Building an AI opportunity map

Start from business objectives and process friction, not from a list of product features.

Map revenue growth, customer value, lead time, quality, risk, and workload as separate objectives. Then look for processes where information is sought, assessed, rewritten, or transferred.

Note for each opportunity the current process, stakeholders, available data, and the decision that should be better or faster. An opportunity without a process owner or measurement point does not go to the pilot list.

Business objective
Process friction
Available data
Decision moment
Owner

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A service provider links slow quote follow-up to conversion loss and first investigates where the handover is getting stuck.

Try this prompt

Create an AI opportunity map for [company] under revenue, customer, operations, quality, and risk. Provide for each opportunity the process, user, data, decision point, and owner. Do not invent company data.

Your practical assignment

Interview one process owner and fill in five opportunities with evidence from current work.

1.2

Selecting use cases based on impact, speed, and complexity

Prioritize value and learnability; let high risk and low verifiability limit the score.

Assess impact, speed to first evidence, technical and organizational complexity, data quality, and error impact. Use hard knock-outs for prohibited data, missing owner, or unacceptable consequences.

A total score should not hide a critical weakness. Test whether the ranking changes when the impact is lower or the implementation time is higher.

Impact
Time until evidence
Complexity
Data quality
Error impact
Knockout

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An internal summary can be more pilot-worthy than automatic pricing, even when the latter promises more theoretical revenue.

Try this prompt

Rank [use-cases] by impact, time to proof, complexity, data quality, and error impact. Show assumptions, knock-outs, and sensitivity. Do not give a winner if key data is missing.

Your practical assignment

Score six candidates and let a second decision-maker challenge the weights.

1.3

From assumption to business case

An AI business case is a testable hypothesis with a baseline measurement, full costs, and uncertainty.

Formulate which result changes, for whom, and against which baseline. Include implementation, control, training, management, corrective work, and license or API costs.

Use low, basic, and high scenarios. Identify the assumption with the greatest impact and the cheapest experiment that can test it.

Baseline measurement
Value formula
Total costs
Scenarios
Decisive test

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A content workflow counts writing time, review, publication, and correction of errors.

Try this prompt

Build a business case for [use-case] with baseline measurement, value formula, one-time and monthly costs, three scenarios, critical assumption, and cheapest valid test.

Your practical assignment

Create a scenario analysis and choose which assumption the pilot should measure first.

1.4

Configure, connect, build or buy

Choose the lightest solution that controllably meets the requirements.

An instruction or skill may suffice for limited work. A plugin is suitable for approved external data or actions; API or provider for scale, integration, logging, or a custom user experience.

Compare data flows, permissions, vendor lock-in, management burden, recoverability, and total costs. Do not build yourself without demonstrable need and do not buy a black box that prevents critical control.

Requirements
Data flow
Permissions
Management
Exit

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A team first tests a controlled skill before the same logic is built into the CRM.

Try this prompt

Compare configuring, plugin, API building, and vendor for [use-case] on fit, data, control, management, cost, scale, and exit. Advise the lightest valid option.

Your practical assignment

Create a choice architecture and add a concrete exit condition.

1.5

Creating a balanced AI roadmap

Combine fast learning pilots with foundations for data, people, and governance.

Plan measurement equipment, data agreements, training, and decision-making in addition to the pilot. Organize dependencies: integration follows only after process and access design.

Use phases with exit criteria and reserve capacity for discontinued experiments and the removal of solutions without value.

Pilot
Measurement plan
Data
People
Governance
Stopping capacity

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Quarter one validates two use cases; quarter two only scales the winner and strengthens logging.

Try this prompt

Create a 90-day and two-quarter roadmap for [portfolio]. Show dependencies, owner, budget range, decision gates, and stopping points.

Your practical assignment

Plan one 90-day pilot with three formal decision gates.

1.6

Ownership and decision rights

Each workflow needs a business, process, data, and technical point of contact.

The business owner carries value and risk appetite; the process owner the working method; the data owner access and quality; technical management integration and continuity.

Record who designs, tests, approves, changes, pauses, and decommissions. One person can combine roles, but decisions must remain visible.

Business owner
process
Data
Technology
Authority to stop

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A sales assistant makes proposals; only the account manager sends price or contract information.

Try this prompt

Create a decision rights matrix for [workflow] with design, access, testing, publishing, incident, and termination. Name one ultimate responsible person.

Your practical assignment

Fill in the matrix with roles and simulate one incident.

AI strategy from opportunity to portfolio

Building an AI opportunity map

Selecting use cases based on impact, speed, and complexity

From assumption to business case

Configure, connect, build or buy

Creating a balanced AI roadmap

Ownership and decision rights

Bring everything together