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Paper A

IMPACT OF GENERATIVE AI ON BRAND AUTHENTICITY AND CUSTOMER TRUST IN MARKETING CONTENT CREATION

sudhanshu shekhar ojha, Vibhor Kumar, A. Verma — 2026 — Zenodo (CERN European Organization for Nuclear Research)

peer reviewed journal article  ·  open access  ·  read now

Key findings

• About half of U.S. consumers say they prefer brands that avoid using AI to create content that customers see directly — meaning AI-made content can push people away rather than pull them in.
• Only a small share of consumers say that knowing content was AI-generated actually makes them trust a brand more, suggesting AI content rarely builds trust on its own.
• When brands clearly tell customers that AI was used AND show that real humans are reviewing and overseeing that content, the risk of damaging the brand's reputation appears to drop significantly.
• The authors argue that AI in marketing should be treated as a big-picture strategic decision — like hiring policy or brand voice — not just a faster way to crank out content.

Marketing implications

• If you're using AI to write or design customer-facing content, add a simple, honest disclosure — something like 'This content was created with AI assistance and reviewed by our team.' The review shows customers a human is still in charge.
• Before scaling AI content production, check whether your audience falls into the large share that dislikes visible AI content. Run a quick poll or A/B test with and without AI-disclosure language before committing your brand's full content pipeline to GenAI.
• Treat AI content decisions the same way you'd treat decisions about your brand tone or spokesperson — not just a tool choice. Get leadership alignment on when and where AI is appropriate, so it doesn't quietly erode brand trust over time.

Paper B

Directional AI Advice: Experimental Evidence from Healthcare

Yuyu Chen, Hongbin Li, Lingsheng Meng, Xinyao Qiu et al. — 2026 — arXiv

preprint  ·   ·  read now

Key findings

• Only 17% of patients who were offered the chatbot actually used it before their appointment — younger, male, employed, and first-time patients were most likely to use it.
• The chatbot was heavily biased in its advice: it warned against medications (especially Traditional Chinese Medicine and antibiotics) nearly 90% of the time, but recommended diagnostic tests without caveats 94.5% of the time. This bias appears to be baked in by AI developers trying to avoid legal liability.
• Patients who used the chatbot were about 5 percentage points less likely to receive a prescription during their visit, and about 3 percentage points more likely to be sent for diagnostic tests — the AI's biases directly changed what happened in the doctor's office.
• Patients with chatbot access reported being less satisfied with their visit and said they were less likely to follow their doctor's advice — even though the total amount they spent on healthcare did not change.

Marketing implications

• If you are building or marketing an AI tool that gives advice in any regulated space (finance, legal, health), know that the guardrails you or your AI vendor build in will directly shape what your users do — not just what they read. Audit your AI's advice for hidden directional bias before launch.
• If you sell to clients in expert-advice industries (insurance, financial planning, legal tech, health), this study is a concrete talking point: AI used before a human consultation changes the consultation itself. Position your product around this dynamic intentionally.
• Customer satisfaction can drop when AI advice conflicts with an expert's recommendation, even if the expert is right. If your product puts AI advice between a customer and a human expert, design a handoff that reduces friction — or expect complaints.

Paper C

THE INFLUENCE OF AI-DRIVEN MARKETING ON THE FINANCIAL PERFORMANCE OF DIGITAL BANKS IN NIGERIA

Asanga Aniekan, Okon Akpan Aniefiok — 2026 — Zenodo (CERN European Organization for Nuclear Research)

peer reviewed journal article  ·   ·  read now

Key findings

• AI-powered personalization (showing customers relevant offers based on their data) explained about 61% of the variation in banks' financial performance — a strong statistical relationship.
• AI-enabled CRM (using AI to manage and improve customer relationships) was the strongest predictor, explaining about 66% of variation in financial performance.
• AI-powered chatbots and virtual assistants explained about 57% of variation in financial performance — still a substantial relationship.
• All three AI marketing tools were positively and significantly linked to better financial performance, suggesting that banks investing in these tools tend to perform better financially.

Marketing implications

• If you work in or with a bank (or any financial services brand), prioritize getting your AI-driven CRM in order before other AI tools — this study found it had the biggest link to financial results.
• If you're pitching AI marketing investments internally, you can point to this paper as evidence that digital banks using AI personalization, CRM, and chatbots tend to report better financial outcomes — but be honest that this is employee perception data, not audited financials.
• If you're running chatbots for customer service, this study suggests they're not just a cost-cutting tool — they may actively contribute to revenue performance when positioned as part of a broader marketing strategy.

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