Ask a leadership team for AI ideas and you'll get two kinds: whatever a competitor has just announced, and whatever the technology team finds easiest to build. Neither gives you a portfolio. An AI opportunity scan fixes that by starting with the work itself rather than the technology. You go function by function, map how real processes run step by step, and ask where AI could change the economics of a step. You lead it; you don't wait for IT or a data team to bring ideas to you.

Map each process at the level of steps someone actually performs: "check the new contract against our standard terms", not "legal". Six to ten steps is usually enough. Then bring evidence: how often each step happens, how long it takes, and where people complain. Volume matters because AI pays back on repetition, and pain points matter because they tell you where people will welcome change.

Run five questions against every step. First, where does the volume sit, the steps repeated dozens or hundreds of times a week? Second, where do people judge or forecast from patterns in past records, such as which clients might leave or which invoices look wrong? That is predictive AI territory. Third, where do people read, write, summarise or search large amounts of text? That is generative AI. Fourth, where does someone shepherd a task with a clear goal across several systems, gathering, checking and handing on? That is where agentic AI may fit. Fifth, what is the data behind the step, and what would it cost if the AI got it wrong? This last question doesn't kill ideas at this stage; it flags them for honest scoring later.

The second, third and fourth questions use the three AI types from Module 1. Naming the type early stops a generative tool being sold as a forecasting engine, or a simple drafting aid being dressed up as an agent.

Write every candidate as a use-case statement: at this step, use this type of AI to do this, creating this value driver (cost reduction, revenue growth, risk reduction or new capability), measured by this number. A statement like "use generative AI to draft first replies to complaint emails, cutting handling time, measured by minutes per complaint" can be scored. "AI for customer service" can't.

A long list of use-case statements is only useful once you can compare them. Four dimensions do most of the work.

Strategic value asks how much the use case matters to the organisation's goals: the size of the value driver, how many people or clients it touches, and whether it strengthens something you compete on. Data readiness asks whether the data the use case needs exists, is accessible, is good enough and is yours to use. Implementation effort covers the technical build, the integration with existing systems, and, most often underestimated, the change in how people work. Risk covers the cost of errors, regulatory exposure, client confidentiality and reputational damage if the system misbehaves in public.

Score each dimension from 1 to 5, and phrase every scale so that 5 is always good. That means you score ease of implementation rather than effort (5 = light lift) and risk containment rather than risk (5 = low, well-understood risk). Otherwise someone will add a high-effort score as if it were a strength. Then weight the dimensions explicitly. This course's default is strategic value 3 and the other three 2 each, so the maximum weighted score is 45. Change the weights if your situation demands it, but agree them before anyone scores, not after they see which project is winning.

These dimensions aren't arbitrary. When Gartner predicted in mid-2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, the reasons it gave were poor data quality, inadequate risk controls, escalating costs and unclear business value: the same four dimensions, seen from the failure end.

Non-specialists tend to misjudge in predictable ways. They over-score strategic value when an idea is exciting or a rival has announced something similar. They over-score data readiness because "we have years of data" ignores whether it is consolidated, clean and permitted for this use. They under-score effort by counting the software and forgetting integration and retraining. And they treat risk as either fatal or irrelevant. The cure is a line of evidence behind every score.

The weighted total gives you a ranking, but plot the results too. An impact–feasibility matrix puts strategic value on one axis and ease on the other. High value and high ease are the candidates to do now; high value and low ease are strategic bets that need a plan; low value and high ease are optional fill-ins; low and low are dropped. The AI Use Case Priority Scorecard produces the weighted ranking; read the quadrant from each candidate's value and ease scores.

A ranked list still isn't a plan. Some top-scoring candidates can start next month; others depend on data work that hasn't begun. This course sorts every shortlisted use case into one of three types. The framing is the course's own; you'll see similar ideas elsewhere under different names.

A quick win can be delivered in under six months, usually with an off-the-shelf generative AI tool and little data preparation: drafting, summarising, searching internal documents. Its job is to deliver visible value fast and to teach the organisation how to adopt AI. A scale opportunity typically takes six to eighteen months, often uses predictive AI, and depends on consolidating or cleaning data first. Scale opportunities are often where most of the money is, because they change decisions across a whole function. A transform bet takes 18 months or more and redesigns a workflow, frequently through agentic AI that carries a task end to end. It shapes where you will compete in future, and it carries the most uncertainty.

You need all three. A portfolio of only quick wins produces a scatter of productivity tools and no strategic shift. A portfolio led by a transform bet risks burning your political capital before anything has worked. Davenport and Ronanki's (2018) case for not starting with moon shots rests partly on MD Anderson Cancer Center, whose high-profile cognitive computing project was put on hold in 2017 after costs topped $62 million, while smaller projects run in parallel showed more promise. Gartner's June 2025 prediction that over 40% of agentic AI projects will be cancelled by the end of 2027 points the same way.

Sequence with three rules. Lead with quick wins that are visible to the people whose support you'll need later. Start the data groundwork for scale opportunities in the same window, so they are ready when the quick wins have earned you credibility. Stage transform bets: fund a bounded experiment with a clear go/no-go test before you fund the full programme. Give each type a stop criterion too: a quick win that hasn't shown its measure moving within its pilot window should be stopped, not extended indefinitely.

For the exercise, use these five initiatives: (a) a generative assistant that drafts first replies to customer complaints; (b) a demand forecasting model that needs three years of sales data merged from two systems; (c) meeting summaries switched on in the office software staff already use; (d) an agent that handles supplier onboarding end to end across procurement, finance and legal; (e) a pricing recommendation model trained on consolidated transaction history.

The scoring framework is only as good as the candidates you feed it and the honesty of the scores. A well-run workshop gets both. A badly run one becomes a wishlist, where every function leaves with its favourite idea intact, or a vendor pitch, where the discussion revolves around a product somebody has already seen.

Get the room right. You want the heads of the functions being scanned, at least one person who does the work day to day, someone who understands where the data lives, and someone from risk or legal. Eight to twelve people is workable. The facilitator should not be the most senior sponsor, and vendors should not be present at all. Ask for pre-work: a one-page process map per function and the volume figures behind it. That turns the session from a debate about whether problems exist into a discussion of which ones to solve.

Structure the 90 minutes so the group diverges before it converges. A good template is: 0–10 minutes, purpose, ground rules and the scoring weights, agreed before any idea is on the table; 10–30, idea generation using 1-2-4-All, one of the free Liberating Structures facilitation methods developed by Henri Lipmanowicz and Keith McCandless, in which people think alone, then in pairs, then in fours, before each group shares its best idea; 30–45, cluster the ideas and rewrite each one as a use-case statement, dropping any that can't be written that way; 45–70, score in mixed-function pairs, then compare scores and discuss only the big disagreements; 70–85, plot the candidates on the impact–feasibility matrix and agree the ranked shortlist; 85–90, name an owner and a next step for each shortlisted candidate.

Three ground rules prevent most failures. No product names: candidates are described by the problem and AI type, not by a vendor. The most senior person speaks last in each discussion, so that the group's scores aren't anchored to theirs. And anything that can't be scored today goes to a visible parking list with an owner, rather than being argued over. The method works on a wall of sticky notes or on any shared online whiteboard.

The workshop should end with three outputs: the ranked shortlist with each candidate's scores, a named owner for every shortlisted item, and the next step and date for each. Capture the scores in the AI Use Case Priority Scorecard straight afterwards, while the reasoning is fresh.