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Preparing Your Brand for Agentic AI

Preparing Your Brand for Agentic AI

TL;DR: AI agents are transforming how consumers research and purchase. Brands must decide whether to deploy their own agents, optimize for third-party agents, or both — while managing the trust gap that makes 72% of consumers demand transparency about AI vs. human interactions.

The Shift Is Already Happening

StatisticSource
60% of US shoppers expect to use agentic AI for purchases within 12 monthsKearney (July 2025)
2/3 of Gen Z and 50%+ of Millennials already use LLMs for product researchHBR (2024 data)
14% of US consumers prefer ChatGPT over Google for searchesFortune
40% month-over-month growth in Target’s ChatGPT-referred trafficFortune
35% of Walmart’s referral traffic now comes from ChatGPTFortune
10% of revenue for some brands already comes from AI agentsFortune

This isn’t future speculation — it’s happening now.

The Three Interaction Modes

AI agents create three distinct brand-consumer interaction patterns:

1. Brand-to-Consumer Agents

Your company deploys proprietary agents that guide customers.

Example: Capital One’s Auto Navigator guides car purchases through financing options.

Advantages:

  • Control over brand voice and recommendations
  • Access to first-party data for personalization
  • Human escalation capability competitors lack

2. Consumer Agents

Independent tools (ChatGPT, Claude, Perplexity) act on behalf of individuals across multiple brands.

Challenge: You don’t control how these agents represent you.

Opportunity: Optimization can increase your “share of model” — how often and favorably you appear.

3. Full AI Intermediation

Autonomous agents negotiate and complete transactions without human involvement.

Example: OpenAI’s restaurant reservation system books tables automatically.

Implication: Your checkout flow and APIs must support agent-led transactions.

Strategic Decision: Should You Deploy an Agent?

Not every brand should. Key considerations:

When to Deploy

  • High-volume routine transactions (Amazon’s Subscribe & Save: 23% of US customers)
  • Complex product matching (Sephora’s Color IQ: 3x purchase completion, 30% fewer returns)
  • Support-heavy categories where AI can handle 80%+ of queries

When NOT to Deploy

  • Luxury experiences where human guidance is the value
  • Products where “visceral experience” matters (Lamborghini rejected autonomous driving)
  • Relationship-dependent businesses where automation undermines trust

Domains Naturally Resistant to AI Adoption

  • Personally meaningful purchases — hobbyist identity-linked products (vinyl collectors, craft brewers)
  • Gift-giving — emotionally significant interactions where personal selection matters
  • High-stakes decisions — healthcare, legal, financial where human judgment is valued
  • Subjective recommendations — art, fashion, taste-dependent categories
  • Luxury/premium — experiences where human guidance IS the product

The hybrid model often wins: AI handles routine queries while humans focus on complex, high-value interactions.

Real-World Results

CompanyApproachResult
AG1Trained AI like new employee with brand voice99% satisfaction matching human standards
ServiceNow80% AI, 20% human escalation52% faster complex case resolution
Vuori~40% AI chat handlingSpecialists freed for high-value interactions
SephoraColor IQ + personalization3x purchase completion, 30% fewer returns

Closing the Consumer Trust Gap

Research with 3,268 UK participants revealed what drives AI adoption:

FactorWeight in Decision
Privacy31%
Auditability/human oversight26%
TransparencyCritical

Key finding: Embedding responsible AI features increased adoption from 2.4% to 63.2% for pension apps.

72% of consumers demand transparency about whether they’re interacting with AI or humans.

Trust-Building Actions

  • Clearly disclose AI vs. human interactions
  • Provide human escalation options
  • Explain AI decision logic where possible
  • Implement and communicate responsible AI practices

Optimizing for Third-Party Agents

When consumers use ChatGPT, Claude, or Perplexity to research your category:

Monitor “Share of Model”

Pernod Ricard discovered their brands were being misrepresented — Ballantine’s Scotch was incorrectly positioned as premium when targeting the affordable segment.

Regular monitoring process:

  1. Prompt leading LLMs with typical customer queries
  2. Document how you’re represented
  3. Identify misrepresentations
  4. Update content to correct positioning

Leverage Emerging Tools

Strategic Text Sequences (STS): Research-backed text patterns that improve LLM recommendations. Harvard found brands rose from excluded to top recommendations.

llms.txt: Machine-readable brand format adopted by Cloudflare, HubSpot, Stripe. Results: 12-25% increase in AI traffic.

Prompt sensitivity: Carnegie Mellon research found synonym substitutions alter recommendations by up to 78.3%. Test variations constantly — small wording changes dramatically affect which brands get recommended.

Competitive Advantages for Brand Agents

What can your agent do that ChatGPT can’t?

  1. Proprietary product knowledge — Sephora’s 140,000 skin tone differentiations
  2. First-party customer data — 34+ million profiles for personalization
  3. Human escalation — Seamless handoff to experts
  4. Real-time inventory/pricing — Information third-party agents can’t access
  5. Transaction completion — Checkout within the conversation

Organizational Changes Required

  1. Continuous LLM monitoring — Track brand representation across major models
  2. Marketing realignment — Prompt-based optimization alongside keywords
  3. Data infrastructure — First-party customer and product data capabilities
  4. Cross-functional teams — Marketing + Product + Service + AI expertise
  5. Experimentation culture — LLM behavior evolves; optimization must too
  6. Ethical AI frameworks — Implement and communicate responsible practices

Integration Strategy

Instacart’s approach:

  1. Built Ask Instacart (ChatGPT plugin in their app)
  2. Created ChatGPT integration for adding ingredients to carts
  3. Launched custom GPT when OpenAI introduced the feature

Lesson: Maintain presence wherever AI conversations happen — customers encounter brands across the ecosystem, not just on your website.

Key Takeaways

  • Three modes: brand agents, consumer agents, full AI intermediation
  • Not every brand should deploy an agent — assess customer preferences
  • Hybrid models (AI + human escalation) often perform best
  • 72% of consumers demand AI transparency — trust is critical
  • Monitor and optimize “share of model” in third-party AI systems
  • Organizational change required: monitoring, data, cross-functional teams

Sources