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Paper A

From Stereotypes to Strategy: Addressing Gender Bias in AI-Powered Marketing

Caterina Fox, Gabriele Schuster — 2026 — International Conference on Gender Research

peer reviewed journal article  ·   ·  deep dive

Key findings

• Marketing professionals vary widely in how seriously they take AI bias: some see it mainly as a PR risk to their brand, while others think of it as a real social problem affecting real people. Very few have formal systems to regularly check whether their AI outputs are fair or inclusive.
• AI bias in marketing is not just a technology problem — it can be baked into the creative process itself. For example, if a team has always shown women in domestic roles, the AI tools they train or prompt will learn to keep doing the same thing, even if no one is consciously choosing that outcome.
• The practical fixes that interviewees trust most are human ones: having a more diverse team, asking for honest feedback from different people before publishing, and writing more careful, specific prompts when using AI tools (e.g., specifying who should appear in an image rather than letting the AI decide by default).
• Even when people in marketing agencies want to do better on bias, they are often blocked by time pressure, tight budgets, and lack of support from leadership — meaning good intentions rarely turn into consistent habits.

Marketing implications

• When using AI tools to generate images or copy, write specific prompts instead of vague ones — for example, say 'show a woman as a surgeon' rather than just 'show a medical professional,' because vague prompts let the AI fall back on stereotypes by default.
• Before publishing AI-generated content, get a quick review from at least one person outside your usual creative circle — different perspectives catch biased patterns that feel invisible to people already used to seeing them.
• If your organization uses AI at scale (e.g., automated ad targeting or AI-generated visuals), set up a simple recurring check — even a monthly 10-minute audit of recent AI outputs — to spot patterns like consistently showing only one gender in certain roles. You don't need a big budget; you need a habit.

Paper B

The Impact of AI-Generated Marketing Imagery on Consumer Trust and Purchase Intentions: Examining Effect of Human-AI Assisted Images on Marketing

Rushikesh Lahane, Mahek Ahuja, Mehak Sharma, Amrita et al. — 2026 — International Journal for Research in Applied Science and Engineering Technology (IJRASET)

peer reviewed journal article  ·   ·  skim later

Key findings

• People trusted human-made ads much more than AI-made ads. On a 7-point scale, human ads scored 5.63 for trust vs. 4.24 for AI ads — a gap that was statistically significant.
• People also said they were more likely to buy after seeing a human-made ad (5.62) compared to an AI-made ad (4.65).
• Hybrid ads — made with both human and AI input — partially closed the trust gap (scoring 5.08 vs. AI's 4.24), but did NOT meaningfully boost purchase intent over fully AI-made ads.
• Consumers saw AI-made ads as less real and less effortful than human-made ones. Hybrid ads fell in between — more credible than pure AI, but still not as trusted as fully human work.

Marketing implications

• If you're using AI to make your ads, keep humans visibly involved in the creative process — and consider telling your audience that. Even a 'Human-reviewed' label or a human creative director's credit may help recover some of the trust consumers lose when they know AI made the image.
• Don't assume that just because someone trusts an ad more, they'll buy more. The study found trust and buying intent don't always move together — so optimize for both, not just one.
• For high-stakes or personal product categories (like skincare or cosmetics), lean on human-crafted visuals or at least human-AI hybrid approaches. These categories likely have higher trust requirements.

Paper C

The AIMx framework: integrating marketing mix modeling, attribution, and AI-driven analytics for adaptive decision systems

Thi Phuong Lan Nguyen — 2026 — Future Business Journal

peer reviewed journal article  ·   ·  skim later

Key findings

• When companies use all three marketing measurement tools together — market mix models (which show what channels drove sales), attribution (which shows which touchpoints influenced a purchase), and incrementality tests (which show whether spending actually caused a result) — they make more stable budget decisions than companies using each tool separately.
• The simulation suggests that combining these tools in one AI-driven system helps companies respond faster to market changes, rather than waiting for lagged reports from each tool independently.
• The paper argues that human managers still need to stay in the loop — AI handles the number-crunching, but people are responsible for interpreting results, making final calls, and ensuring ethical use of the data.
• The authors propose that if many companies adopt this kind of integrated system, it could have a stabilizing effect on competitive markets overall — though this broader claim is purely conceptual and not tested with real data.

Marketing implications

• If your team runs MMM, attribution, and A/B incrementality tests as separate processes, consider connecting them: bring the outputs into a shared dashboard or decision meeting so budget calls use all three signals at once rather than treating each in isolation.
• When your MMM says one channel is driving revenue but your incrementality test says cutting that channel's budget has no effect, you now have a conflict — the AIMx logic suggests you should resolve that conflict before moving money, not just trust one tool.
• As you add more AI to your measurement stack, keep a human in the loop for final budget decisions: this paper argues that removing human judgment from AI-driven allocation is a governance risk, not just a strategic one.

Paper D

AI-Augmented Marketing Automation: Transforming Decision-Making in Omnichannel Retailing

Dainik Kumar Ray, Smita Kaushik — 2026 — International Journal of Integrative Studies (IJIS)

peer reviewed journal article  ·   ·  skim later

Key findings

• AI-powered marketing systems outperform traditional rule-based systems on key metrics according to figures compiled by the authors: customer engagement goes from 60% to 85%, personalization from 50% to 80%, and ROI from 10% to 30% — though these numbers are not from an original study, they are synthesized from prior literature.
• When a retailer connects data from all customer touchpoints (website visits, emails, app use, in-store visits) into one system and lets AI analyze it, the AI can predict what a customer wants to buy before they search for it — and then automatically send the right message at the right moment.
• The authors describe a five-step AI marketing framework: collect all customer data, run it through an AI engine, automate campaign decisions, execute across all channels at once, and continuously measure and improve results.
• In the Best Buy case study example (based on secondary sources), after implementing an AI marketing system, conversion rates reportedly rose from 10% to 25%, customer retention from 60% to 80%, and marketing costs dropped by 40% — though these figures should be treated with caution as they come from an illustrative case, not a controlled study.

Marketing implications

• If you manage marketing for a retailer, this paper gives you a plain-language checklist for building an AI-powered system: first, get all your customer data into one place (a Customer Data Platform); then layer on AI to make automated decisions; then let the automation execute campaigns across email, web, and social simultaneously.
• If you're already using a marketing automation tool like HubSpot or Salesforce, the next step is to connect it to richer customer data and use AI-driven segmentation — the paper suggests this alone can meaningfully improve both personalization and ROI, though you'd need to test it in your own context.
• If you're evaluating whether to invest in AI marketing tools, this paper provides a simple before/after framework you can adapt to benchmark your current system and set targets — just be aware that the specific numbers cited here are illustrative, not guaranteed.

Paper E

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MARKETING STRATEGIES IN FAST-PACED BUSINESS ENVIRONMENTS

Akshay Goel, Anil Kanwa — 2026 — ShodhPrabandhan Journal of Management Studies

peer reviewed journal article  ·   ·  skim later

Key findings

• AI use explained about 55% of the variation in marketing outcomes (R² = 0.551), meaning more than half of what made marketing strategies work well could be tied back to how AI was used.
• Using data to make decisions and personalizing messages to individual customers were the two biggest factors that predicted better marketing results — more so than efficiency or customer retention alone.
• AI helped companies become faster and more flexible in responding to market changes, not just cheaper or more automated.
• Competitive advantage and keeping existing customers were also meaningful predictors, though slightly weaker than data use and personalization.

Marketing implications

• If you're pitching AI investment to leadership, this study gives you a data point: surveyed marketing professionals linked AI use — especially data-driven targeting and personalization — to noticeably better results. Use it as supporting evidence, not proof.
• Focus your AI budget on two things first: better data infrastructure (so decisions are based on actual numbers) and personalization tools (so messages feel relevant to each customer). These showed the strongest link to good outcomes in the study.
• If your team is debating whether AI is just a cost-cutting tool or something more strategic, this study backs the 'strategic' view — respondents said AI helped them move faster and respond to market shifts, not just do the same work cheaper.

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