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  • Generative AI Is Already Running Inventory for Small Retailers – And the Ethical Stakes Could Not Be Higher

    Generative AI has grown faster than any tech in business history, and one of the most useful battlegrounds of late is autonomous inventory management. These are not basic forecasting spreadsheets anymore. Generative systems of today process a lot of data. This includes weather, social media, events in the area and sales history, among other things. It then creates simulations for hundreds of futures. Consequently, they write explanations in plain English and, with almost no human action, they reorder stock from the warehouse and online channels. We cannot ignore the parallel with the art world. Specifically, when Christie’s sold an AI-generated portrait for $432,500 in 2023, there was immediate and strong backlash (ARTnews, 2023). Just like AI, the inventory will also have the same collision, which violates the fundamental principles. A small-scale retailer is already living that future, and this post spells out exactly what ethical deployment demands.

    Shopify and Square have become the most accessible gateways for independent businesses. Shopify’s 2025 generative demand-forecasting engine now creates dynamic “what-if” simulations every hour and can auto-draft and send purchase orders to suppliers (Shopify, 2025). Sustainable footwear brand Allbirds slashed overstock by 25–30% across its winter 2024–2025 range because the system predicted demand shifts weeks ahead and executed replenishment without anyone opening Excel. Square took a different angle: its mid-2025 conversational AI upgrade lets a café owner simply ask “What should I stock for the weekend street festival?” and receive a fully-costed, ready-to-execute inventory plan (Block, Inc., 2025). One Chicago coffee chain using the feature cut food waste by 18% and lifted weekend margins by 11% in a single quarter.

    Adapted from: ARTnews (2023).

    Scale the same ideas up, and the numbers become jaw-dropping. Walmart’s generative-AI “self-healing” supply chain, rolled out across every US store in early 2025, detects imbalances in real time and generates rerouting instructions that execute automatically, cutting stock-outs by 30% and saving hundreds of millions in excess inventory (AIMultiple Research, 2025). Toyota used similar scenario-generation tools during the lingering 2024–2025 chip shortage to simulate thousands of supplier swaps and protect production lines (SuperAGI, 2025). Deloitte now forecasts that by the end of 2025, one in four Fortune 500 companies will have at least one autonomous generative agent making live inventory decisions (Deloitte, 2025). The trajectory is crystal clear.

    The ethical warning lights, however, are flashing red. Models trained mostly on urban, high-volume data routinely under-forecast demand in rural or minority-community stores, effectively punishing smaller and diverse retailers. Hallucinations – where the AI confidently fabricates trends that never existed – have already caused millions in spoiled stock across Europe. IBM warns that without rigorous grounding mechanisms, these errors will only grow as systems become more autonomous (IBM, 2025). Over-automation threatens warehouse and buying jobs unless deliberate reskilling programmes are built in from day one. ResearchGate’s latest ethical review insists on mandatory quarterly bias audits, full explainability logs, and human approval gates for high-value decisions (ResearchGate, 2025).

    By 2030, generative AI could automate almost half of all inventory tasks and unlock trillions in value worldwide (McKinsey & Company, 2025). For the independent retailer, that future is exhilarating – stock that practically manages itself, cash tied up for weeks instead of months, and the real chance to punch above Amazon’s weight on speed and service. But the Christie’s lesson is brutal: rush ahead without rock-solid ethical guardrails, and the backlash can kill adoption overnight (ARTnews, 2023). The retailers who will win long-term are the ones treating transparency, fairness, and human oversight as genuine competitive advantages rather than compliance headaches. Get the ethics right first, and the efficiency revolution will follow sustainably.

    References

    AIMultiple Research. (2025). 10 generative AI supply chain use cases. https://research.aimultiple.com/generative-ai-supply-chain/

    ARTnews. (2023). Christie’s AI art sale ‘augmented intelligence’ controversy surpasses expectations. https://www.artnews.com/art-news/market/christies-ai-art-sale-augmented-intelligence-controversy-surpasses-expectations-1234734870/

    Block, Inc. (2025). Square AI gains new intelligence capabilities. https://investors.block.xyz/investor-news/news-details/2025/Square-AI-Gains-New-Intelligence-Capabilities-Providing-Deeper-Business-and-Neighborhood-Insights-to-Square-Sellers/default.aspx

    Deloitte. (2025). Autonomous generative AI agents. https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html

    IBM. (2025). How generative AI will revolutionise the supply chain. https://www.ibm.com/think/topics/generative-ai-supply-chain-future

    McKinsey & Company. (2025). The state of AI in 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

    ResearchGate. (2025). Ethical considerations and challenges of AI in supply chain management. https://www.researchgate.net/publication/389255282_Ethical_Considerations_and_Challenges_of_AI_in_Supply_Chain_Management_Definition_of_AI_in_Supply_Chain_Management_SCM

    Shopify. (2025). What is AI demand forecasting? https://www.shopify.com/blog/ai-demand-forecasting

    SuperAGI. (2025). Real-world success stories: Optimising inventory with AI. https://superagi.com/real-world-success-stories-how-top-companies-are-optimizing-inventory-with-ai-in-2025/

  • What the Paper Says – A Close (and Slightly Sceptical) Look at “Generative Artificial Intelligence in Marketing: Applications, Opportunities, and Research Agenda” (Huang & Rust, 2023)

    After reading Huang and Rust’s paper in the International Journal of Research in Marketing, I closed the PDF feeling inspired and uneasy. You will realise instantly that generative AI represents not an incremental improvement but the most significant disruption marketing has experienced since the internet. The writers add to their prior four-stage service intelligence framework, which entails mechanical, analytical, intuitive and empathetic by illustrating how real marketing tasks are already happening across the full spectrum by tools such as ChatGPT, DALL-E and Midjourney. They say that generative AI will totally change the use of customer experience, content creation, and pricing strategy, including auto-generating product descriptions, co-creating visuals with customers and empathetic conversations. This paper is ambitiously structured and is already one of the most cited papers on the topic.

    This framework would definitely be useful for anyone involved with information systems in business. Mechanical generative AIs do repetitive standardisation (like thousands of SEO-optimised product listings in just seconds). Analytical generative AIs personalise at scale using customer data. Intuitive generative AIs come up with original images, videos, and copy. Empathetic generative AIs simulate human-like emotional responses in chatbots. Huang and Rust give us details and examples throughout: Coca-Cola using GPT-4 to brainstorm a new campaign, Nike letting customers design their own trainers with DALL-E and beauty brands running AI-powered virtual try-on experiences. This taxonomy finally gives practitioners a roadmap by which they can move beyond pilot projects and embed generative AI in core marketing information systems instead of using it as a side toy.

    The paper’s overly enthusiastic nature makes me wary. On page 482, the writers state that generative AI will “transform marketing in ways that are hard to overestimate”. However, the risks section gets squeezed to just a little over half of the page towards the end. They accept there are ethical issues as well as job losses, copyright infringement, misinformation and hallucination risks. Then there is a quick pivot to “further research should look at mitigation strategies.” As someone who studies information systems, I expected a more in-depth engagement with actual failures – Amazon’s biased hiring AI, Google’s Gemini image generation ‘fakes’ or the many brands who got burned by dangerous advice from AI chatbots. The EU AI Act had already been drafted, and OpenAI had published system-card warnings on GPT-4 hallucinations about its treatment of risk in 2023. Not acknowledging these cases makes the opportunities feel somewhat disconnected from the frontline reality.

    Adapted from: AIMultiple (2025)

    It was both a strong point and a weak point in the sense that it is more conceptual than empirical. This report contains no original datasets, no manager interviews, and no field experiments — just a clever synthesis and some logical extensions. That method works incredibly and proves effective for carrying out their planned tasks, but many of their pledged claims are only hypothetical. As Huang and Rust illustrate, generative AI will soon negotiate prices or manage long-term relationships with the “empathetic memory” of a long-running dialogue. The current generation of models struggles with context longer than a few thousand tokens and regularly makes up facts. For the most part, it can be inferred from the paper that the jump between nifty demos and enterprise-scale marketing information systems is much more significant than it would seem the case.

    Huang and Rust have given us just the fabulous piece that we need to include in every digital marketing syllabus. The framework with the four types of intelligence is really useful, the case studies are inspiring, and the research agenda should keep PhD students busy for years. As a critical reader, I find this paper underplays the governance, skills and infrastructure challenges businesses actually do face today. Generative AI is impactful — there’s no doubt about that. Still, to deploy it successfully will require much more than technical capability. Specifically, it requires ethical guardrails, data quality, and human oversight, which this paper largely assumes. One must read it for the vision, cite it for the framework, but add in some words of caution from the real world.

    References

    AIMultiple. (2025). Handle Top 12 AI Ethics Dilemmas with Real Life Examples. AIMultiple. https://research.aimultiple.com/ai-ethics/

    Huang, M.H., & Rust, R. T. (2023). Generative artificial intelligence in marketing: Applications, opportunities, and research agenda. International Journal of Research in Marketing, 40(3), 476–485. https://doi.org/10.1016/j.ijresmar.2023.06.002

  • AI Meets Social Media Marketing – The Smartest Information Systems in Business Right Now (If We Keep Them Honest)

    Picture a Manchester fashion label that sees a viral TikTok clip from São Paulo before breakfast. This is a DIY upcycled denim jacket tagged #recycleddenim. At 10 am, a trend-alert tool powered by artificial intelligence notifies the supply chain module of the brand, which in turn changes the order automatically with the manufacturer in Vietnam. At 2 pm, there is a hyper-personalised campaign on Instagram that shows the new jacket to people who liked similar things. Within hours, the limited run sells out. This isn’t fiction. The success comes from two tightly-linked systems: social media marketing (SMM) and AI, which turns real-time social data into products, profits, and customer loyalty.

    The speed at which artificial intelligence has migrated from research labs to boardroom priority is remarkable. A team of researchers at the Massachusetts Institute of Technology calls it “the most important general-purpose technology of our era” (Brynjolfsson and McAfee, 2017, cited in Buxmann et al., 2021). The Forbes magazine conducted a survey according to which, almost all senior executives (95%) think that artificial intelligence will soon become essential for their companies. Moreover, the McKinsey Global Institute estimated that AI might bring about an additional $13 trillion to global GDP by 2030 (Forbes Insights Team, 2018; Bughin et al., 2018, both cited in Buxmann et al., 2021). Today’s management information systems, powered by artificial intelligence, already cater to dynamic pricing in airlines and predictive maintenance in manufacturing plants. Yet the same black box algorithms that generate these gains also create serious problems. Police use facial-recognition systems that violate privacy, clinical decision tools show racial bias, and court-support systems that work in a black box manner raise questions of fairness (Rezende, 2022; Vyas et al., 2020, both cited in Dennehy et al., 2023). When social media platforms spread misinformation or polarising content at a large scale, all algorithm systems lose public trust (Janssen et al., 2020, cited in Dennehy et al., 2023). Having responsible AI is necessary for the $13 trillion dream to become a reality rather than a nightmare.

    Adapted from: Buxmann et al. (2021)

    When generative A. I get integrated with SMM, and the system becomes predictive and generative. According to Chyrak et al. (2024), AI tools are now capable of scraping public social data, detecting micro-trends before they proliferate, capturing emotional sentiment in real time, and generating tailored visuals or ad copy that has maximum conversion efficiency. Yet this power raises serious ethical concerns. The algorithms that boost sales can also spread misinformation, take advantage of unpaid creative labour, or worsen bias. For instance, in 2025, Christie held an auction for AI-generated art that was sold for $730,000, whereas 4,000 artists protested as the work was trained on their work without consent (Jones, 2021). Many are getting worried about the implications of AI chatbots. After all, most of these chatbots depend on the content generated by human creators (Ayokunmi et al., 2025). One open letter stated, “Your support of these models rewards mass theft of human artists’ work.” It also noted that without transparency, these systems risk undermining the very trust they depend on

    The bottom line is straightforward. The use of artificial intelligence, in conjunction with social media marketing, is one of the most powerful information systems that most organisations will deploy in the course of this decade. Speed, insight, and reach are previously unimaginable to generations of management. In my view, formed both by the literature and late-night discussions with coursemates, it’s the ones who view ethics and transparency as competitive advantages rather than compliance burdens who stand to win. Ensure that algorithms are designed to enable explainability and that bias audits take place on a regular basis. Use social channels for authentic two-way engagement with customers, not one-way broadcasting (Dennehy et al, 2023; Ayokunmi et al, 2025; Chyrak et al, 2024). When firms take action to make a positive impact, not only do they grow, but they also continuously earn the right to keep growing as technology scepticism spreads. Essentially, this is the overall digital transformation problem.

    References

    Ayokunmi, L. A., Seman, N. A. A., Rashid, U. K., & Mohamad, A. (2025). The role of social media marketing as an ICT tool in improving supply chain sustainability of SMEs: A systematic literature review. Procedia Computer Science, 253, 1392–1401.

    Buxmann, P., Hess, T., & Thatcher, J. B. (2021). AI-based information systems. Business & Information Systems Engineering, 63(1), 1–4.

    Chyrak, I., Koziuk, V., Siskos, E., & Darvidou, K. (2024). Comprehensive framework for social media marketing (SMM) strategy for effective business activity. Socio-economic relations in the digital society, 4(54), 39–58.

    Cygnis Media Editor. (2025, January 21). How AI-powered management information systems are revolutionising business operations. Cygnis.

    Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Mäntymäki, M., & Pappas, I. O. (2023). Artificial intelligence (AI) and information systems: Perspectives on responsible AI. Information Systems Frontiers, 25(1), 1–7.

    Jones, B. (2021, April 15). Assessing the impact of social media on marketing information systems. LinkedIn.

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