MGMT8005 Chap.6 AI Prototypes and Iterative Design
AI Prototypes and Iterative Design
The captured Week 4 workflow uses AI app builders to move from a digital-innovation idea through a first prompt, three or four iterations, a business-model connection and a sixty-second team demonstration. The enduring lesson is experimental design, not any vendor's current credits or features.
A prototype should answer a decision, not perform novelty.
A prototype can make an interaction or workflow concrete quickly, but technical output is not proof of demand, viable delivery or safe governance. State the uncertainty before building.
The team may need to learn whether a user understands the job, whether information is sufficient, whether a recommendation can be executed or whether a pattern fits the model.
A clickable flow may be enough to test navigation; a simulated recommendation may test explanation; a manual back end may test a service workflow. Building live integrations too early increases time and risk without improving the relevant evidence.
Fidelity should match the question.
Specify what observation would support, revise or stop the idea. If every reaction is treated as encouragement, the prototype becomes persuasion. A rule might require users to complete a critical task without help, identify a data gap or prefer the proposed workflow to a credible alternative.
Record limits and do not generalise from a convenience sample.
List the assumptions in the proposed innovation and rank them by uncertainty, consequence and cost to test. A paper flow can expose a misunderstood task; a role-play can reveal a broken hand-off; a simulated recommendation can test whether explanation supports action. None requires production data.
Higher fidelity becomes justified when the next decision concerns integration, reliability or actual behaviour under realistic conditions. The team should also identify prototype risk: participants can believe a polished interface has real intelligence or authority. Label simulation, use fictional or authorised data and debrief what was not tested. This makes the artifact an honest instrument of inquiry.
The best early prototype often looks incomplete because it isolates the assumption whose failure would change the business-model choice.
Begin with a bounded idea linked to the organisation and a consequential decision. “Build an AI app for customer service” is too broad. “Help a service agent choose the next resolution path from customer context while preserving escalation” identifies actor, action and boundary.
The prototype can now represent a workflow and test information or interaction.
Use company and customer evidence to establish the problem. Separate organisational statements from observed outcomes. Identify the current process, friction and alternative.
If the assigned company evidence does not support the problem, do not invent a need because the technology is attractive.
The prototype might test creation—whether the job matters; delivery—whether the workflow can perform; capture—whether the decision supports a viable relationship; or governance—whether users understand and can contest it.
One build can surface several issues, but it should have a primary learning question.
The group must make its own choice and analysis under the current brief. This guide provides a method, not an assigned company idea.
Preserve group records, source decisions and contributions because half the task total may be individually adjusted when performance differs materially.
Use six fields: actor, situation, current decision, changed action, expected outcome and organisational value. Then add one constraint and a comparison.
For example, an associate facing an unavailable item chooses an approved alternative, reducing abandonment without promising stock or terms outside policy, compared with manual searching. This statement determines the screens and evidence. It also reveals what the prototype cannot establish: inventory accuracy, commercial impact and policy fairness may require later tests.
If the chain contains several decisions, choose the earliest uncertain one or split the flow. A bounded idea lets the group defend why each design element exists and prevents a builder's suggestions from silently redefining the innovation. Before building, verify that the company evidence supports the current friction and that the intended user has authority to take the changed action.
If either fails, revise the idea rather than adding interface detail.
Name the primary user, context and action. A manager approving an exception needs different information from a customer exploring options. Specify the entry state, important choices and completion condition.
If several users exist, choose one flow first and map hand-offs explicitly.
List the minimum views needed to understand context, make or receive a recommendation, act and see confirmation. Include error, uncertainty and escalation rather than only the happy path.
A screen that presents a confident result without source, reason or appeal can hide the highest-risk part of the innovation.
Specify what the prototype assumes exists, who supplies it and whether it is appropriate to use. Synthetic data can demonstrate a flow but cannot prove availability or quality. Avoid copying personal or company-confidential information into a builder.
Mark simulated fields and identify the future governance requirement.
For each view, define what the user knows, what choice is available and what changes after action. Include normal, missing-data, conflicting-data, no-permitted-action and escalation states. This state model tests whether the proposed decision can be executed responsibly.
The prompt should distinguish displayed data from inferred or generated content and require visible provenance where users need it. It should also preserve accessibility, device and language assumptions. Ask the builder to expose reusable data objects and transitions rather than inventing a broad backend.
Review generated fields carefully: a plausible interface can introduce sensitive attributes or fabricated certainty that the idea never authorised. The first version is successful when its assumptions are easy to inspect and revise, not when it appears production ready. Create a short data register with field, source, sensitivity, freshness and simulation status.
Check whether a user can recognise stale or missing context and whether the flow preserves a reason for escalation. These details turn the prompt into a testable representation of decision requirements. Version the prompt with the prototype.
If the team changes navigation, content, model output and colour at once, an improved reaction cannot be explained.
Select the bottleneck most relevant to the hypothesis, predict what should happen and change that element. Cosmetic work is appropriate only when comprehension or trust is the question.
What this chapter covers
- 01
Prototype
- 02
first prompt
- 03
controlled iteration
- 04
learning question
- 05
business-model connection
- 06
evidence ladder
- 07
Evidence, alternatives and governance
- 08
Original worked application and chapter synthesis
AskSia-authored practice weighting (not an official mark scheme): AI Prototypes and Iterative Design
- 2 AskSia pointsDefine the focal decision and apply Prototype precisely.
- 2 AskSia pointsUse evidence to test first prompt rather than assert the label.
- 2 AskSia pointsTrace the mechanism through controlled iteration and the affected actor.
- 2 AskSia pointsCompare the nearest alternative and state a boundary using learning question.
- 2 AskSia pointsRecommend a bounded next decision with owner, validation, counter-metric and stop rule.
Key terms
- Prototype
- A deliberately incomplete artefact used to test one important innovation assumption.
- first prompt
- A design hypothesis specifying user, purpose, screens, state, interaction and constraints.
- controlled iteration
- One recorded design change tied to a prediction and observable evidence.
- learning question
- The primary uncertainty that determines what the prototype must represent and test.
- business-model connection
- The causal link from prototype action to creation, delivery, capture or governance.
- evidence ladder
- A progression from representation through comprehension, operation, outcome and model viability.
AI Prototypes and Iterative Design FAQ
What does Prototype mean in this guide?
A deliberately incomplete artefact used to test one important innovation assumption.
What does first prompt mean in this guide?
A design hypothesis specifying user, purpose, screens, state, interaction and constraints.
What does controlled iteration mean in this guide?
One recorded design change tied to a prediction and observable evidence.
What does learning question mean in this guide?
The primary uncertainty that determines what the prototype must represent and test.
What does business-model connection mean in this guide?
The causal link from prototype action to creation, delivery, capture or governance.
What is the nearest mistake to avoid?
Do not use AI Prototypes and Iterative Design as a label detached from actor, action, evidence and outcome. Apply the chapter's mechanism and state what would change the conclusion.
Are the worked examples official Macquarie questions or marking schemes?
No. They are independently authored AskSia learning drills. The 10 points are an AskSia planning scaffold, not official marks, questions, answers or rubric criteria.
How should this chapter be used in assessment work?
Verify the current iLearn brief, use company-specific evidence, apply only the concepts that explain the mechanism and preserve individual or group authorship required by the task.
Assessment move
Ask what the prototype can establish. Interaction tests can support comprehension and workflow claims. Synthetic data cannot establish availability, bias or predictive quality. A builder's successful output cannot establish security, integration or economics. Put each unresolved assumption into the next-decision list.
Builder names, free tiers, credit limits and product capabilities can change.
Treat them as dated workshop context and confirm current availability. The evergreen skill is translating an innovation hypothesis into a testable flow and learning cycle.
For the retail prompt, the revised flow should test one primary question such as whether an associate can understand and apply approved alternatives without overpromising. Define a task, participant, current comparison, observation and decision rule.
A successful completion requires an allowed resolution and correct escalation when no option fits; speed alone is insufficient. Mark inventory and policy as synthetic, and record every field that an operating service would need. If participants hesitate, diagnose whether context, explanation, option structure or authority is responsible before changing the interface.
This exercise turns prompt quality into experimental quality. It also keeps current vendor details outside the lasting knowledge claim: another builder can implement the same bounded state and evidence design. Include one participant who is unfamiliar with the proposed terminology and one exception that has no approved alternative.
Their behaviour tests whether the flow supports genuine judgement rather than recognition of the designer's intended path.
Stop or redesign when the job is unsupported, the decision cannot be bounded, required data is inappropriate, the workflow transfers unacceptable risk or the mechanism fails its evidence threshold.
A rapid build makes it cheaper to stop; it does not create an obligation to proceed.
Many digital innovations depend on actors and capabilities outside one firm.
The next chapter asks when bilateral contracting or a linear value chain is insufficient and an ecosystem becomes a deliberate governance choice, using modularity, customisation, multilateralism and coordination as decision criteria.
Distinguish five claims: the interface can be represented, users can understand it, the workflow can operate, the mechanism changes an outcome and the business model can sustain it.
Evidence at one level permits only the next proportionate commitment. A generated application usually supports the first claim; observed tasks may support the second; a bounded pilot may support the third. Outcome and viability require longer, governed evidence. For every demo, state the current rung, unresolved assumption and next test with a stop rule.
Preserve version history and group decisions so learning is attributable and defensible. This ladder prevents speed from becoming overconfidence and helps the team connect a small prototype to strategic analysis without claiming that technical output proves demand, safety or capture. At each transition, name the new data, permission, operational resource and affected-party safeguard required.
A stage gate is meaningful only when failure can delay, redesign or stop the commitment.
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