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Paper A

Vertical tacit collusion in AI-mediated markets

Felipe M. Affonso — 2026 — arXiv

preprint  ·   ·  deep dive

Key findings

• Joint exploitation by platforms (via ranking manipulation) and sellers (via description optimization) produces consumer harm more than double what would occur if each party acted independently — a super-additive (complementary) effect, not merely additive.
• AI shopping agents exhibit strong, consistent, and uniformly exploitable cognitive biases (position/primacy bias, anchoring, framing, keyword susceptibility, decoy effects) that are more exploitable than analogous human biases because they are homogeneous and predictable across millions of interactions.
• Vertical tacit collusion requires no coordination or communication between platform and sellers — each party independently learns to exploit AI biases through standard profit-maximizing optimization (e.g., reinforcement learning), making the harm invisible to traditional antitrust frameworks that require evidence of agreement.
• The mechanism produces a regulatory gap: existing competition law targets horizontal collusion among rivals, but vertical tacit collusion involves structurally different actors (platform vs. sellers) with naturally aligned incentives around a common exploitable target (the AI agent), which existing frameworks are not designed to address.

Marketing implications

• Sellers and brands optimizing product listings for AI shopping agents (e.g., Amazon Rufus, ChatGPT shopping, Perplexity) should be aware that description optimization tactics (anchoring language, strategic keywords, framing) may yield outsized returns precisely because AI agents process these inputs with systematic biases — but this also exposes them to potential future regulatory scrutiny.
• Marketers and brand managers should monitor emerging regulatory and platform policy developments around AI agent-mediated commerce, as the paper identifies an urgent governance gap that regulators and platforms are likely to address as AI shopping adoption scales.
• Agencies advising clients on digital shelf strategy must recognize that AI-mediated ranking introduces a fundamentally different dynamic than traditional SEO or paid placement — position in the AI agent's context window (not just search results) becomes a critical variable influencing purchase outcomes, warranting new optimization frameworks.

Paper B

Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work

Sean Kelley, David De Cremer, Christoph Riedl — 2025 — arXiv

preprint  ·   ·  deep dive

Key findings

• Participants working with fully personalized AI produced marketing campaigns of significantly higher quality and creativity than those working with generic AI, and also surpassed what AI alone could produce — indicating genuine human-AI synergy.
• Personalized AI increased self-reported levels of assistance, feedback, trust, and confidence compared to generic AI conditions.
• Causal mediation analysis showed that personalization improves performance indirectly by enhancing collective memory, attention, and reasoning during human-AI interaction — not simply by producing better one-shot AI outputs.
• The study provides a theoretical framework framing personalization as 'external scaffolding' that builds shared partner models and common ground, reducing uncertainty and enabling joint cognition.

Marketing implications

• Marketing teams deploying AI creative tools should invest in onboarding and profiling mechanisms — gathering user psychometric data, work styles, and domain expertise upfront — to meaningfully improve campaign output quality rather than relying on generic AI prompts.
• AI assistants for creative marketing tasks should be designed for multi-turn dialogue that builds shared context over time, not single-shot content generation; the interaction architecture matters as much as the underlying model.
• Personalized AI appears to increase user trust and confidence, which may support broader adoption of AI tools within creative and marketing teams — suggesting that personalization has both performance and organizational adoption benefits.

Paper C

Persuading the proxy: a framework for AI-mediated marketing decisions

Anil Bilgihan, Melanie P. Lorenz, Ye Zhang, Massimiliano Ostinelli — 2026 — International Journal of Contemporary Hospitality Management

peer reviewed journal article  ·   ·  deep dive

Key findings

• Persuasion in AI-mediated hospitality environments is bifurcated into two distinct pathways: one targeting human psychology (via AI-assisted decision support) and one targeting algorithmic evaluation (via machine-readable signals and structured data).
• Four mediators are proposed to govern how influence flows across these pathways: trust, cognitive load, preference alignment, and perceived agency.
• Boundary conditions — including service type, technological fluency, and emotional salience — determine whether human judgment or agentic optimization dominates a given decision.
• Customer loyalty, satisfaction, and decision quality are argued to emerge increasingly from a hybrid interplay between human sentiment and agentic logic, rather than from human cognition alone.

Marketing implications

• Marketers in hospitality (and adjacent sectors) should develop two distinct content and data strategies: emotionally resonant storytelling and narrative for human audiences, and structured, verifiable, machine-readable data (e.g., schema markup, API-accessible attributes) for AI agents that autonomously evaluate and book services.
• Data quality, transparency, and preference alignment are highlighted as key levers for influencing AI agent selection — meaning investments in clean, well-structured backend data may become as strategically important as creative content.
• As AI agents increasingly intermediate consumer decisions, preserving a sense of human agency in AI-assisted recommendations will be important for customer acceptance, suggesting UX and communication strategies should acknowledge and respect user autonomy.

Paper D

Empowering marketing with AI and hyper-automation

Marcin Pawłowski, Sylwia Sobolewska, Tymoteusz Doligalski — 2026

academic book chapter  ·   ·  deep dive

Key findings

• AI and hyper-automation are being actively applied across a wide range of marketing functions, including advertising content creation, performance marketing, media planning, SEO, CRM management, marketing research, and strategic planning.
• Generative AI enables rapid creation of advertising variants, but concerns about unpredictability, consistency, and quality limit its commercial viability in some contexts.
• Implementing generative AI in customer service introduces challenges including unpredictable outputs, high latency, and data privacy issues; marketing teams must learn to accept probabilistic rather than deterministic outcomes.
• Organisations are experiencing structural changes: subject-matter experts are being replaced by 'orchestrators' who manage networks of specialised AI agents, and practitioners report unmet or unrealised expectations from AI adoption.

Marketing implications

• Marketing teams adopting generative AI should build workflows that explicitly account for output unpredictability—quality control checkpoints, human review layers, and defined acceptance criteria are necessary before deploying GenAI-produced content commercially.
• Organisations planning AI-driven customer service should anticipate latency and data privacy challenges, and should set realistic internal expectations to avoid disillusionment when AI performance falls short of deterministic benchmarks.
• As AI agents take on specialised marketing tasks, the strategic value of marketers is shifting toward orchestration and prompt/agent management skills; training and hiring strategies should reflect this structural change.

Paper E

The prospect of AI-enhanced agile marketing: boosting marketing ROI through customer engagement and sales performance

Luoxi Pu, Robert Radics, Muhammad Umar, Faith Jeremiah et al. — 2026 — Marketing Intelligence & Planning

peer reviewed journal article  ·   ·  deep dive

Key findings

• AI-enhanced marketing agility operates through a 'parallel dual-mediation' pathway, simultaneously improving both customer engagement and sales performance — unlike traditional resource-constrained marketing where trade-offs are typically assumed.
• AI-enhanced agility exhibits both autonomous operation (independent effects on performance) and synergistic integration (amplifying existing marketing capabilities), challenging linear capability–performance assumptions.
• The study validates that AI can remove traditional resource trade-off barriers, enabling SMEs to pursue multiple value-creation pathways concurrently.
• Both customer engagement and sales performance mediate the relationship between AI-enhanced marketing agility and marketing ROI.

Marketing implications

• For SMEs investing in AI marketing tools, this study suggests that AI can help overcome the classic trade-off between acquisition (sales performance) and retention (customer engagement), allowing both to be pursued simultaneously.
• Marketing leaders in e-commerce contexts may justify AI investment by framing it as an agility enabler that produces ROI through multiple parallel pathways, not just single-channel optimization.
• Practitioners should audit whether their AI deployments are enhancing agility (speed, adaptiveness, responsiveness) rather than just automating existing tasks, as agility is the key mechanism identified here.

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