How AI Assistants Are Reshaping Brand Marketing

AI assistants can affect brand marketing by helping choose a product while parts of the customer journey remain outside the brand’s view. In a MarTech column dated 2026-10-28, the author discusses such a purchase and suggests how teams might adapt. This article examines his example, estimates and recommendations; it does not promise the same outcome for every brand.

The MarTech author describes his own smartphone purchase: an AI assistant compared five options for him, and he ordered from a marketplace without visiting the brands' websites. This is the author's personal example, not a study of all buyers. It illustrates how a brand might see a sale without knowing which options and criteria the customer discussed with AI beforehand.

The issue isn't that customers turn to AI for advice — that's a given. The challenge is that marketing is built around campaigns, calendar-based plans, and the assumption that customers independently search for information, compare options, and make decisions on brand websites. When AI agents become intermediaries in this process, marketing loses visibility at the early stages of the customer journey — before purchase even happens.

How are AI assistants reshaping brand marketing?

The author recommends moving from isolated campaigns toward continuous work across the customer journey. Citing McKinsey, MarTech says teams in which AI coordinates processes while people provide direction and judgment can improve marketing ROI by up to 30%. This is an estimate of potential relative improvement, not a guarantee, not a result for every team, and not a claim that the final ROI equals 30%.

The author recommends asking about current changes as well as the previous reporting period. In his hypothetical example, automation frees up 20% of a marketer's working time; unless management decides how to use those hours, freeing the time alone does not create business value. That share is an illustration, not a measured savings benchmark for every team.

Counting emails sent or campaigns launched becomes pointless in this model. Tracking impact on business metrics matters, but that's only half the battle. From day one, leaders need to set explicit value targets, continuously measure progress against them, and actively reallocate the resources AI frees up toward work that actually drives business growth. A dashboard showing only activity won't tell you whether it mattered.

How brands can influence AI assistant recommendations

Leaders must accept an uncomfortable reality: AI assistants increasingly shape which brands customers consider. When AI agents filter information, summarize data, and suggest options on behalf of users, a brand needs to be accessible and understandable to machines before humans ever see it.

The principle resembles search engine optimization, but now extends to language models and AI agents.

MarTech connects preparation for AI discovery with AEO and GEO. The author presents these approaches as an extension of SEO principles: discoverability, credibility and verifiable product information matter. Structured data may make information easier to interpret, but it does not itself guarantee a brand recommendation.

To achieve this, you need to create a machine-readable knowledge base — a structured set of signals that includes detailed technical specifications, verified expert data, and information that AI systems can extract and cross-reference rather than invent or guess. This is critical infrastructure for brands that want to influence how AI understands and recommends their products.

Editorial checklist: assessing readiness for AI-mediated purchases

• Verify that your product data is structured: technical specifications, attributes, and categories should be presented in machine-readable format (schema.org, JSON-LD).
• Assign responsibility for your brand's representation in AI systems — someone who controls data accuracy and how algorithms interpret it.
• Build a knowledge base with expert content: detailed guides, comparison tables, FAQs with factual data that AI can use to form recommendations.
• Set up continuous monitoring: track how AI assistants describe your product, what alternatives they suggest, and where customers drop off in the funnel.
• Reallocate the time freed up by automation: if AI is handling routine tasks, clearly define which strategic projects your team should focus on.
• Measure impact, not activity: replace "number of campaigns" metrics with business results and how quickly you respond to shifts in customer behavior.

What This Means for the Russian Market

Russian teams can use the article's ideas as a plan for examining their own customer journeys. Establish which information sources customers use before ordering and which parts of that journey available data can reveal. The column does not establish that all Russian consumers have switched to AI assistants.

Our editorial recommendation is to check the accuracy of product descriptions in owned catalogs, marketplaces and partner systems. Teams can then test how AI systems interpret that information and record errors. Such a test does not prove that structuring data by itself increased sales.

If a product does not appear in an assistant's responses, check information completeness and accessibility as well as the test conditions. An absent recommendation does not establish its cause or prove that a competitor was recommended solely because of its data structure.

Frequently Asked Questions

How can a brand find out if AI assistants are recommending its product?

An editorial way to investigate is to give selected assistants the same queries and save the date, wording and response. Check whether the product appears and whether its attributes are described accurately. These observations describe the test results; they do not measure all recommendations or every real customer journey.

Should You Abandon Traditional SEO for AI Optimization?

No. The source treats SEO as a foundation for discoverability and credible information, with AEO and GEO extending that work toward AI systems. This is an evolution of the approach, not a replacement for SEO or a guarantee of inclusion in AI responses.

How to measure marketing effectiveness when the customer doesn't visit the brand's website?

For an internal measurement plan, teams can compare sales, partner data and tests of brand visibility in AI responses. UTM tags and promotional codes help distinguish trackable visits and orders, but cannot reconstruct the entire customer conversation with AI. Changes in sales or branded searches should not automatically be attributed to AI recommendations.