Agentic Commerce Is Redefining the Chief Marketing Officer

As artificial intelligence becomes an active participant in product discovery, evaluation and purchasing, marketing will no longer be responsible only for influencing people. It will also have to earn the confidence of the machines advising them.

Category: Strategy. Written by Jaime Garcia, Founder, SnowRock. Published July 7, 2026. 20 min read.

In short

• Agentic commerce inserts an AI evaluator between company and customer. Marketing now has two audiences, the person with the need and the machine advising them, and the brand must satisfy both to be recommended.
• Machine evaluation collapses the old split between promise and performance. An agent can inspect the gap between what a company claims and what customers experience, so brand stewardship becomes brand accountability.
• The C.M.O. mandate expands from communications to enterprise strategy: make the company legible and provable to agents, turn customer intent into product and operating decisions, and design for both human persuasion and machine proof.

For most of modern marketing, the customer was assumed to be human. Companies competed for attention, shaped perceptions, simplified choices and designed messages meant to move people from awareness to purchase. Search engines, social platforms and e-commerce marketplaces changed where those interactions happened, but not the underlying premise: a person encountered the brand, a person interpreted the message, a person made the decision.

Artificial intelligence is beginning to alter that sequence. Consumers increasingly use AI systems to identify products, compare alternatives, investigate claims, negotiate trade-offs and complete transactions, and in many cases the system does not merely retrieve information. It interprets the customer goals and recommends what should happen next. This is the beginning of agentic commerce, in which AI systems become active participants in the commercial relationship. They stand between the company and the customer, examining evidence, comparing offers and influencing which brands are considered credible. The customer remains economically important. The agent becomes strategically important.

Companies must now persuade two kinds of decision-maker: the person with the need and the machine helping that person decide how to meet it. This shift will make the chief marketing officer role more consequential than it has been in decades. The C.M.O. will no longer be responsible primarily for communications, campaigns or demand generation. The function will increasingly sit at the intersection of brand truth, customer intelligence, operational performance and machine-mediated distribution, expected to answer three questions. What does the company promise? Can it prove that it consistently delivers? And will both people and AI systems recognize the difference?

Agentic commerce moves from search intermediaries to decision agents

Digital platforms have mediated commerce for years. Search engines ranked websites, marketplaces ordered products, social platforms selected which messages users encountered, and recommendation systems predicted what someone might watch, read or buy. Agentic systems go further. They interpret intention. A consumer may ask an agent to find the most reliable vehicle for a family of five, plan an anniversary trip within a fixed budget or identify a financial product appropriate for retirement. The request contains more than a keyword. It contains context, priorities, constraints and trade-offs, and the agent can examine products across several categories, compare evidence and return a recommendation tailored to the desired outcome.

This changes the competitive environment. A hotel is no longer competing only with other hotels. It may be evaluated alongside vacation rentals, cruises or entirely different experiences capable of satisfying the customer broader objective. A utility provider may be compared not only with another provider but with energy-efficiency products, solar installation, financing or changes in consumption. A bank account may be assessed as one component of a larger financial objective involving debt, education, retirement and household cash flow. Traditional market categories become less stable when the customer begins with an outcome rather than a product. The agent organizes the available market around the need.

The emergence of a second audience

Brands have traditionally designed information for human interpretation. A person responds to narrative, imagery, emotion, social proof and familiarity. A machine may evaluate many of the same signals, but it does so differently. An AI system can examine prices, reviews, availability, service outcomes, complaint records, warranty terms and product specifications across thousands of sources, compare what a company claims with what customers report, and identify patterns no individual customer would have the time or ability to find. This creates a second audience for marketing.

The human audience asks whether the brand feels relevant, whether it can be trusted and whether it represents what the customer values. The machine audience asks whether the claim is supported, how the offer compares, whether performance justifies the recommendation and whether the information can be verified. The strongest brands satisfy both. A compelling emotional promise may attract the customer, while observable evidence determines whether the agent endorses it. Marketing therefore becomes partly an exercise in machine legibility: product information must be structured, evidence must be accessible, claims must be consistent across channels, and operational performance must support the brand position. Visibility alone will not be enough. The brand must become recommendable.

Brand stewardship becomes brand accountability

The traditional brand model separated promise from performance. Marketing defined the story, while product, operations, service, distribution and finance determined how the company actually delivered. The distinction was never entirely sustainable, and it becomes even less viable in an agentic market, because AI systems can evaluate the full distance between what a brand says and what customers experience. A financial-services company may position itself around trust and simplicity while an agent finds persistent complaints about rejected claims or slow resolution. A retailer may promote convenience while showing frequent stock shortages and difficult returns. A hospitality company may sell a premium experience while receiving inconsistent service reviews across locations. The machine does not need to accept the official narrative. It can inspect the evidence.

This raises the standard for brand stewardship. The C.M.O. can no longer treat operational inconsistency as another department problem when that inconsistency directly affects visibility, recommendations and customer acquisition. Brand management must evolve from narrative control into promise-performance alignment. The brand promise defines the value the company intends to provide, and brand performance shows whether that value appears reliably in products, pricing, service, fulfillment and resolution. The more closely those two elements align, the more credible the company becomes to both people and agents.

The brand claim must be defensible

Many brand positions are broad enough to be functionally meaningless. Companies describe themselves as innovative, customer-centered, trustworthy, premium or convenient without establishing how those qualities are demonstrated, and AI systems may punish this vagueness. A model comparing several options looks for evidence of differentiation, and generic claims provide little basis for recommendation. The company must define a position that is both meaningful and supportable, one that answers what specific outcome it delivers, for whom it is superior, under which conditions, with what observable evidence, and where it outperforms relevant alternatives.

This makes brand strategy more rigorous. A company cannot credibly promise the fastest service when public data show frequent delays, or claim premium quality when return rates and complaints suggest inconsistency. The strongest promise may be narrower than the most ambitious one. It will also be more defensible. Artificial intelligence may therefore improve branding by forcing companies to distinguish between aspiration and demonstrated advantage.

Marketing must gain operational authority

If customer experience determines machine recommendations, marketing cannot remain isolated from the functions that create that experience. Pricing, product availability, fulfillment reliability, complaint resolution and service quality all become part of brand performance, and the C.M.O. will need greater influence over those systems. This does not mean marketing should control operations. It means the organization must create shared accountability around customer promises, so that a claim made in a campaign has a corresponding operational measure. A promise of fast delivery should connect to actual delivery performance, a promise of simplicity should be reflected in onboarding, billing and support, and a promise of trust should appear in pricing transparency, complaint handling and data protection.

Marketing can bring these signals together because it sits closest to the market perception of the company. The future C.M.O. will need to identify promise-performance gaps, quantify their commercial effect and work across functions to close them. This is brand stewardship with operational consequences.

Customer intent becomes a strategic data asset

The second major change concerns customer intelligence. Traditional marketing data reveal what people clicked, viewed, purchased or searched, but often provide limited insight into why. A keyword may indicate interest in a category while saying little about the larger goal driving the search. Agentic interactions are richer. A customer may explain that a trip is for an anniversary, that dietary preferences matter and that the schedule must accommodate limited mobility. A household may ask for lower utility costs because college expenses are rising and retirement is approaching. A business owner may seek accounting software because the company is expanding internationally and the current system cannot manage multiple currencies.

These prompts reveal motivation, context and constraint. They show what customers are trying to achieve, not merely which product they are considering, which is a fundamentally more valuable class of intelligence. It exposes the job the customer is trying to accomplish.

Product categories begin to dissolve

Traditional market analysis often begins with products: a company defines a category, estimates its size, identifies competitors and segments customers within it. Agentic intelligence begins with desired outcomes, and from the customer perspective, products in different categories may be substitutes or complements if they contribute to the same goal. A customer trying to reduce household expenses may consider a new utility plan, a smart thermostat, an insulation service, solar installation or a financing product. These may be studied by different corporate teams and categorized as separate markets. The agent sees them as components of one objective.

This reveals forms of competition that conventional category analysis misses, and it reveals opportunities for combination. Companies may create bundles, partnerships or ecosystems built around outcomes rather than individual products. A travel provider could combine transportation, lodging, dining and event access around a particular type of trip. A financial institution could integrate savings, education planning, insurance and household budgeting around a life-stage objective. A healthcare company could combine diagnostics, medication access, scheduling and monitoring around a patient outcome. Marketing intelligence becomes a source of business-model design.

Marketing moves upstream

Marketing has often entered the process after a product was substantially defined, studying the market, determining positioning, selecting audiences and developing the go-to-market plan. Agentic customer data can move marketing earlier. When organizations can see the goals, frustrations and trade-offs expressed directly by customers, those signals can influence what the company builds. Marketing can identify unmet needs before they appear in sales data, reveal when customers are combining products manually because no company offers the complete solution, and show when operational limits are preventing an otherwise attractive offer from succeeding.

This intelligence can inform product development, service design, bundling, partnerships, pricing structures, distribution and new business models. The C.M.O. becomes not only the steward of how the company enters the market but a contributor to what the company brings into the market.

The failure is usually not a lack of data

Most large companies already possess substantial customer information: transaction histories, support records, survey responses, loyalty data, behavioral signals and market research. The problem is often organizational. Data remain divided across systems, teams interpret customers through different metrics, insights arrive after major decisions have been made, and incentives reward local performance rather than customer outcomes. The company does not lack information. It lacks the operating structure required to convert information into action.

Agentic data will not automatically solve this. It may worsen it by generating more signals than the organization can absorb. The C.M.O. opportunity is to create an intelligence-to-action system in which customer intent is synthesized, distributed to relevant decision-makers and connected to concrete choices. A useful insight should have a destination: a product decision, an operational change, a commercial experiment or a resource allocation. Without that connection, advanced customer intelligence becomes another unused dashboard.

The customer can finally enter the strategy room

Companies frequently claim to place the customer at the center of their strategy, but in practice the customer is often represented through periodic research, historical sales and generalized personas. Agentic interactions create the possibility of a more continuous representation, letting the organization observe how customer needs change, which trade-offs matter and where existing offers fail, aggregated without reducing everything to broad demographic segments. The customer role in strategy becomes more direct.

This does not mean every stated preference should determine the company direction. Customers may contradict themselves, misunderstand available solutions or prioritize short-term convenience over long-term value. The significance lies in immediacy. Executives can gain a regularly updated view of what customers are attempting to accomplish, and marketing can become the institutional mechanism through which that view reaches the rest of the company.

The attention economy gives way to the evaluation economy

For two decades, digital marketing was organized around attention. Companies competed to appear in search results, interrupt media consumption, sustain engagement and move users through carefully designed journeys, on the assumption that human attention was scarce and marketing existed to capture it. Agentic commerce introduces another scarce resource: machine endorsement. An AI system does not become distracted the way a person does. It can compare hundreds of offers without fatigue and may be less influenced by visual prominence, repetition or conventional persuasion.

This changes the nature of competition. The objective is no longer merely to be seen. It is to survive evaluation. A company may spend heavily to attract a customer attention only to be excluded when the customer agent compares pricing, quality and service evidence. Advertising can create interest. The recommendation system can redirect the transaction. The customer journey is moving from an attention architecture toward an evaluation architecture.

Human and machine influence will coexist

The human customer will not disappear. Emotion, identity, aesthetics, trust and social meaning will continue shaping decisions, and artificial intelligence will influence different purchases to different degrees. A person may delegate the entire selection of a routine household product, yet use an agent only for research before choosing an emotionally significant purchase, and in other cases the customer and agent will decide together. This creates several distinct commercial pathways, each requiring a different marketing response.

1. Human-led decisions The customer uses AI minimally. Traditional brand, creative and experience design remain dominant.
2. Agent-assisted decisions The customer forms an initial preference while the agent verifies claims, surfaces alternatives and tests assumptions.
3. Agent-led decisions The customer delegates most of the evaluation and accepts a recommendation based on predefined criteria.
4. Agent-executed transactions The system not only recommends the product but completes the purchase, renewal or negotiation.

The company must understand where human persuasion matters, where machine evidence matters, and where the transaction depends on both.

The new marketing funnel is dual

Traditional funnels were designed around stages such as awareness, consideration, conversion and loyalty. Agentic commerce creates a parallel machine pathway. The human pathway may involve reputation, emotion, trust and preference. The machine pathway may involve retrieval, verification, comparison and recommendation. The two interact. A customer may ask an agent to evaluate several brands already known through advertising, the agent may introduce an unfamiliar option based on stronger evidence, and the customer may then investigate that recommendation through reviews or social media. The decision journey becomes a negotiation between perception and proof.

Marketing teams must design for both pathways at once, creating compelling human communication while ensuring that machine-readable evidence supports the message. This requires tighter coordination among brand, content, data, product and customer-experience teams.

Agent visibility is not agent preference

Companies are beginning to optimize content for generative systems the way they once optimized websites for search engines, creating structured product information, publishing factual content and exposing data through APIs. These actions improve visibility. They do not guarantee recommendation. An agent may find the brand easily and still conclude that a competitor is stronger. Machine visibility answers whether the company is present in the evaluation set. Machine credibility determines whether it advances.

The difference is critical. A company can manipulate keywords more easily than it can manipulate sustained evidence across customer reviews, service outcomes and independent sources. Agentic optimization will therefore be less about publishing the right phrase and more about creating a consistent evidence base. The strongest machine-marketing strategy may simply be operational excellence.

Unrequested recommendations raise the pressure

AI systems frequently provide more information than the user explicitly requests. A customer asking about one product may receive comparisons, cautions or alternative recommendations, and this behavior expands competition. The brand can lose the customer even when the customer initially asked about it by name. A person may inquire about the best version of a particular product and learn that another category better solves the underlying problem. The agent is not limited to the structure of the question. It can challenge the premise.

Companies therefore cannot rely entirely on existing awareness or customer habit. Every interaction becomes an opportunity for the system to reopen the market. Brand loyalty remains valuable, but it may be subjected to more continuous testing.

New measures of marketing influence

Traditional metrics will remain relevant but become incomplete. Impressions, clicks, conversion rates, brand awareness and share of voice describe human-facing channels. Agentic commerce requires additional measures: how frequently a brand appears in relevant AI responses, which sources models cite when describing it, how often agents recommend its products, which attributes drive those recommendations, where competitors outperform it, how its claims are summarized, and whether it is included when customers express a broader need rather than a product-specific query.

These metrics are still emerging, and they will become more important as machine-mediated influence grows. The objective is not to manipulate the model. It is to understand how the market evidence is being interpreted.

The branded-agent temptation

Many companies will consider building their own customer-facing agents, and the appeal is obvious. A proprietary agent keeps the interaction inside the brand environment, can access customer history, can recommend relevant services and can generate valuable first-party intelligence, while reducing dependence on general-purpose systems that may recommend competitors. The strategic logic is strongest where the company holds a deep, trusted relationship. A bank may understand a person financial history well enough to give relevant guidance, a healthcare organization may hold longitudinal information a general agent lacks, and a business-software provider may know the customer operations in enough detail to automate meaningful work. In these settings, proprietary context creates an advantage. For many brands, it will not.

Why customers may reject branded agents

An agent owned by the seller has an inherent conflict. The customer may reasonably assume it exists to promote the company products, which weakens trust when the decision requires comparison across providers. A hotel agent may be useful for managing a reservation but less credible when advising whether another hotel would be better. A bank agent may understand the customer finances yet still have an incentive to recommend the bank own products. General-purpose agents can position themselves as neutral intermediaries even when their own incentives are complicated.

The branded agent must provide enough proprietary value to overcome skepticism, whether from deep customer history, superior privacy, specialized expertise, service integration, exclusive data or the ability to complete tasks unavailable elsewhere. A company should not build an agent merely because the technology is available. It should build one when customers have a reason to prefer it.

Proprietary agents can disrupt existing channels

A customer-facing agent can create internal conflict. It may reduce traffic to websites, applications, branches, call centers or sales teams, recommend lower-cost products when the company historically relied on higher-margin ones, expose inconsistencies among channels, and shift control of the customer relationship away from established business units. These effects are not necessarily harmful, but they are strategically significant.

The decision to deploy an agent should be treated as an enterprise decision, not a marketing experiment. The company must determine which customer problems the agent will solve, what data it may access, which actions it may take, how recommendations will be governed, how conflicts of interest will be disclosed, and which existing channels it may displace. The C.M.O. should play a central role, because the agent becomes a direct expression of the brand.

The operating model was built for campaigns

Most marketing organizations remain structured around channels and specialist functions: paid media, social, communications, brand, content, research, customer relationship management and performance marketing operating separately. This model was designed for campaign production and distribution. Agentic commerce requires continuous intelligence and coordinated response, connecting what customers are asking, how agents describe the brand, which operational gaps affect recommendations and which market opportunities are emerging. A channel-based structure can slow that flow, because each team optimizes its own output and few are accountable for the complete customer outcome. The operating model must move from channel orchestration toward intelligence-led growth.

The intelligence-led marketing organization

An intelligence-led organization begins with customer needs rather than media channels. It continuously gathers signals from human behavior, agent interactions, service data, market performance and competitive evidence, then synthesizes and routes them toward decisions. A customer-intelligence team may identify an emerging need, product teams determine whether a new offer is required, operations assess delivery constraints, brand teams define the promise, commercial teams determine distribution, and measurement teams evaluate whether both human customers and AI systems recognize the resulting value.

This structure requires faster movement across functions and shared metrics. Marketing cannot be rewarded only for lead volume or campaign performance if the larger objective is profitable growth and recommendation credibility.

Humans and agents will share the work

The internal marketing function will also become more agentic. AI systems can conduct research, generate creative variations, analyze customer feedback, monitor competitive activity and coordinate campaign execution. The objective should not be to automate existing processes without reconsidering them, because AI provides an opportunity to redesign the function. People should retain responsibility for strategic judgment, brand meaning, ethical choices, creative direction, organizational influence and accountability, while agents take on more of the repetitive analysis, production, monitoring and coordination. The result should be a different operating model, not merely a faster version of the old one.

Productivity is the immediate gain, not the largest one

Most marketing investments in AI currently focus on efficiency, using models to draft content, produce variations, summarize research and accelerate analysis. These gains matter; they reduce cost and increase capacity. They are not the largest strategic opportunity. The greater value lies in using agentic intelligence to change how the company competes. Customer interactions with AI reveal needs that can shape new products, machine evaluation exposes operational weaknesses that affect the brand, and agent-mediated journeys create new forms of distribution and influence. A C.M.O. who uses AI only to cut production costs will improve the function. A C.M.O. who uses it to reshape the value proposition, customer intelligence and operating model can alter the enterprise.

The C.M.O. credibility problem

Chief executives have often questioned whether marketing contributes directly enough to growth, seeing it as hard to measure, overly focused on communications or disconnected from commercial performance. Agentic commerce creates an opportunity to change that perception. Marketing can become the function that explains what customers are trying to achieve, why agents recommend one brand over another, where the company promise is operationally unsupported, which adjacent markets are emerging, and how customer intelligence should influence product and enterprise strategy. The brand becomes what the company repeatedly demonstrates.

This moves marketing closer to measurable value creation. The C.M.O. gains credibility by connecting brand, customer and operational performance to revenue, retention and market growth, and the role becomes more consequential because the market becomes harder to interpret without it.

The new responsibilities of the C.M.O.

The agentic era will expand the C.M.O. mandate well beyond conventional execution, placing the role near the center of enterprise strategy.

1. Brand truth Ensure the company stated promise is supported by measurable experience.
2. Machine legibility Make product information, evidence and performance signals accessible to AI systems.
3. Customer-intent intelligence Capture what customers are trying to accomplish, including the context and trade-offs behind their decisions.
4. Enterprise translation Convert those signals into product, operating and strategic choices.
5. Dual-journey design Build experiences for both human persuasion and machine evaluation.
6. Agent governance Decide whether the company should deploy customer-facing agents and how they should behave.
7. Cross-functional accountability Align product, service, operations and communications around a shared customer promise.

The companies that will struggle

Several kinds of organization will be especially vulnerable. Companies whose brand promises are unsupported by operations will be exposed quickly. Companies dependent on information asymmetry may lose advantage as agents make comparison easier. Companies organized around rigid product categories may fail to recognize outcome-based competition. Companies with fragmented customer data may be unable to convert intent into action. Companies that build branded agents without sufficient trust may create expensive products customers avoid. And companies that treat AI only as a cost-reduction tool may miss the larger strategic change. The technology will not reward every company equally. It will magnify the difference between organizations that understand the customer and those that merely communicate at them.

The brand becomes a verifiable system

The most durable implication is that a brand will become less controllable through communications alone. It will increasingly be assembled from observable evidence: price behavior, product quality, availability, service speed, complaint resolution, customer reviews, employee conduct and independent reporting. AI systems can combine these signals continuously, so the brand becomes what the company repeatedly demonstrates. This may weaken the power of persuasive storytelling when it is unsupported, and it may strengthen companies that have delivered quietly but failed to communicate their advantage. The machine does not eliminate branding. It makes branding more accountable.

The customer relationship is being reintermediated

Digital commerce initially promised direct relationships between companies and customers. Platforms then inserted themselves between the two, as search engines, marketplaces and social networks came to control discovery. Agentic systems represent another layer of intermediation. The agent may know the customer preferences more deeply than the seller does, manage decisions across categories and maintain a continuous relationship no individual brand can match. This creates a strategic risk: a company may fulfill the transaction while losing ownership of the customer intention. The agent understands why the purchase occurred. The brand sees only what was purchased. Companies must decide where they can build direct trust and where they must become the best possible supplier inside an agent-controlled ecosystem. Not every brand will own the relationship. Some will compete to become the preferred component.

Marketing to machines is still marketing to people

The phrase marketing to AI can mislead. Machines do not possess needs, aspirations or purchasing power of their own in the ordinary commercial sense. They represent and interpret human objectives. The company task is not to persuade the model independently of the customer. It is to provide evidence that lets the model represent the company accurately and recommend it appropriately. Manipulating machine systems without delivering customer value will be unstable: the agent may be misled temporarily, but the customer experience will generate new evidence. The most durable machine-marketing strategy is therefore the same as the most durable human strategy, to provide differentiated value and demonstrate it consistently. The difference lies in the speed and scale at which inconsistency can now be discovered.

The agentic C.M.O.

The chief marketing officer of the agentic era will not be defined by mastery of a new advertising channel. The role will be defined by the ability to connect intelligence, performance and growth, understanding how customers delegate decisions, how AI systems evaluate companies and how market signals should alter what the organization builds. The function will be responsible for ensuring the brand promise survives machine scrutiny, bringing customer intention into enterprise decision-making, designing parallel influence systems for people and agents, and helping govern the AI systems that represent the company to the market.

This is a larger mandate than modern marketing has traditionally held, and a more accountable one. The C.M.O. will not be able to rely on awareness, engagement or campaign activity as sufficient evidence of impact. The function will be judged by whether it identifies new value, strengthens customer preference and turns market intelligence into profitable growth. Artificial intelligence will make many elements of marketing cheaper and faster. Its more consequential effect will be to make marketing inseparable from enterprise performance. The companies that understand this early will not merely become more visible to artificial intelligence. They will become more deserving of its recommendation.

The SnowRock take

For a mid-market company the change is unusually favorable, and it is worth saying plainly. When the buyer is a person swayed by budget, familiarity and brand recall, the biggest spender tends to win. When the buyer is an agent checking evidence, the best-run company wins. Agentic commerce shifts weight from the arena where a global brand dominates toward the one arena a smaller, disciplined operator can actually control.

The work that follows is unglamorous and entirely within reach. Make yourself legible: structure your product data, your pricing and your terms so a machine can read them without guessing. Make yourself provable: keep claims consistent across every channel and close the gap between what you promise and what customers experience, because that gap is now inspectable. And resist the branded agent unless your customers have a genuine reason to trust it over a neutral one. Novelty is not a strategy. Be recommendable, not just visible. In the evaluation economy the company that keeps its promises wins the ones it cannot afford to buy.

That is the whole lesson from the buyer side. Spend less time trying to be seen and more time being worth recommending. The agent will do the rest, and for once the mechanics of the market favor the operator who simply does the work well.