D1 · Finding and Scoping Opportunities

This domain is worth 16% of the mock — roughly 8 of 50 items. It tests whether you can look at the work in front of you and decide what to automate first, before you touch a prompt. The Applied AI course opens here for a reason: the most common failure of a competent prompter is not writing bad prompts, it is building repeatable workflows for the wrong tasks — high-visibility, low-frequency ones that never pay back, or high-sensitivity ones that should never have left a human’s hands.

What you need to know

An automation opportunity is a recurring task whose value, risk and data profile make it worth turning into a repeatable workflow. The screen has four dimensions: how frequently the task happens, how much time each instance costs, how much error the task can tolerate, and how sensitive the data is. High frequency and high time cost pull toward automating; low error tolerance and high data sensitivity pull toward caution and heavier oversight (or not automating at all). Scoping means writing down the trigger, the inputs, the output and the definition of done before building, so you are solving a real bounded problem rather than a vague wish.

Learning objectives

By the end of this page you should be able to:

1. Screen a recurring task against frequency, time cost, error tolerance and data sensitivity to decide whether it is a good automation candidate.
2. Rank several candidates so you automate the one with the best payback and acceptable risk first.
3. Recognise the anti-patterns — the impressive-but-rare task, the irreversible task, the sensitive-data task — that look like opportunities but are not.
4. Scope a chosen opportunity into a bounded problem statement with a trigger, inputs, output and definition of done.
5. Distinguish the part of a task that is genuinely repeatable from the part that needs a human every time.

1.1 The four screening dimensions

Every candidate task is scored on four axes. Two of them measure value; two measure risk.

Value is roughly frequency × time cost — a task that happens 40 times a week and takes 20 minutes each is 13 hours a week, an obvious target. Risk is governed by error tolerance and data sensitivity — and risk does not reduce the value, it changes how you build (more review, more sampling, sometimes a human kept fully in the loop).

1.2 A mental model for the screen

Plotting value against risk turns the four numbers into a decision.

                 HIGH VALUE (frequent × time-consuming)
                              ▲
        Automate with         │        Automate now —
        strong oversight:     │        the obvious win.
        review gates,         │        Lightweight review.
        sampling, kept        │
        human-in-the-loop     │
   ───────────────────────────┼───────────────────────────►
        LOW RISK              │              HIGH RISK
        Skip or defer —       │        Usually do NOT
        automating a rare,    │        automate end-to-end;
        cheap task rarely     │        assist a human instead,
        pays back.            │        or leave it manual.
                              ▼
                 LOW VALUE (rare × quick)

The top-right quadrant — high value and high risk — is where judgment matters most: the task is worth automating but a mistake is expensive, so you automate the drafting and keep a mandatory human review (the topic of Domain 5).

1.3 Good candidates, with worked examples

Good candidates are frequent, time-consuming, tolerant of small errors, and touch low-sensitivity data.

1.4 Bad candidates, with worked examples

Bad candidates fail on frequency, on risk, or both. The trap is that some of them look impressive.

1.5 Ranking candidates when you can only build one

When several tasks pass the screen, you still build one first. Rank by expected payback adjusted for risk.

A task saving 10 hours a week with a light review beats a task saving 12 hours a week that needs a human to check every single item — the second one’s net saving is smaller once you subtract the review labour.

1.6 Scoping the chosen opportunity

Screening picks what. Scoping defines the bounded problem so you can build it. A scoped opportunity has five parts.

TRIGGER        When does this workflow run? (a new ticket, Friday 4pm, a file arrives)
INPUTS         What does it need to start? (the ticket text, the thread list, the file)
OUTPUT         What exactly does it produce? (a drafted reply, a one-page digest, a table)
DONE           How do we know it worked? (reply passes review, digest posted, table imports)
BOUNDARY       What is explicitly out of scope? (no refunds, no medical advice, no sending)

Writing the boundary is the step people skip, and it is where scope creep starts. “Draft the reply” quietly becomes “and send it” and “and issue the refund” unless the boundary says otherwise.

1.7 Separating the repeatable part from the human part

Almost no real task is 100% automatable, and pretending it is produces brittle workflows. The skill is to find the stable core — the part that is the same every time — and route the variable judgment to a person.

The larger and more variable the human part, the smaller the payback — which feeds back into your ranking in 1.5.

Decision framework

The FTED opportunity screen — Frequency, Time cost, Error tolerance, Data sensitivity. Score each candidate 1–3 and read the guidance.

How to read the totals:

The framework’s value is that it forces the risk axes to be scored separately from value, so a high-value task cannot bully its way past a data-sensitivity problem.

Common mistakes

Scenario challenge

Scenario. Priya leads a 12-person operations team. She has an afternoon to pilot AI on one recurring task and wants a quick win the team will notice. Four candidates are on the table. (1) The quarterly board report — highly visible, the CEO reads it, takes a full day to assemble four times a year. (2) Vendor-invoice categorisation — 200 invoices a week, ~3 minutes each, currently done by hand, occasional mis-categorisation is caught at month-end reconciliation, data is internal financial. (3) Refund approvals — 60 a week, currently a manager approves each after reading the case; irreversible once issued. (4) Weekly customer-health summaries — pulled from internal notes every Monday, ~90 minutes, low consequence if slightly off, internal data. Her instinct is the board report, because “that is the one leadership will see”.

Expert reasoning trace.

1. Resist the visibility trap. The board report is high-value per instance but frequency is 4×/year, so a workflow built for it runs four times and the build cost never amortises. It is the classic impressive-but-rare candidate — assist it manually, do not automate it. Priya’s instinct is exactly the mistake this domain warns about.
2. Score the frequent candidates on value. Invoices: 200 × 3 min = 10 hours/week. Health summaries: 90 min/week. Refunds: 60 cases, real time, but the approval is the point. Invoices win on raw value.
3. Apply the safety axes. Invoices are internal financial with errors caught at reconciliation — medium error tolerance, so automate the categorisation with sampling at month-end rather than a gate on every invoice. Refunds are irreversible and financial — error tolerance is low, so you must never automate the approval itself; you could automate the case summary the manager reads, but that is a smaller, different win. Health summaries are low-risk and easy.
4. Rank by risk-adjusted payback. Invoice categorisation: ~10 h/week saved, light sampling review, internal data — top of the list. Health summaries: ~1.5 h/week, trivial risk — a fine, safe second pilot but smaller. Refunds: keep human; automate only the summary later. Board report: not a workflow at all.
5. Scope the winner. Trigger: invoices arrive in the shared inbox. Input: invoice fields. Output: a category label per invoice. Done: month-end reconciliation error rate no worse than today. Boundary: the workflow labels, it never pays or approves.

The decision: pilot vendor-invoice categorisation with month-end sampling, not the board report. It has the best risk-adjusted payback, low-to-medium risk handled by sampling, and a clean boundary. The board report is a manual-with-assistance job; refunds keep their human approval. The “quick win the team notices” is real precisely because invoices are the daily grind, not the quarterly showpiece.

Assessment traps

Practice questions

Each item states how many responses to select. Commit before revealing.

Key takeaways

• Screen every candidate on FTED: Frequency, Time cost, Error tolerance, Data sensitivity — two value axes, two risk axes.
• Value ≈ frequency × time cost. Importance and visibility are not value; the impressive-but-rare task rarely pays back a workflow.
• Risk changes how you build, not whether the task has value. High-value + high-risk means automate the drafting and keep a human gate.
• Rank candidates by risk-adjusted payback — subtract the review burden and discard anything policy forbids — then build the top one first.
• Irreversible, financial, regulated or sensitive steps stay human; automate everything up to them.
• Scope before you build: trigger, inputs, output, definition of done, and an explicit out-of-scope boundary.
• A one-off task is not a workflow — do it ad hoc with AI help.
• Find the stable core to automate and route the variable judgment to a person.