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Paper A

A Discussion on the Future of Advertising Agencies in the Impact of Artificial Intelligence

Simge AKSU — 2026 — Intermedia International e-journal

peer reviewed journal article  ·   ·  test this week

Key findings

• AI tools from Meta and Google (like automated campaign targeting and content generation) are now doing work that agencies used to charge for — threatening their traditional 'middleman' role.
• AI still struggles with things that require human judgment: reading cultural nuance, making gut-call creative decisions, handling ethical dilemmas, and connecting emotionally with audiences.
• The paper argues agencies that survive will be the ones that use AI for the fast, repetitive work, while keeping humans in charge of strategy, creative direction, and quality control — a 'hybrid' model.
• Rather than being replaced, agencies that adapt can carve out new specializations — such as AI output auditing, brand safety oversight, and culturally intelligent creative strategy — that pure AI tools cannot replicate.

Marketing implications

• If you run or work at an agency, start letting AI handle the repetitive stuff — writing ad copy variations, A/B test setups, basic media planning — so your team can focus on the work AI gets wrong: cultural judgment, emotional storytelling, and ethical calls.
• Position your agency's value around what AI cannot do: pitch clients on your team's ability to catch AI mistakes, understand local culture, and make creative leaps that automated tools miss.
• If you manage a brand in-house, be skeptical of fully AI-automated campaigns on Meta or Google without a human reviewing outputs for brand tone and cultural appropriateness — the paper flags this as a real gap.

Paper B

Generative AI for Personalized Marketing and Customer Experience in E-Commerce

C. Patel — 2026 — International Journal of Emerging Research in Engineering and Technology

peer reviewed journal article  ·   ·  use cautiously

Key findings

• A GAN-based model correctly identified loyal vs. at-risk customers about 96% of the time — beating simpler models like Logistic Regression and Random Forest, which are more commonly used in practice.
• Customer satisfaction scores were the single strongest signal for predicting whether a customer would stay loyal or leave — more predictive than other features in the dataset.
• GANs were especially good at spotting patterns in how customer behavior changes over time (e.g., a customer who used to buy often but has slowed down), which simpler models tend to miss.
• The study found that deep learning approaches like GANs can be applied to real e-commerce customer data for predicting churn and tailoring marketing, not just in theory but in a tested pipeline.

Marketing implications

• If you run an e-commerce store, your most important metric for predicting whether a customer will come back is how satisfied they were — not how much they spent. Make customer satisfaction scoring a core part of your CRM data collection.
• If you have enough transaction history, consider testing GAN or deep learning-based churn models instead of defaulting to simpler tools — this paper suggests they can catch 'at risk' customers that standard models miss.
• The finding that customer behavior patterns over time (not just snapshots) drive predictions means: set up your data pipeline to capture sequences of events (e.g., login → browse → abandon → return), not just totals.

Paper C

Expectations and Practices around AI Disclosure in CS Research

Arati Mohapatra, Danish Pruthi — 2026 — arXiv

preprint  ·   ·  test this week

Key findings

• Most major computer science conferences (35 out of 65 surveyed) have AI disclosure policies, but these policies are vague — they don't tell authors when to disclose AI use or what details to include.
• Researchers believe AI disclosure matters most when AI was used in the core research work (like designing experiments or analyzing results), especially when humans were minimally involved. They think disclosing AI use for things like polishing sentences matters less.
• In practice, the opposite happens: the most common disclosures are for low-stakes tasks like editing text or cleaning up code — not for the high-stakes tasks researchers actually care about.
• A key piece of information researchers consistently expect in disclosures — a clear statement saying the authors take responsibility for any AI-assisted work — is missing from 98% of EMNLP disclosures and 77% of ICLR disclosures.

Marketing implications

• If your marketing team or agency uses AI tools to help create research reports, strategy decks, or client-facing content, spell out specifically what AI did and confirm your team reviewed and takes responsibility for the output — don't just add a generic 'AI tools were used' line, because that tells readers almost nothing.
• When reviewing AI-generated content from vendors, contractors, or research partners, ask them to disclose which tasks AI handled and how much human oversight was applied — especially for anything involving data analysis, strategy, or insights, not just copyediting.
• If your brand or agency is building internal AI use policies for content or research, this paper's framework (mandatory/recommended/optional disclosure by task type) gives you a ready-made structure to adapt — prioritize disclosure rules for high-judgment tasks like campaign strategy or audience analysis over low-stakes tasks like grammar checks.

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