An organization can know that its quotation process is slow without knowing whether it needs AI. A useful framework should help it make that decision.
AI Opportunity Map starts with a process and a performance gap, compares possible interventions, and produces an opportunity record with evidence, assumptions and the next experiment. The method is a practical synthesis of process analysis, enterprise experience and published AI research. It has not been empirically validated as a ranking model.
Discover through processes; evaluate interventions
Use the process as the discovery unit, the intervention as the evaluation unit, and the process outcome as the success unit.
A task or handoff can still be the implementation location. Its improvement needs a credible connection to the result the process delivers. Saving time on a task outside the current constraint may free capacity without improving turnaround.
Begin with a bounded process: request to approved quote, order to fulfillment, issue to resolution, tender to submission, customer onboarding, or demand to supply. These industrial B2B examples reflect the work I focus on; use names and boundaries appropriate to your organization.
An illustrative quotation example
Suppose a quote takes five elapsed days. Drafting takes one hour and technical clarification accounts for two days of waiting. These figures are invented to explain the method.
A writing assistant might save thirty minutes and leave most of the customer delay intact. Investigate why clarification takes two days: missing specifications, overloaded engineering or uncertain approval responsibility?
Compare improved intake and ownership with an AI-supported option that extracts requirements and links them to the source. Use rules for compatibility and pricing where those checks are deterministic. Engineering approves exceptions and customer commitments.
Measure total quote turnaround, critical omissions, review effort, rework and clarification loops against an improved non-AI baseline. Faster drafting with equal additional review releases no net capacity.
Five decision areas
| Area | Establish | Produce |
|---|---|---|
| Process and outcome | Trigger, endpoint, customer, owner, baseline and gap | A bounded problem worth investigating |
| Constraint and evidence | Waiting, rework, exceptions, handoffs and possible causes | A hypothesis with competing explanations |
| Intervention alternatives | Process repair, integration, rules, predictive models and generative AI | Options and their expected mechanism |
| Delivery conditions | Data access, evaluation, authority, review, integration and adoption | Blockers, unknowns and enabling work |
| Economics and decision | Realizable benefits, total costs, timing and dependencies | A conditional recommendation and next experiment |
These areas can be deepened as evidence emerges. They are not a requirement to run five workshops before discovering whether a process deserves attention.
Diagnose the cause behind the symptom
Waiting is a symptom. Incomplete information, reviewer capacity, unclear decision rights or an approval policy could explain it. Each explanation implies a different intervention.
Investigate where facts are hard to find, decisions wait, outputs require rework, handoffs stall or actions fail. These observations help locate a cause; they do not establish AI suitability.
For important claims, record a source, date, scope and owner. Mark the claim as unknown, assumed, reported, observed or measured. A long narrative is still self-report. A measurement with a small or biased sample can also mislead.
Keep uncertainties separate: the cause, AI performance, adoption and economics may each have different evidence. An incomplete business case can justify measurement rather than rejection.
Compare the interventions before choosing architecture
Structured intake and clear ownership may solve missing information. Integration may remove re-keying. Stable, explicit conditions may call for rules. Predictive models may fit forecasting or classification. Generative AI may help interpret documents, synthesize knowledge or prepare drafts.
Combinations are legitimate. A quote process might need intake improvements, system integration, deterministic pricing checks and AI-supported requirement extraction. Architecture and sourcing follow those requirements. Retrieval, agents, fine-tuning, building and buying are not mutually exclusive options.
Make economics explicit
For a separate illustrative calculation, 1,200 monthly cases with fifteen minutes of net time saved per adopted case and sixty percent adoption release 180 hours per month. At an assumed CHF 70 loaded hourly cost, that is CHF 12,600 in capacity value. It is not automatically cash savings.
A cash-saving claim needs reduced or avoided expenditure. Productive redeployment may improve service or revenue; explain that benefit separately. Include implementation, integration, evaluation, training, maintenance, model use, monitoring and human review. Show benefit ramp and low, base and high assumptions. Avoid claiming the same benefit in multiple opportunities or both as savings and additional revenue.
Conditions precede deployment
Authority to use inputs and act, suitable evaluation, controls for consequential errors, accountable ownership and a workable adoption path should be checked separately from potential value. This is the decision discipline proposed here, informed by NIST AI RMF principles. A large benefit cannot resolve a critical missing condition.
A failed condition can create enabling work. An unknown can create a measurement task. A pilot should include representative cases and difficult exceptions, a fallback, and explicit stop and scale conditions.
Leave with a decision record
Every shortlisted record should state the process outcome, constraint, evidence, proposed mechanism, alternatives, changed roles, delivery conditions, economics and owner. Then make the next decision explicit: measure first, pilot, enable first, choose a simpler intervention or defer.
Record the reason and what evidence could change it. Identify shared dependencies and overlapping benefits across the shortlist. The output should make a useful conversation possible with the people who do and own the work.
Build your AI Opportunity Map, or use the worksheet with your process team.
Research foundations and limits
APQC guidance supports business-problem and process-first discovery. That foundation is established practice; the value of this method depends on the diagnosis and decisions it helps users make.
Dell’Acqua and colleagues found that GPT-4 improved tested consulting tasks within its capability frontier and reduced correctness on a tested task outside it. This supports representative evaluation, with limits from the study’s model and setting. It does not provide a generic AI-fit rating for a business process.
NIST AI RMF Core informs attention to deployment context, oversight, evaluation and alternatives. NIST does not validate AI Opportunity Map or its recommendations.
The method needs testing with process owners who arrive with a business problem and no named AI solution. Judge its usefulness by the decisions it improves: a plausible intervention, an honest estimate, a necessary measurement or a well-supported decision to defer.