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Paper A

Artificial intelligence (AI) powered marketing strategy: a systematic literature review and future research direction

Sujood, A Ananda Kumar, Shuchita Singh, Nidhi Srivastava et al. — 2026 — International Journal of Quality and Service Sciences

meta analysis  ·   ·  read now

Key findings

• AI is actively changing how companies make marketing decisions — from targeting customers to personalizing messages — rather than just automating routine tasks.
• Using AI in marketing raises new ethical questions for businesses, such as concerns about data privacy, bias in automated decisions, and transparency with customers.
• The authors created a new framework called the AI Marketing Intelligence Pyramid (AIMIP) to map out how companies move through stages of using AI in their marketing, from basic automation up to more sophisticated, strategy-level AI use.
• The review identified gaps in current research — areas where not enough studies exist yet — and suggested specific directions for future academic work on AI and marketing.

Marketing implications

• If your team is debating where to start with AI in marketing, look up the AIMIP framework — it may give you a practical roadmap for what to tackle first versus what comes later as your capabilities grow.
• Before rolling out AI-driven targeting or personalization, build a checklist for ethical risks (data privacy, algorithmic bias, transparency) — this review confirms those concerns are real and increasingly documented in the literature.
• Use this paper as a shortcut to understanding the overall landscape of AI-marketing research — rather than reading dozens of papers, this review synthesizes the field and flags where the open questions still are.

Paper B

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  ·   ·  read now

Key findings

• When an AI assistant helps a customer choose a hotel or service, marketers need to appeal to human emotions AND to what the AI is looking for — these are two different jobs, not one.
• The authors identify two separate paths for influence: one where a human is still making the final call (with AI help), and one where the AI books or decides almost entirely on its own. Each path requires a different marketing strategy.
• For the human path, things like storytelling, emotional appeal, and making people feel in control still matter. For the AI path, what matters is clean, structured, machine-readable data — think verified facts and clear labels, not vivid prose.
• Customer loyalty and satisfaction in the future may depend as much on whether an AI algorithm picks your brand as on whether a human falls in love with it.

Marketing implications

• If you market hotels, restaurants, or travel services, start auditing your content for two different audiences: Is your copy emotionally engaging for humans? And is your underlying data (amenities, prices, policies, reviews) clean, structured, and machine-readable for AI booking assistants? Both matter now.
• When writing product descriptions or listings, add schema markup, structured metadata, and verifiable facts — not just pretty language. AI agents that book on behalf of travelers will increasingly rely on this kind of data to make decisions.
• Think about brand loyalty differently: if an AI agent is filtering options before a customer even sees them, winning the algorithm is as important as winning the customer's heart. Start experimenting with what signals make your property or service 'AI-preferred.'

Paper C

Agentic AI and Autonomous Marketing Systems: A Systematic Review and Integrative Framework

Emad Ramezanie — 2026 — International Journal of Advanced Business Studies

systematic review  ·   ·  read now

Key findings

• Agentic AI is moving marketing beyond just generating content — these systems can now make decisions and carry out tasks (like adjusting bids, personalizing offers, or running campaigns) on their own with little human involvement.
• This shift to autonomous marketing can bring real benefits: faster execution, continuous learning from results, and highly personalized experiences at scale.
• But more autonomy also means bigger risks — these systems can make mistakes that harm a brand, treat customers unfairly, violate privacy laws, or act in ways that are hard to explain or reverse.
• The paper proposes a framework connecting five elements: what drives adoption, how the AI agent is designed, what guardrails are in place, what value it creates, and how to weigh performance gains against risk.

Marketing implications

• If your team is considering using AI agents for tasks like automated bidding, campaign management, or personalization, this review signals you need a governance plan before you deploy — not after something goes wrong.
• Ask your AI vendor or internal team: 'What happens if this agent makes a bad decision? Who catches it, and how fast?' If they don't have a clear answer, that's a red flag.
• Start small with autonomous systems — give them a narrow, reversible task first (like adjusting email send times) before trusting them with high-stakes decisions like media spend or customer segmentation.

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