Module 7 · AI in the Real World — Use Cases & Value
Where AI Creates Value (and Where It Doesn't)
70 min
Learning objectives
- Identify the characteristics that make a task a strong candidate for AI
- Recognize warning signs of a poor-fit AI use case
- Frame any proposed use case in terms of a measurable business outcome
- Distinguish automation opportunities from augmentation opportunities
Start with the problem, not the technology
The most common reason AI projects fail is that they start from 'we should use AI' rather than 'we have a costly, repetitive, or slow problem.' A practitioner's first move is to name a specific business outcome — fewer late shipments, faster ticket resolution, lower fraud losses — and only then ask whether AI is the best tool to move that number.
A good use case is a problem with a measurable outcome that AI is well-suited to improve — not a technology looking for a place to live.
What makes a strong AI use case
AI shines on tasks that are repetitive, pattern-rich, and tolerant of occasional errors, where good historical data exists and the value of getting it right (or doing it faster) is clear. The more of these traits a task has, the better the fit.
| Characteristic | Why it favors AI | Example |
|---|---|---|
| High volume / repetitive | Per-task savings multiply across many cases | Classifying support emails |
| Rich, relevant data exists | Models learn patterns from examples | Years of labeled transactions for fraud |
| Patterns too complex for fixed rules | AI infers patterns humans can't easily hand-code | Detecting defects in product photos |
| Errors are tolerable or recoverable | Occasional mistakes don't cause severe harm | Recommending products |
| Clear, measurable outcome | ROI can be proven and tracked | Reduce average handle time by 20% |
Automation vs. augmentation — Automation lets AI complete a task end-to-end; augmentation lets AI assist a human who keeps control. The right choice depends on error cost and accuracy.
Analogy
Think of AI like a power tool. A nail gun is fantastic for framing a house — high volume, repetitive, forgiving of a slightly imperfect angle. You would not use it for surgery. The tool is excellent; matching it to the right job is everything.
Where AI is a poor fit
Some tasks look attractive but are bad bets. Be honest about these — recommending against AI when it doesn't fit is a sign of competence, not weakness.
- Rare, one-off decisions with no data to learn from (e.g., 'should we enter this brand-new market?').
- Tasks where a single error is catastrophic and unrecoverable, with no human check (e.g., irreversible financial transfers without review).
- Problems a simple rule or spreadsheet already solves — AI adds cost and fragility for no gain.
- High-stakes regulated decisions requiring legally-binding explanations the model cannot reliably give.
- Cases where the underlying data is biased, sparse, or unrepresentative of the population you'll serve.
Watch out
A frequent trap: deploying AI on a problem a deterministic rule already handles perfectly. If 'if balance < 0, flag account' works, you don't need a model — you've just added cost, latency, and a new failure mode.
Example — Reframing a vague request
A manager says 'let's add AI to our hiring.' Reframed: 'We spend 40 hours/week manually screening 2,000 resumes for keyword matches.' That is high-volume, repetitive, and data-rich — a plausible augmentation use case (surface likely matches for a recruiter to review), but with a serious fairness risk that demands human oversight and bias testing.
Always pair a use case with its risk profile. High value plus high error-cost usually means augmentation with a human-in-the-loop, not full automation.
Knowledge check
Quick practice — not part of your exam score.
Which task is the strongest candidate for an AI solution?
A recruiting team wants AI to screen resumes. Given the high error-cost (fairness/legal) of hiring, which design is most appropriate?
What is the best first step when evaluating a proposed AI use case?
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