IS5542 Chap.9 GenAI Use Cases in Marketing and Retail
GenAI Use Cases in Marketing and Retail
Marketing: a maturity framework
The marketing guest lecture draws on a large survey of marketers across many countries to define four pillars of AI-powered marketing, measurement and insight, media and personalisation, creative and content, and people and processes, and four stages of maturity from essentials to transforming.
Its message is that a company needs to use AI to run the strategy it already has, and its data show generative video as the most planned investment.
Retail: trends and cases
The retail lecture sets out five trends, multimodal AI, agents, assistive search, near-invisible customer experience and tougher security, then shows named brands reporting gains in click-through, conversion, order value and search use from generated imagery, better search and recommendations, faster creative concept work and predictive maintenance.
From chatbot to agent
The same lecture traces an evolution from a prompted model to retrieval, then to tools and actions, then to multi-agent systems.
A chatbot can write a social post but cannot publish it until it is given tools and a reasoning loop that lets it act, observe and continue.
Agentic commerce
Finally the lecture describes agents that anticipate needs, compare options and buy with permission, a common protocol that lets outside agents check out while retailers remain the seller of record, and a two-step preparation of data foundations followed by ecosystem connection.
The venture-capital session adds that vertical applications beat general ones.
What the investors add
The venture-capital session describes AI-native firms as born global and extremely lean, and expects the most adoption in technology, life sciences and professional services, which frames the retail cases as part of a wider shift.
What this chapter covers
- 01
Four pillars of AI-powered marketing
- 02
Four maturity stages
- 03
Planned investments and generative video
- 04
Five retail trends
- 05
Reported results from named retail cases
- 06
Low-cost AI-generated advertising
- 07
From prompts to tools and multi-agent systems
- 08
Agentic commerce and the seller of record
Worked example · free
Diagnose a marketing team's maturity
- 2Place the creative pillar on the maturity stages.
- 2Place the measurement pillar.
- 3Recommend and justify the next step.
Key terms
- Maturity Stage
- One of four levels, from essentials to transforming, describing how far an organisation has built AI into its marketing.
- Incrementality Testing
- Measuring the additional result caused by a campaign or channel compared with what would have happened without it.
- Seller of Record
- The business legally responsible for a sale; under the commerce protocol described, the retailer keeps this role when an outside agent checks out.
- Multi-agent System
- Several AI agents with different roles coordinating to complete a workflow, the latest step in the retail lecture's evolution.
- Assistive Search
- Search that helps users complete knowledge work, described as the next frontier in the retail trends.
GenAI Use Cases in Marketing and Retail FAQ
How should I use the percentages quoted in the industry lectures?
As attributed illustrations. They come from vendor case studies, press reports or surveys, each with its own purpose and method, so in an essay name the source type, say what the number measures, and avoid treating one company's result as a general rate for the industry.
What makes an agent different from a chatbot in the retail examples?
Tools and a reasoning loop. A chatbot answers within the conversation, while an agent can call external tools, such as a social platform or a checkout, observe the result and decide what to do next. That is why the lecture's chatbot could write a post but not publish it until it was given tools.
What does a retailer need before joining agentic commerce?
Reliable product data and search first. The lecture's two steps are to build a data and search foundation with the retailer's own shopping and support agents, and only then to connect to outside agents through a shared protocol and payment integration, so that agents buying on customers' behalf receive accurate information.
What were the five retail trends presented in the lecture?
Multimodal AI that uses richer context, agents growing from single chatbots into coordinated systems, assistive search for knowledge work, customer experience so smooth it becomes almost invisible, and security that is both tighter and harder because attackers use the same tools.
Exam move
Make a one-page table of every named case in the marketing and retail lectures with three columns: what was done, which capability it used, and which KPI improved. Then practise one essay paragraph that uses a case as evidence while attributing its numbers correctly.
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