D1 · Opportunity and Value

This domain carries 18% of the mock — roughly 9 of 50 items — and it is where most real AI programmes go wrong before a single tool is bought. It tests whether you can look across a value chain, name where generative AI actually creates value, size that value in numbers a CFO will not laugh at, and — just as important — decide which ideas to stop. The standalone Academy AI Leadership course (180 minutes, the longest in the catalogue) frames leadership as setting priorities, aligning stakeholders and producing an initial AI strategy draft; opportunity identification is the raw material for all three.

What you need to know

Opportunity work is triage, not enthusiasm. Every function will bring you a wish list; your job is to convert vague “AI could help here” into a small set of framed bets with an owner, a benefit type, a rough size and a kill condition. Value comes in three flavours — cost-out (do the same work with less time or fewer errors), revenue (win, retain or grow customers) and risk (reduce loss, fraud, downtime or compliance exposure) — and each is measured and defended differently. A credible portfolio mixes quick cost-out wins that build belief with a few higher-variance revenue and risk bets. The discipline that separates leaders from tinkerers is killing bad ideas cheaply and early, before they consume a year of a team’s calendar.

Learning objectives

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

1. Map where generative AI creates value across a value chain, distinguishing high-volume/high-variance work from work AI cannot yet own.
2. Classify an opportunity as a cost-out, revenue or risk play and choose the right benefit measure for each.
3. Size a benefit credibly using a driver-based estimate with explicit assumptions and a confidence band.
4. Balance a portfolio across effort, value, time-to-impact and risk rather than chasing one big bet.
5. Kill weak opportunities early using a written set of disqualifiers, and defend that decision to their sponsors.
6. Frame an opportunity on a single page a steering group can approve or reject.

1.1 Where AI actually creates value

Generative AI does not create value uniformly. It concentrates in work that is language-shaped, high-volume, and tolerant of a review step. Walk the value chain function by function and ask three questions of each activity: is the input mostly text, code or images? does it happen thousands of times? can a human check the output before it matters?

  Value density of an activity
  high │  ████ contracts drafting        ████ support draft replies
       │  ████ research synthesis         ████ document extraction
       │  ────────────────────────────────────────────────────────
  low  │  ░░ physical control   ░░ audit sign-off   ░░ legal opinions
       └───────────────────────────────────────────────────────────►
           high volume · text-shaped · reviewable   →   AI-friendly
           low volume · physical · irreversible      →   keep human-owned

1.2 The three kinds of value: cost-out, revenue, risk

Every opportunity resolves to one dominant benefit type. Naming it wrong is the most common sizing error, because you then measure the wrong thing and cannot defend the number.

The hardest honesty test is cost-out: time saved is not money saved unless the time is redeployed or removed. Five minutes saved per ticket across an under-utilised team is capacity, not cash, until you either raise volume or reduce headcount — and the second is a workforce decision (see D5), not an automatic saving.

1.3 Sizing a benefit credibly

Board members have seen inflated AI business cases and discount them reflexively. Credibility comes from a driver-based estimate — a small chain of observable quantities — plus stated assumptions and a range, never a single hero number.

Volume      : 40,000 tickets / month
Coverage    : 60% are draftable → 24,000
Time saved  : 4 min → 2 min per ticket = 2 min saved
Rate        : £25 / hour loaded
Gross saving: 24,000 × 2/60 × £25 = £20,000 / month
Adjust      : 30% of saved time redeployed to backlog (real), 70% is slack
Credible    : ~£6,000 / month cashable, ~£14,000 capacity
Range       : ±40% pending a 6-week pilot

Baseline win rate : 22%
Hypothesis        : +2pp from faster, tailored proposals
Deals / quarter   : 300 → +6 wins
Avg deal          : £18,000
Gross uplift      : £108,000 / quarter
Counterfactual    : assume 40% would have closed anyway → net +£65,000
Confidence        : low until an A/B test on 2 regions runs

The move that earns trust is the counterfactual line and the confidence band. A leader who volunteers “40% would have closed anyway” is believed on the remaining 60%; a leader who claims the full number is discounted entirely.

1.4 Portfolio balance

One big bet is a career risk and a programme risk. Leaders assemble a portfolio balanced on effort, value, time-to-impact and uncertainty, so that early wins fund belief while a few larger bets carry the upside.

            VALUE
             ▲
   high      │   Major bets            Transformative
             │   (revenue, risk;       (rare; long,
             │    scale carefully)      board-sponsored)
             │
             │   Quick wins            Fill-ins
   low       │   (cost-out; ship       (nice-to-have;
             │    first, build trust)   defer)
             └──────────────────────────────────────► EFFORT
                 low                        high

A healthy first-year portfolio is roughly 60% quick cost-out wins, 30% scaled revenue/risk plays, 10% exploratory — enough momentum to keep sponsors funding, enough ambition to matter. The strategy and roadmap domain turns this portfolio into a sequenced plan.

1.5 Killing bad ideas early

The most valuable leadership behaviour in this domain is stopping work. Every opportunity should carry disqualifiers written before enthusiasm sets in, so the kill decision is a policy, not a personality clash.

Kill cheaply: a two-week spike, a back-of-envelope size, and a documented “no” preserve credibility far better than a year-long project that dies quietly.

Decision framework

The V-A-L-U-E opportunity screen

Run every candidate through five gates in order. The first “no” stops the screen — you do not size an opportunity you cannot own or measure.

Only candidates that pass all five earn a one-page frame and a slot in the portfolio. The screen is deliberately cheap to run — minutes per idea — so it can be applied to a whole backlog in an afternoon.

Common mistakes

Scenario challenge

Scenario. You are the transformation lead at a 4,000-person insurer. Three functions bring you AI ideas in the same week. Claims operations wants to auto-draft settlement letters (120,000 letters a year, currently 12 minutes each, heavily templated, always reviewed by a handler). Marketing wants a “GPT-powered” campaign personalisation engine with a promised “40% lift” and no baseline. The chief underwriter wants AI to make certain low-value coverage decisions automatically to cut cycle time. The CFO has given you funding for two initiatives this quarter and wants a one-page rationale for each choice, including what you are declining and why.

Expert reasoning trace.

1. Screen before sizing. Run V-A-L-U-E on all three. Claims letters: text-shaped, high-volume, reviewable, owner is the head of claims, data is internal, evidence is easy (time per letter is logged) — passes all gates. Marketing: fails Evidence (no baseline) and its number fails the credibility test (a bare 40% with no driver chain). Underwriting auto-decisions: fails Value-chain fit and Lawful/accountable — an irreversible, regulated decision of record with no human review is not an assist opportunity, it is a governance problem.

2. Classify benefit type honestly. Claims is cost-out: 120,000 × ~6 minutes saved × loaded rate — but I split cashable from capacity, because handlers are not going to be reduced this year, so most of it is capacity plus a quality/turnaround gain worth quantifying separately. Marketing would be a revenue play, but I cannot size a revenue play with no baseline and no counterfactual.

3. Fix, don’t reject, the salvageable one. Marketing is not a “no” forever — it is a “not yet”. I fund a two-week baseline-and-A/B design as a cheap spike, not a build. That respects the sponsor while refusing to fund on a hero number.

4. Decline the dangerous one clearly. Underwriting auto-decisions get a written “no” with the reason: regulated decisions of record need a human-review design and a governance review first. I offer the underwriter the assist version (AI drafts the rationale, a human decides) as the fundable alternative.

5. Compose the portfolio. Fund the claims quick win (builds belief, real turnaround gain) and the marketing baseline spike (keeps the higher-upside revenue bet alive at low cost). Document the underwriting decline and its safe alternative.

Board-ready outcome: two funded initiatives (one cashable/capacity cost-out win, one cheap revenue-hypothesis spike), one documented decline with a safer path offered, and every number carrying assumptions and a range. The CFO can defend all of it.

Assessment traps

Practice questions

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

Key takeaways

• AI value concentrates in high-volume, text-shaped, reviewable work; irreversible, safety-critical and regulated-of-record work is not a full-automation opportunity.
• Every opportunity is dominated by one benefit type — cost-out, revenue or risk — and each is measured and defended differently.
• Time saved is not cash unless it is redeployed or removed; separate cashable from capacity.
• Size with a driver-based estimate, explicit assumptions, a counterfactual and a range — never a hero number.
• Balance a portfolio: quick cost-out wins to build belief, a few scaled revenue/risk bets, a little exploration.
• Kill early and in writing. Pre-agreed disqualifiers turn a hard “no” into a defensible policy.
• Run the V-A-L-U-E screen before sizing: value-chain fit, accountable owner, lawful data, unit economics, evidence path.
• The portfolio you assemble here becomes the input to the strategy and roadmap domain.