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In development · Preparing for public launch

AIDIS AI Assessment

Structured discovery, workflow priorities, and transparent financial scenarios

THE BUSINESS PROBLEMHow can a business assess workflow opportunities without burying uncertainty inside a financial estimate?
ACTUAL OUTCOME / CURRENT STAGEA product being prepared for public launch, with structured discovery, branching questions, scoring and financial modeling. It has not been released.

I defined the product direction, refined question wording and workflow scope, directed AI-assisted development, and reviewed the customer journey and calculation assumptions.

The problem

I approached assessment as a sequence of business questions and evidence decisions: understand the operation, identify specific workflows, make scope explicit, and distinguish the kinds of benefit being estimated.

My decisions

Ask for a clear number

Replace ambiguous numeric ranges with one plainly labeled value so an owner can understand what a question asks.

Make assessment scope explicit

Allow one to three workflow deep dives and state the customer-selected scope transparently.

Separate benefit types

Keep capacity value distinct from cash savings, gross-profit opportunity and risk instead of presenting one blended savings figure.

Explore the workflow

What could this workload free up?

Change the monthly workload or mark the labor cost unknown. See how review time, maintenance and missing evidence affect the estimate.

Interactive controls are unavailable until this example loads. The complete annotated example is below.

Read the annotated example

120 occurrences per month

1,440 annual occurrences × 10 minutes ÷ 60 = 240 work hours. At 50% removable work, gross capacity is 120 hours.

Subtract 24 review hours and 12 maintenance hours: 84 hours per year. At the sample $40/hour cost: $3,360 annual capacity value.

240 occurrences per month

Gross capacity is 240 hours; review is 48 hours. After 12 maintenance hours: 180 hours and $7,200 annual capacity value.

At zero volume, recovered time and value are zero rather than negative.

Unknown labor cost at 120/month: 84 hours can still be calculated. Monetary value is not estimated—labor cost evidence is needed. Cash savings are not established for any example.

Evidence and outcome

AIDIS AI Assessment: Actual assessment question · Unreleased local development preview
Actual assessment question · Unreleased local development preview · Captured September 2026

Selected source evidence, summarized for this portfolio. Newly authored examples use fictional data.

A product decision changed the question

The decision record documents replacing customer-facing Low/High numeric input pairs with one plainly labeled number. It also separates a service business’s category, delivery or booking model, and primary offer.

  • Before: a numeric range could leave the expected answer unclear.
  • Decision: request one plainly labeled number.
  • Related decision: separate business category from how the service is delivered.

Source basis: Assessment decision log D-017, D-022 and D-027; paraphrased.

A limited scope is still a valid scope

The recorded workflow decision allows one to three deep dives: one is sufficient and three are recommended. The interface and report should identify which scope the customer actually selected.

Source basis: Assessment decision D-018; paraphrased.

Financial logic makes deductions visible

The capacity calculation deducts ongoing review and maintenance from gross removable time, and floors net recovered hours at zero. The accompanying example uses that formula with openly stated fictional assumptions.

Source basis: Assessment financial-domain calculation and approved portfolio scenario.

The development work demonstrates structured discovery and careful benefit modeling. Public release and the complete customer journey still require launch validation. This portfolio contains an illustrative scenario, not the released assessment. A live product link will be added after launch is confirmed.

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