Tech Tempest is a team-based competitive simulation where participants design, build and defend a digital banking bid, making real strategic decisions on technology, risk and competitive positioning. The scenario is banking. The learning is universal: strategy, risk, AI literacy and teamwork, capabilities every organisation needs.
Eight civilisations, eight strategic capabilities. History becomes a lens for modern decisions.
Research, analyse, evaluate, then apply human judgement. AI literacy through structured practice.
Real trade-offs in strategy, infrastructure, pricing and AI governance. Every choice has a consequence.
The four phases follow Kolb's experiential learning cycle (1984).
How each element connects to established theory.
Historical analogies shape how leaders judge and act on current problems. Tech Tempest makes this process explicit: each civilisation embodies a strategic capability that participants apply to modern challenges.
Participants assigned to historical factions with committed perspectives report higher engagement, self-efficacy and perceived learning than those in conventional courses.
Civilisation identities create cognitive scaffolds. Participants remember that they are the Byzantine Architects and their strength is quality long after they have forgotten the point values. The narrative carries the learning.
What customers say is not what they do. AI surfaces stated preference; judgement finds revealed behaviour.
Critical evaluation of AI-generated output. Is it behavioural proof, or stated intent?
A four-step AI interaction framework: ask, give context, probe every answer, own the judgement.
Three-tier strategic design: essentials, then differentiators, then signature innovation. A synthesis of Kano, Porter and McKinsey Three Horizons.
Two-dimensional portfolio analysis. Fit against evidence guides feature selection into Priority, Strategic Bet, Consider or Avoid.
Strategy as a deliberate choice, not an accidental outcome. Participants choose their shape before building.
Risk as a quantifiable function of decisions. The committee prices every bold choice.
AI governance as a design decision. The hidden cost appears at governance review, not at build time.
Breaking complex programmes into releases, and managing the trade-off between speed and execution risk.
Team-based learning methodology, with individual accountability inside team performance.
C-suite perspectives and cross-functional diversity.
What you leave out shapes what you see.
Problem-based learning and narrative pedagogy.
An original teaching metaphor that synthesises Kano, Porter and McKinsey Three Horizons into a single visual framework, accessible to secondary participants and senior executives alike.
AI feature selection framed as a risk management decision, where the cost appears at governance review rather than at build time. Grounded in NIST AI RMF and the EU AI Act.
A four-step AI interaction framework, ask, give context, probe every answer, own the judgement, built on UNESCO's AI Competency Framework for structured, critical AI use.
Every participant finds a professional lens that matches how they think, from engineer to philosopher. Built on Belbin's team roles, Hambrick's Upper Echelons, and T-shaped skills research.
The core works the design stands on. A fuller reading list is available on request.