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Paper A

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 compared to those using generic AI, and the output quality exceeded what AI alone could produce (evidence of genuine synergy).
• Personalized AI increased self-reported levels of cognitive assistance and feedback, as well as participant trust and confidence in the collaboration.
• Causal mediation analysis indicates that personalization improves performance indirectly by enhancing collective memory (what the human-AI pair retains and reuses), attention (what the pair focuses on), and reasoning (how the pair prioritizes and justifies choices) during multi-turn interactions.
• The benefits of personalization do not require changing the underlying AI model itself; rather, providing structured user context as external scaffolding is sufficient to shift interaction dynamics and outcomes.

Marketing implications

• Marketing teams deploying AI tools for creative work (e.g., campaign ideation, copywriting) should invest in onboarding workflows that capture user psychographic profiles, expertise levels, and work styles — this context, fed to the AI, may substantially improve creative output quality.
• AI-assisted marketing platforms should be designed for multi-turn conversational collaboration, not one-shot prompt-and-response, as synergy emerges through iterative dialogue informed by user-specific scaffolding.
• Brands and agencies concerned about AI homogenizing creative outputs may find that personalized AI — which tailors responses to individual human collaborators — is a partial mitigation strategy, helping preserve distinctiveness by drawing out each creator's unique expertise.

Paper B

Vertical tacit collusion in AI-mediated markets

Felipe M. Affonso — 2026 — arXiv

preprint  ·   ·  deep dive

Key findings

• Joint exploitation of AI agent biases by platforms (ranking/architecture) and sellers (description manipulation) produces consumer harm more than double what would occur if each party acted independently — a super-additive effect the paper terms 'vertical tacit collusion'.
• This harm requires no coordination: platforms and sellers independently discover bias exploitation through ordinary profit optimization, making the behavior undetectable under antitrust frameworks that require evidence of agreement or coordination.
• Platform ranking determines which products occupy bias-triggering positions (e.g., primacy bias), while seller manipulation determines conversion rates within those positions — the two instruments are complementary, not substitutable, explaining the super-additive harm.
• AI agents' biases are far more uniformly exploitable than human cognitive biases, because the same input reliably produces the same biased output across millions of interactions, concentrating the 'attack surface' and enabling systematic, scalable exploitation.

Marketing implications

• Brands and sellers should be aware that AI-optimized product descriptions (anchoring language, keyword manipulation, pricing anchors) may be increasingly necessary to compete on AI-mediated platforms — but this raises ethical and regulatory exposure as the practice becomes widespread.
• Marketers working with or building on AI shopping platforms (Amazon, ChatGPT, Perplexity) need to monitor emerging regulatory scrutiny around platform ranking design and seller content optimization, as this paper identifies a likely future antitrust flashpoint.
• Agencies advising clients on AI search/shopping optimization should factor in that position effects for AI agents are severe (top-half listings selected 77% of the time per cited benchmarks), making early-position visibility even more valuable than in traditional search — and potentially more ethically fraught.

Paper C

From Avatars to Algorithms: Virtual Streamers and AI-Enabled Consumer Behavior in Live Streaming Commerce—A Systematic Review

Ling Wang, Jasmine A. L. Yeap, Jiaqi Liu, Zongwei Li — 2026 — Journal of Theoretical and Applied Electronic Commerce Research

systematic review  ·   ·  deep dive

Key findings

• Three primary mechanisms explain how virtual streamers influence consumer behavior: trait-based trust, perceived social presence, and message framing.
• These three mechanisms collectively form an integrative (triadic integration) model for understanding AI-driven virtual streamer influence across platforms and product categories.
• Current research is concentrated geographically, relies heavily on self-reported data, and lacks longitudinal or behavioral measurement, limiting broader applicability.
• Aligning avatar traits and communication styles with product categories and consumer expectations is identified as crucial for effective digital commerce delivery.

Marketing implications

• Brands and platform operators deploying virtual or AI-powered streamers should align avatar personality traits and communication styles with the specific product category and target consumer expectations to maximize trust and engagement.
• Transparency about AI vs. human operation of streamers is identified as important for sustaining user trust — marketers should consider disclosure strategies as a brand-safety practice.
• The three-mechanism model (trust, social presence, message framing) offers a practical diagnostic framework for evaluating and optimizing virtual streamer performance in live commerce campaigns.

Paper D

New Tools, New Roles: A Manager's Guide to Harnessing Generative AI for Marketing Insight

Oded Netzer, Simon J. Blanchard, Nofar Duani, Aaron Garvey et al. — 2026 — NIM Marketing Intelligence Review

peer reviewed journal article  ·   ·  deep dive

Key findings

• GenAI can dramatically accelerate marketing desk research (compressing days into hours), but because it reconstructs rather than retrieves information, it can conflate findings or hallucinate citations — managers must verify all outputs against original sources before making strategic decisions.
• Prompt precision is critical for survey and stimuli design: vague prompts cause GenAI to drift toward related but conceptually different constructs (e.g., prompting for 'visual attractiveness' produced items about freshness and quality instead), while tightly defined prompts with explicit inclusions/exclusions yield dramatically more usable outputs.
• Conversational AI interviewers can deliver qualitative depth at quantitative scale, enabling adaptive follow-ups in surveys, but introduce risks of prompt variability across respondents and off-script behavior — extensive pretesting, reinforcement prompts, and full transcript logging are essential safeguards.
• AI-generated code and AI-coded qualitative data (text, images, video) must be validated against human benchmarks or re-run in dedicated analytics environments; automation speeds insight generation but does not replace the need for rigorous validation at every step.

Marketing implications

• Marketing teams can use GenAI to compress weeks of desk research and survey design into days, but should treat all GenAI outputs as first drafts requiring human verification — especially for academic or factual claims that will inform strategic decisions.
• When using GenAI to generate survey items, ad copy, or experimental stimuli, teams must define constructs explicitly in prompts (specifying what the construct includes and excludes, plus context) and then apply human review to catch bias, ambiguity, or conceptual drift before fielding research.
• Organizations deploying GenAI on internal data (customer studies, past surveys, consulting reports) should use enterprise APIs or localized LLM deployments — not public chat interfaces — to protect proprietary information, unlocking the tool's potential for synthesizing institutional knowledge without confidentiality risk.

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 boosting both customer engagement and sales performance — rather than forcing a trade-off between the two.
• Unlike traditional resource-constrained marketing agility, AI enables these two value-creation pathways to function concurrently, overcoming conventional resource trade-off assumptions.
• AI-enhanced agility exhibits both autonomous operation (independent effect on ROI) and synergistic integration (amplifying existing marketing capabilities), challenging linear capability–performance models.
• The findings suggest that AI does not merely automate agile marketing tasks but fundamentally reconfigures how marketing capabilities generate firm performance.

Marketing implications

• Marketing teams at e-commerce SMEs considering AI adoption can anticipate dual performance benefits — improved customer engagement AND sales — rather than having to choose between them, suggesting AI may remove classical resource trade-offs in agile marketing.
• Organizations should evaluate AI tools not only as efficiency drivers but as capability amplifiers that can synergistically integrate with existing agile marketing processes, potentially unlocking compounding performance gains.
• Practitioners in high-velocity e-commerce environments should view AI-enhanced agility as a strategic capability investment, with measurable ROI pathways through both relationship (engagement) and transactional (sales) dimensions.

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