Practical guide
AI fluency for financial analysts: normalize a cost comparison
Compare fictional service quotes using consistent units, included quantities and explicit assumptions.
A financial analyst can use AI to organize a cost comparison while checking that the alternatives use the same units and scope. This original fictional exercise compares two service quotes. It is arithmetic and evidence-handling practice, not financial advice, an investment recommendation or a prescribed procurement decision.
Extract the quote structures
Separate fixed charges, included quantities and variable charges.
Quote A costs one hundred units per month and includes ten reports; extra reports cost eight units each. Quote B costs forty units per month plus twelve units per report. Both fictional quotes cover the same month, but the packet does not establish whether report quality or support arrangements are equivalent.
Ask AI to extract the price rules and identify unresolved scope differences. Do not compare the headline monthly fees alone. The lower fixed charge does not establish the lower total cost for every usage level.
Calculate at a stated quantity
A comparison needs a usage assumption the reader can inspect.
At ten reports, A costs one hundred units and B costs one hundred and sixty. At five reports, A still costs one hundred and B also costs one hundred. At fifteen reports, A costs one hundred and forty and B costs two hundred and twenty.
Recalculate these totals independently from the supplied rules. They are synthetic examples, not actual vendor prices. Keep the quantity assumption next to each result so the reader can see which situation the number describes. Do not present one scenario as the organization's confirmed usage.
Check what the totals leave out
A correct price calculation does not establish that the options are equivalent.
The packet does not specify report format, turnaround, data access or support. List these as comparison questions rather than assuming equivalence. If the decision depends on any of them, the cost table is only one part of the evidence needed.
AI can help draft clarification questions, but should not invent a feature comparison from generic vendor expectations. Distinguish known price rules from unknown service conditions. The analyst's role in this exercise is to make the comparison inspectable, not to resolve absent information through confident wording.
Explain the decision boundary
Show where the cost relationship changes without overstating its importance.
Within the zero-to-ten report range, B's total is forty plus twelve times quantity, while A remains one hundred. They match at five reports under the toy rules. Below that quantity B is lower; above it A is lower within the range. Beyond ten, apply A's extra-report rule rather than extending the flat price.
Check the boundary with nearby quantities. This helps expose a formula error or misunderstood inclusion. It does not determine the final service choice when quality, scope or other conditions remain unresolved.
Deliver a conditional comparison note
The handoff should separate calculated findings from the purchasing decision.
Include the extracted rules, tested quantities, unresolved scope questions and the owner who must confirm expected usage. State that the price comparison alone does not establish the preferred provider. Avoid language suggesting a binding recommendation or authorized commitment.
Review unit discipline, arithmetic and handling of missing information within this exercise. The FP&A analyst guide revises a forecast from drivers; this guide normalizes alternative cost structures. A separate future cost-comparison page must add materially different evidence or utility rather than repeat this worked calculation.
Sources and scope
The references below are background reading, not evidence that this fictional exercise has been validated or endorsed.
The case details, calculations and suggested review questions are original instructional material. Use them to discuss observable work, not to infer customer outcomes, professional credentials or performance in every setting. Before adapting the exercise, confirm the relevant facts, approved tools, data permissions and decision owners. If you change the case, revisit the expected answers and checks as well. These examples describe practice tasks, not a promise that a particular product includes the fictional features.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England