The Exam Room · AIB-C01
AI Business Strategist
The business side of AI: cases, governance, readiness, and scaling past the pilot. Preparing for the AI Business Strategist certification.
Exam Room · AI Business Strategist
Forty AI Tools Nobody Approved
An expenses review at a 1,400-person professional services firm turns up forty-one AI subscriptions bought in a year, most on personal cards, several holding client files. A blanket ban was issued eight months ago and broken twenty-four times since. A ban, a closed allow-list and a published three-state register all look like governance from the outside; only one of them gets people to tell you what they are actually running.
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Four Asks and One Budget
Four functions at a manufacturer each arrive with an AI ask, and the board has money for one of them. None can be priced until somebody names the capability each one actually needs, and naming it changes three of the four answers.
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Nine Thousand Refunds a Month
Nine thousand refunds a month get approved by hand, and two proposals want the same budget line: write the returns policy down as code, or train a model on the four years of past decisions already sitting in the case system. The business case for the model rests on that history being usable, and the decision turns on what each option costs to run rather than what it costs to build.
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Nobody Here Has Trained a Model
A food-service distributor with four hundred staff and no data scientists has five months left on its reporting contract and two things already promised to the business: an assistant that answers product and order questions for sixty reps, and a finance team that can ask the numbers a question without joining a three-day queue. Renting a model, training one, buying a packaged assistant and buying a vendor product are four different purchases, and what separates them is who runs the thing in year two.
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The Assistant That Has to Book the Van
A logistics operator has an assistant that explains why a delivery failed, and the ask on the table is for it to rebook the van itself. That is a different kind of system with a different approval behind it, and four capabilities separate the two. Naming them is what tells the board what it is actually agreeing to fund.
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The Damage Claim With Forty Photos
An insurer opens one storm-damage claim and finds forty phone photos of a roof, a twenty-two minute recorded call, a scanned assessor's report signed by hand, and a row with fifteen fields. Three uses are queued behind one budget, the business believes only the row is usable, and that belief is wrong at both ends: the photographs can be read today, and the field everybody trusts has meant two different things for six years.
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The Handbook the Assistant Never Read
An insurer's internal assistant answers 11,000 questions a week in the confident voice of a handbook that was replaced last April, and a partner has quoted AUD$310,000 to fine-tune a model on the company's documents. Writing a better instruction, handing the model the current handbook at question time, and training the model on company material fix different failures, reach an answer on different clocks, and leave the result in different hands.
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The Vendor Called It AI
Three proposals to forecast which depot runs short next week, and all three have AI on the cover. One is a scoring sheet, one trains a model on three years of despatch history, and one writes a paragraph. Shortlisting them means reading the decks in the right vocabulary, because algorithm, model, training, inference and prediction are doing different work in each.
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Cheat Sheet: AI Vocabulary and Solution Types
A last-pass revision sheet for AIB-C01 Domain 1: the words a proposal uses, the capability behind each business phrasing, what separates rules from a model and a workflow from an agent, and the traps that make a good-looking business case fall over. Tables to drill rather than prose to read.
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One subject a card: the five things that matter, when to pick it over the alternative, and why it is the wrong answer when it is.
Pop quizzes
Exam-style multiple choice, one question a card, with the reasoning behind the tempting wrong answer.
Exam Room · AI Business Strategist
Pop Quiz: Accurate in March, Wrong in November
A demand forecast launched in March at 91% accuracy reads 68% by November, with nothing deployed and no incident raised. The shape of the decline tells a stale model from a broken one.
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Pop Quiz: Ninety-Seven Per Cent of What?
An internal audit reports nine months of fraud screening at 97% accuracy. Two per cent of the transactions are fraudulent, so approving everything untouched would have scored 98%.
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Pop Quiz: Same Question, Different Answer
An operations lead saw a routing feature handle one test enquiry correctly three times, then wrote a service promise and a release check that both assume something the feature does not do.
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