---
id: "action-develop-ai-persuasion"
type: "action-item"
source_timestamps: ["§ Decades of Marketing Science Built for a Different Customer", "§ What Comes Next: Competing for an AI Customer's Preference"]
tags: ["strategic", "marketing-science"]
related: ["concept-bnn-vs-ann", "claim-persuasion-science-gap"]
speakers: ["Kartik Hosanagar"]
action: "Test and map the specific biases, framing effects, and decision rules of AI models."
outcome: "Creates a new marketing playbook capable of persuading AI agents to select your brand over competitors."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-tier2-05-market-to-ai-customer"
sourceUrl: "https://hbr.org/2026/06/how-do-you-market-to-an-ai-customer"
sourceTitle: "How Do You Market to an AI Customer?"
---
# Develop a science of AI persuasion

**Action:** Stop relying solely on human psychological triggers (scarcity, $19.99 charm pricing, color layouts) and begin **testing what variables actually influence an ANN's decision-making.** [[entity-kartik-hosanagar]] gives marketers a concrete diagnostic to ask their teams: **"What does the AI reward, what does it ignore, and what does it trust?"**

**Why:** Because the science of human persuasion does not transfer ([[claim-persuasion-science-gap]], [[concept-bnn-vs-ann]], quote [[quote-ann-new-species]]), and because AEO alone is insufficient ([[contrarian-visibility-vs-persuasion]]).

**Outcome:** A new marketing playbook that can persuade AI agents to select your brand over competitors — mapping the agent's biases, framing effects, and decision rules.

*Enrichment note:* this is **forward-looking and speculative** — there is not yet a mature empirical literature on persuading autonomous shopping agents. A pragmatic starting hypothesis (from counter-analysis): because agents often optimize for human satisfaction, competitively-priced products with strong, well-structured reviews may still be favored; test that before assuming a clean break from human-centric signals.