MKTG90049 · Marketing, Society and Sustainability
The Future of Marketing
Week 12 looks ahead to how generative AI and emerging technology reshape marketing, drawing on Grewal et al. (2025) on generative AI and Puntoni et al. (2020) on consumers and AI, plus futures thinking (VUCA, scenario planning). It covers opportunities, risks and the ethical and sustainability responsibilities of AI-enabled marketing.
Note this recap week was flagged as not directly examined on the 2026 exam, though its concepts recur across the paper — it is most useful for the group report's forward-looking recommendations and for framing the future-facing parts of other answers.
What this chapter covers
- 01Futures and sustainability: futures are created, not just awaited; imagined futures help tackle wicked problems
- 02VUCA (Volatile, Uncertain, Complex, Ambiguous) as the context frame
- 03Scenario planning / strategic foresight and the four future archetypes (Growth, Constraint, Transformation, Collapse)
- 04Generative AI (Grewal et al. 2025): deep-learning models creating new output
- 05Analytical vs generative AI: predicting from past data vs creating new content
- 06The organising framework: nature of Gen AI inputs × extent of human augmentation; humans-in-the-loop
- 07Consumers and AI (Puntoni et al. 2020): data capture, prediction, autonomy and trust; persistent concerns (privacy, transparency, bias)
Short answer: generative vs analytical AI and the responsibilities of AI-enabled marketing
- +5Distinguish the two AIs (about 5 marks). Analytical (predictive/discriminative) AI analyses past structured data to predict outcomes and improve large-scale operations, and is relatively interpretable. Generative AI uses deep-learning models to create new content (text, image, video) from existing content. State the core difference: predict vs create.
- +4Opportunities with an example (about 4 marks). For a chosen brand: rapid content creation and personalisation at scale, faster concept testing, and augmenting (not replacing) creative teams. Name one concrete use (e.g. generating tailored campaign variants for different segments).
- +4Responsibilities and the organising framework (about 4 marks). Note the persistent concerns — privacy, transparency, data quality and keeping humans in the loop — and use the framework's two axes (nature of inputs × extent of human augmentation) to argue for appropriate oversight. Add sustainability: the energy/compute cost and the risk of driving more consumption.
- +2Evaluate through Better Marketing for a Better World + close (about 2 marks). One line placing responsible deployment in the win-win quadrant and irresponsible use (bias, manipulation) in win-lose. Keep to ~300 words.
Key terms
- Generative AI (Grewal et al. 2025)
- Deep-learning models that create new, novel output (text, image, video) from existing content — distinct from analytical AI, which predicts from past data. Reshapes content creation, personalisation and concept testing in marketing.
- Analytical vs generative AI
- Analytical (predictive/discriminative) AI analyses past structured data to predict future outcomes and is relatively interpretable, used to improve large-scale operations; generative AI creates new content. The core distinction is predict versus create.
- VUCA
- A frame for the contemporary context — Volatile, Uncertain, Complex, Ambiguous — used to explain why marketers need futures thinking and scenario planning rather than single-point forecasts.
- Scenario planning (four archetypes)
- Building multiple plausible-yet-surprising futures from the most influential and uncertain drivers of change, to 'think the unthinkable'. Four archetypal scenarios: Growth, Constraint, Transformation and Collapse (van Duijne & Bishop 2018).
- Consumers and AI (Puntoni et al. 2020)
- An experience framework covering how AI captures data, predicts, and affects consumer autonomy and trust, with persistent concerns around privacy, transparency and bias — the consumer-side counterpart to firms' AI opportunities.
- Humans in the loop
- The principle that human oversight and judgement must remain part of AI-enabled marketing decisions, to manage the persistent concerns (privacy, transparency, data quality, bias) that come with generative and analytical AI.
The Future of Marketing FAQ
Is Week 12 on the MKTG90049 exam?
the material flags Week 12 (the recap 'future of marketing' week) as not directly examined on the 2026 exam, so you should confirm the examinable scope for your own offering on Canvas. That said, its concepts recur — the analytical-versus-generative AI distinction, VUCA, scenario archetypes and the consumers-and-AI concerns show up as context in other answers, and they are directly useful for the forward-looking recommendations in the group report. Treat it as consolidation and application material rather than a standalone exam topic.
What is the difference between analytical and generative AI?
Analytical AI (also called predictive or discriminative) analyses past structured data to predict future outcomes — think forecasting demand or scoring leads — and is relatively interpretable, so it is used to improve large-scale operations. Generative AI uses deep-learning models to create new content (text, images, video) from existing content. The crisp exam line is predict versus create. A good answer then notes that generative AI brings persistent concerns — privacy, transparency, data quality and the need to keep humans in the loop — plus a sustainability cost in energy and compute.
Why does the subject cover 'futures' and scenario planning?
Because sustainability is inherently about the future (future generations), and wicked problems cannot be solved with single-point forecasts. Futures thinking treats the future as something created, not merely awaited: VUCA describes why the context is hard to predict, and scenario planning builds several plausible futures (Growth, Constraint, Transformation, Collapse) so marketers can 'think the unthinkable' and identify robust action pathways. It gives the group report a disciplined way to justify forward-looking sustainability recommendations rather than assuming business as usual.
Can AI help me with Week 12 of MKTG90049?
Yes, as a study aid. Sia can drill the analytical-versus-generative AI distinction, VUCA and the four scenario archetypes, and check that you name genuine responsibilities of AI-enabled marketing rather than only benefits. Ask it to structure a future-of-marketing answer step by step. It does not write your graded report or answer, and University of Melbourne academic-integrity rules apply.
Exam move
Treat Week 12 as consolidation and report fuel rather than a heavy exam-revision target, but confirm the examinable scope on Canvas. Learn the crisp distinctions — analytical (predict) versus generative (create) AI, VUCA, and the four scenario archetypes (Growth, Constraint, Transformation, Collapse) — because they recur as context across the paper and strengthen the group report's forward-looking recommendations. Practise evaluating an AI-enabled marketing move for both opportunities and responsibilities (privacy, transparency, humans-in-the-loop, sustainability cost) and judging it through Better Marketing for a Better World. When the AI concepts feel hand-wavy, ask Sia for a concrete scenario and check that you balance benefit against genuine responsibility; it teaches the method and never does your graded work. Confirm assessment details on Canvas.
Working through The Future of Marketing in MKTG90049? Sia is AskSia’s AI Marketing tutor — ask any MKTG90049 The Future of Marketing question and get a clear, step-by-step explanation grounded in how MKTG90049 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
