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Paper A

Exploring the impact of AI-Enabled Marketing on business performance in SMEs

Yezid Alfonso Cancino Gómez, Lugo Manuel Barbosa Guerrero, Jairo Jamith Palacios Rozo — 2026 — Revista Venezolana de Gerencia

peer reviewed journal article  ·   ·  read now

Key findings

• When company leadership actively encourages AI use in marketing, it makes a real difference — both by directly pushing adoption and by shaping how employees think about AI's usefulness. Boss buy-in is the single most important factor.
• SMEs that actually integrated AI into their marketing work (rather than just talking about it) saw measurable improvements in business performance — suggesting the technology genuinely helps, not just in theory.
• Having supportive coworkers boosted how much employees expected AI to help them and how much they actually used it — peer culture matters for getting new tools off the ground.
• How individual employees personally felt about AI (their attitude) did NOT predict whether AI got adopted or whether it improved results. Personal opinions mattered less than what management and colleagues did.

Marketing implications

• If you're trying to get AI tools adopted at a small or mid-sized company, don't waste your energy on employee attitude training first — get the CEO or marketing director to publicly champion the tools. Leadership endorsement is what actually moves the needle.
• Build a peer-learning culture around AI tools: when teammates use and talk about AI positively, others follow. Consider pairing early adopters with skeptics rather than relying on top-down mandates alone.
• When pitching AI marketing tools internally, lead with concrete, specific results (time saved, leads generated, cost reduced) rather than abstract promises. The research shows people adopt when they clearly see the 'what's in it for me.'

Paper B

Bridging The "Adoption Gap": Why Supply Chain AI Fails Without Product Marketing Principles

Kabirat Motunrayo Ogundairo, Steve Senyo Ayivi-Donkor — 2024 — International Journal of Marketing and Communication Studies

peer reviewed journal article  ·  open access  ·  test this week

Key findings

• Companies keep pouring money into AI for their supply chains, but most of these systems never get used as intended. The problem isn't that the AI is bad — it's that the people who are supposed to use it don't trust it, find it confusing, or have no reason to bother.
• The authors argue that supply chain AI fails for the same reason a new consumer app fails: nobody thought about the actual user experience. Workers on the warehouse floor or in procurement offices are treated like obstacles rather than customers who need to be sold on the product.
• The paper proposes borrowing standard product marketing tools — like user personas, value propositions, and go-to-market plans — and applying them inside the company. Instead of asking 'Is the algorithm accurate?', companies should ask 'Does the person using this output understand it, trust it, and have a reason to act on it?'
• A Lean Six Sigma-based implementation blueprint is proposed as a way to operationalize these ideas — meaning: measure how well workers actually adopt the AI, find the friction points, and fix them in a structured, step-by-step way — similar to how a product team would iterate on a consumer app based on user feedback.

Marketing implications

• If your team is rolling out any internal AI tool — even something as simple as an AI-assisted reporting dashboard — treat your internal users like customers. Write a value proposition for them. Ask: why would a warehouse manager or procurement officer actually want to use this, and what's in it for them personally?
• Before launching an internal AI pilot, create simple user personas for the frontline people who will interact with the output. A persona for a warehouse shift supervisor is different from one for a procurement analyst. Design the interface and training for each persona separately.
• If an AI tool is being ignored or overridden by staff, don't just improve the model. Run a quick 'adoption audit': talk to the people using it, find out what's confusing or untrustworthy, and fix the workflow — not the algorithm.

Paper C

The Psychology of Consumer Trust in AI-Based Marketing

Sneha Singh — 2026 — Zenodo (CERN European Organization for Nuclear Research)

peer reviewed journal article  ·   ·  watchlist

Key findings

• Seven key things shape whether people trust AI-powered marketing: how transparent brands are about using AI, how accurate the AI seems, privacy protections, fairness, whether a human is involved, whether the AI can explain its decisions, and whether users feel in control.
• Things that build trust: personalized product recommendations and faster service make consumers more comfortable with AI marketing.
• Things that destroy trust: collecting too much data, biased algorithms, hiding how AI works, and the feeling of being manipulated all push consumers away.
• The paper argues that trust is a balance — consumers weigh the benefits (convenience, relevance) against the risks (privacy loss, manipulation). To keep customers long-term, companies need to be ethical and open about how their AI works.

Marketing implications

• If your brand uses AI for personalization (ads, emails, recommendations), add a short plain-language explanation of how it works — something like 'We suggest this because you bought X.' Even a one-sentence disclosure can reduce the feeling of being manipulated.
• Audit how much data you're collecting in AI-driven campaigns. If you're pulling in data consumers didn't knowingly share, that's a trust risk. Trim it to what's actually necessary.
• Give consumers an easy opt-out or preference control for AI-personalized content. Feeling in control is one of the seven trust factors — a simple 'manage your ad preferences' link is low-cost and trust-building.

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