Decoding Scenarios With a Requirement Ledger

Throughout this course, one fictional scenario carries every lesson: the complaint backlog case. A company’s service team has a large backlog of customer complaints, and a stakeholder wants AI to help. We use a single case for three reasons. First, you apply each new idea, such as data quality, solution type, KPIs, ROI, governance, and scaling, to the same problem, so you see how the decisions connect. Second, it mirrors how AIB-C01 questions are written: short business scenarios with missing information. Third, complaint handling is familiar in any industry, so you can focus on the decision instead of learning a new domain. The company has no name; we call it “the company” and its project “the complaint pilot.”

In the complaint backlog case, a stakeholder asks the service team to “use GenAI to cut complaints.” The team has a large complaint backlog, applies categories inconsistently, and cannot state what counts as resolved. The request names a technology, but it does not yet state a decision-ready business problem.

A requirement ledger is a nine-field record that separates stated scenario facts from information we still need before recommending an approach. It keeps our reasoning focused on the decision rather than on the most impressive-sounding technology.

The nine fields are stakeholder, workflow, measurable outcome, baseline, data, constraints, risk, owner, and unresolved assumptions. Together, they reveal whether a scenario supports an AI decision or first requires clarification.

Inspect the requirement boundaries before treating a proposed technology as the solution.

Recording the complaint backlog facts

Let’s create the first entry for the complaint backlog scenario using only the facts provided. The stakeholder is the unnamed requester who asked for GenAI, and the service team operates the workflow. The workflow is complaint intake, categorization, and resolution. The stated outcome is to cut complaints, although that phrase remains too vague to measure.

The backlog is large, categorization is inconsistent, and resolution definitions are unclear. Those are useful facts, but none establishes the current complaint volume, backlog age, resolution rate, or customer impact. We cannot create a baseline from a vague target.

A proposed tool is not an outcome. “Use GenAI” describes a possible means, while a useful outcome might measure fewer repeat complaints, faster resolution, more consistent categorization, or another agreed business result. The case has not selected among those possibilities, and they are not interchangeable: cutting complaints, clearing the backlog, and improving categorization each need a different measure, so the outcome must be agreed first.

Inspect which entries are facts and which remain unresolved before choosing an approach.

Choosing the best next step

A best next step is the action that removes the most important decision gap before an organization commits to an approach. The ledger lists six unresolved items, and three of them block the decision most. No baseline shows what improvement would mean. No owner can accept accountability for the outcome. No confirmed data access shows whether the complaint records can support evaluation. The vague outcome is clarified first because a baseline and an owner both depend on it, and data quality is checked once access is confirmed.

When an exam scenario leaves gaps of that kind, the strongest answer usually clarifies the outcome, establishes a baseline, confirms data availability, or assigns accountable ownership. Selecting a tool immediately would skip the evidence needed to judge value, feasibility, and risk.

Consider how this appears in an AIB-C01 question. A retail company wants to use GenAI to reduce complaint volume. Complaint records exist, but the company has no baseline, no named owner, and no confirmed data access. Which action is the best next step?

The strongest answer is to establish a baseline for complaint volume and resolution, confirm data access, and name an accountable owner. An option that deploys a GenAI assistant or selects a larger model is a distractor because it chooses a tool before the scenario shows what success means or whether the evidence exists.

Once the ledger is sufficiently complete, the decision changes. We can then compare possible approaches against the agreed outcome, constraints, and risks rather than treating AI as the starting point.

Predict whether the missing evidence requires clarification first or supports an approach decision now.

Recognizing requirement-led answers

In an AIB-C01 scenario, options that immediately select a tool are likely distractors when the stem lacks a baseline, accountable owner, or confirmed data. A technically plausible proposal still fails if it cannot show what success means, who owns it, or whether the required evidence exists.

Prefer an option that matches the missing field. Clarify the business outcome when the target is vague. Establish a baseline when improvement cannot be measured. Confirm data access and quality when the proposed use depends on records that may be unavailable or unreliable. Assign an owner when no party can make or sustain the decision.

Use the ledger as a reusable selection rule: choose technology only after the scenario establishes a measurable outcome, relevant evidence, accountable ownership, and acceptable constraints. If one of those foundations is missing, choose the action that closes that specific gap first. Once the ledger is filled, we can judge data readiness and compare solution types to choose an approach for the complaint backlog case.