Module 1 frameworks applied against real organisations, real decisions, and real failures — not restated, not illustrated, but used as analytical instruments that produce findings.
Module 1: AI Foundations — 5 cases
Module 2: How Machines Learned — 6 cases
Module 3: Business Value and Strategy — 6 cases
Module 4: Challenges in AI Implementation — 6 cases
Module 5: Ethical Considerations and Responsible AI — 6 cases
Module 6: AI Project Management — 6 cases
Module 7: AI in Marketing, Sales & Customer Experience — 6 cases
Module 8: AI in Operations & Supply Chain — 6 cases
Module 9: AI in Human Resources & Organisational Processes — 6 cases
Module 10: Managing & Exploring AI Capabilities and Adoption — 6 cases
Module 11: AI-Driven Innovation and Emerging Technologies — 6 cases
Updated: 15 August 2026
Smart search — find cases and concepts
How these cases work — applied vs illustrative
Each case uses the week's framework as an instrument — running it against a real-world scenario to produce a finding. The finding is the point, not the framework explanation.
Applied (what these cases do)
Framework → real data → finding. The Innovare Index's 10 models applied to He/Cao/Tan produces: every current model is Stage 4, yet 4 capability levels of empty space sit above them. That's a result. Robodebt run through Wilson & Daugherty produces: zero augmentation criteria passed, $1.76B cost.
Illustrative (what these cases avoid)
Explaining the framework, then giving a generic example that merely restates it. "Netflix uses recommendation algorithms — this is an example of the filter bubble." That's description, not analysis. The framework has to produce something the source material didn't already say.
Module 1 · AI Foundations · 5 cases
What is AI, how did it actually develop, and what are the ethical and human implications?
Frameworks from He, Cao & Tan (four-stage model); ANI/AGI/ASI taxonomy; Morris et al. (DeepMind AGI levels); Netflix ethics template; Wilson & Daugherty (augmentation); Pariser (filter bubble).
Module 4 · Challenges in AI Implementation · 6 cases
Why is enterprise AI adoption so hard — and what does it actually take to succeed?
Concepts from VUCA (Bennett & Lemoine, 2014); Technology Acceptance Model (Davis & Bagozzi, 1989); TOE Framework (Tornatzky et al., 1990); Booyse & Scheepers (2024) barriers to AI in decision-making; Mollick (2024) secret cyborgs, crowd vs lab; Russell Reynolds H2 2023 Global Leadership Monitor (N=1,287).
Module 5 · Ethical Considerations and Responsible AI · 6 cases
When AI makes decisions that affect real people — whose name is on that decision?
Concepts from Mikalef et al. (2022) dark side of AI; Buolamwini & Gebru (2018) Gender Shades; Carroll et al. (2020) CARE Principles; IBM Responsible AI framework; EU AI Act (2024); Australia NAIC Essential Practices (2025); Tsoukalas & Falk (2025) AI Layoff Trap; Royal Commission into Robodebt (2023).
Module 5 · Short Video
Module 5 · Long Form · Whose Name Is On That Decision?
AI projects don't fail on the technology — so what actually decides whether they succeed?
Concepts from Miller (2025) framework for avoiding harms, losses & damages; the PM lifecycle & PDCA (Deming/Shewhart); project charters, SMART/OKRs/KPIs, Scrum for AI; the Atlassian set case; RAND (2024) & Bernard Marr on why ~80% fail; Goodhart's Law; model drift; the EU AI Act (Art. 72 & 99). Real wrecks: the Deloitte welfare report, Commonwealth Bank, and Zillow.
Module 7 · AI in Marketing, Sales & Customer Experience · 6 cases
The upside of AI marketing is real — so where does it quietly mislead, and what should you actually measure?
The three pillars, the AI-marketing definition, five impact areas & five IBM benefits; generative content and the Coca-Cola AI ad; the set Coca-Cola case audited; Sora, deepfakes & C2PA provenance; the sales funnel’s real history (Lewis, 1898; falsified by Vakratsas & Ambler, 1999) and Google’s “messy middle”; AI & customer experience — Netflix, journey mapping, CommBank & Kathryn Sullivan, Klarna, Sierra & Lorikeet; and the required Kumar, Ashraf & Nadeem (2024) reading — dynamic capabilities, six themes, ethics 5.28/7, algorithmic bias, and the APP 1.7 disclosure date (10 Dec 2026).
Module 8 · AI in Operations & Supply Chain · 6 cases
The textbook makes AI in supply chains sound solved — so what’s proven, what’s hype, and where do the experts actually disagree?
How a supply chain really works (SCOR’s six processes) and why a small wobble becomes a huge swing (the bullwhip effect, Forrester 1961 / Lee 1997); the real AI toolbox — forecasting, inventory, predictive maintenance, routing, negotiation, visibility — and machine learning vs genuinely generative AI; the one peer-reviewed study that went inside six factories (Cannas et al., 2024) and found AI landing on the factory floor, not the clever planning layer; a three-question audit for any vendor statistic, and the five walls that keep AI from being everywhere (data quality is blocker #1); Walmart honestly — the Pactum negotiation bot, the scrapped Bossa Nova shelf-robots, the quiet Element platform; and the frontier the textbook skips — who built these ideas and whether they still hold, OR vs AI (“predict, then optimise”), the modern stack (digital twins, deep reinforcement learning, causal & agentic AI, knowledge graphs), the real movers & shakers (Kinaxis, Blue Yonder, o9, Amazon SCOT, Palantir, Symbotic), the ~95% failure reality, and resilience vs efficiency under geopolitics.
Module 8 · Long Form · What AI Actually Does in Supply Chains
Module 9 · AI in Human Resources & Organisational Processes · 6 cases
The textbook says AI makes HR fairer and faster — so why did the hiring algorithms end up in court, and who is accountable when a model says no?
How AI enters HR — recruiting, performance management and employee engagement (Oracle, IBM, KPMG, Gartner) — and where it went to court: Mobley v. Workday's agent-liability theory, iTutorGroup, Amazon's scrapped tool. The algorithmic boss and digital Taylorism (Microsoft's rolled-back Productivity Score, the Cornell resistance finding, the Progress Principle); wellbeing-washing and the ghost-jobs economy; the KPMG four-step / Gartner playbook and who it is selling to; then the organisation half — People/Process/Technology, the TOE readiness framework, the InfoTech roadmap and scaling; the required reading (Murire 2024) audited, the Australian disclosure rules from Dec 2026, and the spine that ties it together: it's people, not the AI.
Module 9 · Short · The Algorithmic Boss
Module 9 · Long Form · The Algorithmic Boss (Short-Doc)
Module 10 · Managing & Exploring AI Capabilities and Adoption · 6 cases
The frameworks are thirty years old and the tools are two years old — so what still holds, what is just vendor gloss, and why does running everything through AI make everyone answer the same?
How organisations actually manage and adopt AI. The seven aspects of managing AI and Drucker's definition, taught through Air Canada's liable chatbot; the two theory lenses — Teece's dynamic capabilities (sense/seize/reconfigure), read through Kodak, and Cohen & Levinthal's absorptive capacity, read through Zillow's half-billion-dollar collapse; the required reading (Hossain et al. 2025) and its six leader capabilities, with managers — not technologists — leading the transformation (Iansiti & Lakhani); the EY responsible-AI case audited against its nine principles and $100M fine, plus IBM / NIST / EU AI Act governance (and the 2026 deferral to Dec 2027); and the other side — augmentation done rigorously (Bainbridge, Zuboff, Brynjolfsson, Autor, Klein, Elish) and the consensus-machine evidence (Doshi & Hauser, Kleinberg, StoryScope) that says consensus is not a strategy.
The word everyone owns and no one defines — what “innovation” really means, why the tool that dazzles us on the frontier is a small specialist model and not a giant brain, and the question underneath all of it: good for whom?
The two definitions of innovation — the OECD/Oslo-Manual measure versus IDEO's human-centred need, kept honest by MIT's user-innovation lens (von Hippel, Aulet); the industrial arc from steam to Industry 5.0 (human-centric, sustainable, resilient — European Commission 2021), grounded in the Factory of the Future at Flinders; the innovation helix (Etzkowitz & Leydesdorff) extended to civil society, and Society 5.0; the science spine — protein folding, AlphaFold (Nobel 2024), rational drug design, the 1060 chemical space and the drug-cost funnel — and why the frontier runs on checked specialist tools; the eight emerging technologies sorted into real, frontier and marketing; and the other side — job displacement, bias, accountability and the self-driving question, weighed with Khogali & Mekid: good for whom?