emotional feedback that those brands will reduce risk, deliver a recognized level of quality, and make the purchase decision easier. In agency marketing, the same is true, but it’s more formal, evidence-based, and less forgiving.
A customer may choose a brand for any number of reasons (eg, emotion, identity, values, past experience, or habit), but the customer agent will evaluate that brand for strong signals. As a result, the product promise must be proven with performance.
Agent AI requires a high level, so the product cannot:
- Claim usability while displaying incorrect inventory.
- Ensure customer centricity while hiding cancellation policies.
- Increase premium service while making returns painful.
The customer and the customer’s agent may not agree. An agent may prioritize a customer’s preferred brand due to price fluctuations, poor fulfillment performance, or unclear return policies. That creates a disconnect between consumer preferences and algorithmic recommendation.
For example, when a customer asks an AI agent to evaluate telecom renewal options, the agent compares current contract terms, payment history variations, network performance data, customer service complaint rates, and competitor offerings. It ignores the customer’s personal interactions with the company, whether good or bad.
A brand can spend a lot of money on advertising, but if it has inconsistent payment accuracy or unclear contract terms, it may be outsourced before advertising maintenance has a chance to intervene. Agents’ dilemmas and the potentially binary need to get a “yes” or “no” answer to certain questions mean that brands must reevaluate how they write and communicate.
Product preferences as interpreted by agents
The first moment of brand influence is from the homepage, search ad, product page, email, or retail shelf. Now, buyer agents interact with brand systems, marketplaces, review sites, response engines, commerce systems, loyalty databases, and fulfillment data before a customer sees a recommendation.
This creates a new layer of caution, meaning that species must be understood by systems before humans can consider them. Pricing, promotion, and product attributes must be accurate, accessible, and machine-readable. Traditional SEO is still important, but now it’s part of a broader agent visibility strategy that requires collaboration across product information, order management, customer service, loyalty, and other applications.
For example, a customer asks their AI agent to find a washing machine under $900, available within five days, with reliable service, and an easy return process. If a customer agent encounters incomplete product data, unclear delivery windows, missing warranty information, or weak service ratings, it will turn off brands the buyer might consider.
Machine readability is important, but a complex and complex challenge creates confidence in an agent to recommend a product based on solid and consistent data, policies, and evidence of performance. In this area, visibility is the first step, while accurate translation can determine sales.
Integrity must be agent-accountability
For better or worse, immeasurable loyalty is not assumed in the agent’s workflow. Thus, if an agent cannot estimate the number of points, status, value of service, or accrued benefits in real time, those benefits are not necessarily present in the decision model.
Most loyalty programs are designed for human interaction, based on apps, emails, points, categories, rewards, member rates, and promotional moves. That model still works, but in commerce, the trust value must be readable and applicable to the agent.
The agent must be able to understand the status of the category, available rewards, subscription benefits, warranty coverage, return rights, and renewal options. Loyalty needs to be part of the overall value, not stuck in an app notification that the customer may never open.
Brands need to show the customer agent why loyalty brings a better result. If a competitor has a lower price but the customer’s existing product relationship includes better service, faster delivery, unused rewards, or stronger warranty protection, the agent needs to know that.
Membership alone is not sufficient if the agent cannot see, calculate, or use the benefit.
Customer data becomes the shipping layer
While agency interactions will undoubtedly increase, people will still be making more purchases and interactions for the foreseeable future. Thus, customer data needs to support both personalization (in human interaction) and delegated decision making (in agency interaction).
For years, brands have used customer data to determine what message, offer, channel, or experience to launch next. Now, that data also needs to help the agent understand what the customer has approved, preferred, declined, purchased, acquired, or requested.
This changes the role of the customer profile. No more product-side view of segments, trends, and campaign relevance. It must also reflect the permissions and preferences the customer may want the agent to enforce, including price sensitivity, product preferences, durability expectations, privacy restrictions, accessibility requirements, reliability value, service requirements, and risk tolerance.
Consent becomes more important because agents may request access, compare options, initiate transactions, or act on the customer’s behalf. Ownership adjustments are also becoming more difficult. Products will need to distinguish between a human customer, a family member, a business account, an authorized agent, and another intermediary.
Supporting authorization and identity resolution at agent scale is an enterprise-wide task. Marketing must own the integrity of the customer promise and ensure that the business can prove it with data, permissions, and information, without needing to own all the systems.
Equilibrium to move upstream
It’s important to understand how well agents understand and recommend your product before lost revenue is at risk. Traditional measures of website traffic, search rate, media engagement, and last click attribution are no longer perfect measures of brand influence. Instead, the buyer’s agent may research, compare, filter, and reject options outside of branded areas.
By the time conversion rates drop, the product may no longer be in the consideration sets generated by the agent. As incomes decline, a shift in preferences may be underway.
A key marketing metric should first be whether agents can find and understand the brand. Then, critically, the ability to interact with the product throughout the journey needs to be measured.
We are moving from measuring only visual human interaction to measuring machine perception.
The product promise needs to be verified
Brand loyalty will not disappear because agents enter the buying process. People will still value meaning, identity, experience, confidence, and emotional connection. But those signals will increasingly be filtered by agents who evaluate evidence, policies, data, and outcomes before making or recommending a decision.
This makes it more important than ever to have a product strategy closely aligned with operations. The promise made in marketing should be reflected in product data, pricing, fulfillment, loyalty benefits, service discovery, privacy practices, and consent management.
The product cannot simply be trusted. It should create trust.
In the agency era, the strongest brands will be the ones people want to trust, and agents can recommend with confidence.
