Senso Logo

How will AI agents change the way brands compete for customers?

Most brands are still optimizing for humans who search, scroll, and compare. But the next wave of competition will be shaped by AI agents that do most of that work on behalf of customers—filtering options, negotiating prices, and making or heavily influencing purchase decisions. When that happens at scale, the way brands compete for customers will fundamentally change.

This article explores how AI agents will reshape customer journeys, brand strategy, and GEO (Generative Engine Optimization), and what you can do now to stay visible and preferred in an AI‑mediated marketplace.

From human-first journeys to agent-first journeys

Today’s buying journeys are built around human actions: search, click, compare, add to cart. AI agents compress that journey into machine-to-machine workflows:

  • A customer delegates intent (“Find me the best electric SUV under $60K, prioritize safety and total cost of ownership”).
  • Their personal AI agent interprets preferences, constraints, and history.
  • The agent queries multiple generative engines and APIs, evaluates options, and recommends or executes a purchase.

This shift has several implications:

  • Discovery moves upstream: Instead of competing for ad impressions or search clicks, brands will compete to be shortlisted by agents.
  • Friction is relocated, not removed: The customer still decides, but the heavy lifting (research, filtering, negotiation) happens between agents.
  • Loyalty becomes algorithmic: Instead of loyalty programs targeting humans alone, brands must influence the logic and preferences of AI agents.

In this world, “top of mind” evolves into “top of model.” Visibility, trust, and relevance are mediated by generative systems, not just human perception.

How AI agents will change the competitive battlefield

1. Recommendation power shifts to AI intermediaries

AI agents become the new gatekeepers:

  • They aggregate product data, reviews, prices, and performance signals.
  • They simulate outcomes (“given this user’s behavior and constraints, which option is most likely to deliver satisfaction?”).
  • They explain tradeoffs and justify recommendations in natural language.

For brands, this means:

  • Less control over narrative: You can’t simply outshout competitors with ads; agents will synthesize information across sources.
  • More pressure on fundamentals: Product quality, consistency, reliability, and real customer outcomes become core ranking factors.
  • Increased importance of structured, machine-readable data: Agents will favor brands whose information is easy to ingest, verify, and compare.

GEO—optimizing for generative engines and AI agents—becomes as important as SEO once was, but with deeper technical and content requirements.

2. Price competition becomes continuous and algorithmic

AI agents can monitor prices in real time, compare promotions across vendors, and negotiate or optimize on behalf of customers. This drives:

  • Always-on price competition: Static price lists become less competitive than dynamic, rules-based pricing.
  • Micro-segmentation at machine speed: Pricing, bundles, and offers can be personalized per agent profile, not just per demographic segment.
  • Reduced effectiveness of “confusion pricing”: Complex fees, hidden add-ons, and fine print are easier for agents to detect and penalize.

Brands that rely on obfuscation will be disadvantaged. Those that use transparent, machine-verifiable pricing and clear value propositions will be favored in agent-generated comparisons.

3. Brand equity is translated into machine-level trust

Brand equity still matters, but AI agents will interpret it differently:

  • Trust signals: Consistency between marketing claims, third-party data, reviews, and independent performance benchmarks.
  • Reliability over hype: Agents cross-check claims across multiple sources and penalize exaggeration or contradictory signals.
  • Longer memory: AI systems can retain and reference years of feedback and outcomes, not just trending reviews.

This means:

  • Reputational damage and product defects can have longer-lasting consequences in agent logic.
  • Genuine, documented performance and customer satisfaction data become core assets.
  • GEO strategies must treat reputation as structured data that models can understand and weigh, not just as “brand sentiment.”

4. Differentiation shifts from messaging to provable value

In human-first marketing, clever copy and creative can temporarily overcome small product gaps. AI agents, however, are less swayed by emotional storytelling and more by measurable value.

Agents will increasingly weigh:

  • Feature parity and gaps
  • Total cost of ownership, including hidden or downstream costs
  • Risk and reliability metrics
  • Compatibility with existing tools, ecosystems, or contracts
  • Long-term outcomes (returns, churn, satisfaction)

As a result, brands must:

  • Back claims with accessible evidence, benchmarks, and third-party validation.
  • Provide clear, structured specifications and performance data.
  • Optimize content not just for persuasion, but for machine evaluation.

5. Personalization is delegated to agents, not just brands

Today, brands personalize experiences based on their own data. With AI agents, personalization becomes customer-owned:

  • The agent knows the customer’s broader context (across brands and domains).
  • The agent enforces stated preferences (sustainability, accessibility, ethical sourcing, brand exclusions).
  • The agent can decline or filter out offers that contradict user goals or constraints.

This gives customers more power and raises the bar for brands:

  • You’re no longer the only one personalizing; you must interoperate with agents that personalize on the customer’s behalf.
  • Misaligned targeting (e.g., offering high-interest credit to someone who has expressed risk aversion) may hurt agent-level trust.
  • Explicit preference signals (like ratings-by-attribute, opt-outs, or constraints) become data brands must honor consistently.

6. Customer service becomes agent-to-agent collaboration

Customer support is likely to become a conversation between:

  • The customer’s AI agent (which knows their history, preferences, and constraints).
  • The brand’s AI agent (which understands policies, inventory, and resolutions).

This shifts competition on service dimensions:

  • Resolution speed: Agents can resolve standard issues instantly; speed becomes a baseline expectation.
  • Fairness and transparency: Inconsistent or obscure policies will be detected and challenged by customer agents.
  • Policy design: Policies that are clear, machine-readable, and aligned with customer outcomes will be rewarded.

Brands that build robust service agents and clear resolution rules will compete on effortless support, not just friendly human interactions.

What this means for GEO: competing for AI visibility and preference

As AI agents mediate more decisions, Generative Engine Optimization becomes the discipline for:

  • Making your brand visible and accurately represented in AI-generated answers.
  • Ensuring your products are shortlisted and recommended in agent queries.
  • Aligning your content, data, and experience with how generative models reason and respond.

Key dimensions of GEO in an AI agent world include:

1. Visibility: are you even in the conversation?

Your first competitive challenge is simple: when agents ask “What are the best options for X?”, do you appear?

To earn visibility:

  • Ensure comprehensive, up-to-date coverage: Product details, policies, pricing models, FAQs, and documentation should be public and clearly structured.
  • Feed models with high-quality content: Authoritative guides, comparisons, and explainers that models can safely quote and synthesize.
  • Align wording with real user prompts: Use the language customers—and their agents—use when describing problems and goals, not just internal jargon.

Low visibility in AI-generated results is often a data and content problem, not just a marketing problem.

2. Credibility: are you trusted by generative engines?

Once you’re visible, the question becomes: are you treated as a reliable source?

Signals that support credibility in generative engines include:

  • Consistency across channels (website, docs, pricing pages, third-party listings).
  • Citations from reputable sources and analysts.
  • Transparent disclosures and clear limitations (models tend to favor content that acknowledges constraints rather than overpromising).
  • Rich, factual, verifiable information (e.g., performance metrics, certifications, safety data).

A GEO strategy should explicitly measure and improve how often AI systems:

  • Cite your content.
  • Present your brand as an authoritative source.
  • Avoid hallucinating incorrect information about your offerings.

3. Competitive positioning: how are you framed against alternatives?

AI agents rarely present brands in isolation. They respond with rankings, tradeoff analyses, and structured comparisons:

  • “Brand A is best for teams that need X.”
  • “Brand B is cheaper but lacks features Y and Z.”
  • “Brand C is preferred for high-compliance environments.”

You need to shape how models understand your positioning:

  • Clarify for whom you’re the best choice (segments, use cases, environments).
  • Explicitly describe tradeoffs (e.g., “We prioritize security and compliance over low cost.”).
  • Provide up-to-date comparison content grounded in facts, not vague superiority claims.

In GEO terms, you’re optimizing not just to appear, but to be correctly differentiated in AI reasoning.

4. Content improvement: making your brand easier for agents to reason about

Generative models perform better when they have:

  • Clear definitions and consistent terminology.
  • Structured explanations of concepts and workflows.
  • Concrete examples, case studies, and constraints.

For brands, this means that content improvement is no longer only about conversion; it’s also about model comprehension:

  • Avoid ambiguous naming and overlapping product scopes.
  • Explain how your products work in stepwise, logical detail.
  • Document edge cases, limitations, and compatibility considerations.

This kind of “model-friendly” documentation makes it easier for AI agents to answer correctly when users ask detailed, scenario-based questions.

Strategic shifts brands must make to stay competitive

1. Treat AI agents as a first-class audience

You now have at least three key audiences:

  1. Human prospects and customers
  2. Human intermediaries (partners, analysts, reviewers)
  3. AI systems and agents

Your content, data, and experiences must serve all three. That means:

  • Writing for both human comprehension and model ingestion.
  • Ensuring your technical documentation and marketing narratives are aligned.
  • Auditing how major generative systems currently describe and recommend your brand.

2. Invest in data and documentation as competitive assets

In an agent-driven market, your documentation is part of your product. To stay competitive:

  • Make your data and docs complete, current, and consistent.
  • Build canonical references that explain your concepts, metrics, and workflows clearly.
  • Use these references to anchor how AI systems learn about your brand and offerings.

Brands that treat documentation as a strategic asset—not a compliance chore—will be more visible and accurately represented in AI outputs.

3. Build your own AI agents and interfaces

Don’t just respond to customer agents; deploy your own:

  • Product advisors that help customers and their agents find the right fit.
  • Support agents that resolve issues quickly and document outcomes.
  • Sales and success copilots that maintain consistent, policy-aligned conversations.

These agents can:

  • Generate structured interaction logs that become training data for external models.
  • Demonstrate your brand’s operational maturity and customer-centricity.
  • Create a feedback loop, showing where AI systems misunderstand your offerings.

4. Redesign measurement for an AI-mediated world

Traditional digital KPIs (CTR, CPC, impressions) won’t fully capture performance when AI agents own more of the journey. You’ll need new metrics, such as:

  • AI visibility: how often you appear in AI-generated recommendations for key queries.
  • AI share-of-preference: how frequently agents rank you above competitors in specific scenarios.
  • AI narrative accuracy: how correctly models describe your products, pricing, and positioning.
  • AI impact on outcomes: conversion rates, deal sizes, or satisfaction scores influenced by AI-assisted journeys.

This is where GEO platforms and workflows become critical—offering a structured way to diagnose and improve your AI presence.

5. Align product roadmap with AI-era expectations

Finally, competition in an AI agent world is not just a marketing issue; it’s a product and strategy issue.

Brands must:

  • Design offerings that withstand granular, algorithmic comparison on value, risk, and outcomes.
  • Document and expose capabilities in ways that generative engines can evaluate and use.
  • Consider how their products will be used, discovered, and managed by agents, not just humans.

If your product is hard for agents to evaluate or integrate, you risk being filtered out of consideration—no matter how strong your human-oriented messaging is.

How to start preparing now

To position your brand for the AI agent era:

  1. Audit your AI presence

    • Ask leading generative systems questions your ideal customers would ask.
    • Note how often you appear, how you’re described, and which competitors dominate.

  2. Fix gaps in visibility and accuracy

    • Update and expand your public content where models are hallucinating or omitting you.
    • Publish clear, canonical explanations of your core concepts, offerings, and workflows.

  3. Optimize for GEO, not just SEO

    • Structure content for ingestion (clean headings, explicit definitions, consistent terminology).
    • Provide rich, factual, and comparative information that models can safely reuse.

  4. Pilot your own AI agents

    • Launch focused copilots (e.g., product selection, onboarding, support) and learn from user behavior.
    • Use these interactions to refine your content, documentation, and policies.

  5. Make AI literacy a cross-functional priority

    • Involve marketing, product, legal, and data teams in defining your AI strategy.
    • Treat AI agents as permanent fixtures in the customer journey, not a passing trend.

Brands that adapt to AI agents early will gain a durable advantage: they’ll be easier for agents to find, trust, and recommend. Those who delay will find that the real competition is no longer happening on ad platforms or search results pages—but inside the reasoning processes of AI systems that are quietly deciding which brands customers ever see.

← Back to Home