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Advanced•10–16 hours

AI Business Case Evidence Pack

Build a decision-ready AI business case where ROI, risk, controls, and quality evidence remain separate instead of being collapsed into one attractive number.

business caseROI modelingrisk analysisevaluation designdecision governance

Scenario

Task

An AI pilot looks convincing and a vendor case study promises double-digit uplift. Leadership needs a scaling decision. Build a business case that separates provider-reported metrics from internal measurement, includes review, retry, and failure costs, exposes uncertainty, and defines release and stop controls instead of optimistic spreadsheet theatre.

Step-by-step execution

1. Lock the pre-AI baseline

Outcome: The business case has a verifiable reference point.

Tasks

  • →Define the denominator and unit of work
  • →Collect volume, time, cost, error, and outcome baselines
  • →Separate direct cost from opportunity cost
  • →Label data gaps and confidence

Checks

  • ✓No benefit exists without a baseline denominator
  • ✓Averages do not hide high-cost tail cases
  • ✓The baseline has an owner and reproducible source

2. Calculate cost per successful task

Outcome: Token or API price no longer hides the real operating cost.

Tasks

  • →Add inference, retrieval, and tool costs
  • →Add retries, fallbacks, and failed attempts
  • →Estimate human review and escalation rates
  • →Add observability, support, incident, and governance overhead

Checks

  • ✓The cost denominator is a successful verified outcome
  • ✓Failure handling and review are not assumed to be zero without evidence
  • ✓The scenario includes low, base, and high volume and quality assumptions

3. Separate reported claims, internal evidence, and assumptions

Outcome: External case studies are not treated as causal proof for your organization.

Tasks

  • →Label every number as measured/internal, provider-reported, independent external, or assumption
  • →Document transfer assumptions
  • →Run sensitivity analysis for adoption, quality, review rate, and unit cost
  • →Identify claims that require an experiment before scaling

Checks

  • ✓Provider metrics are not used as an internal baseline
  • ✓ROI does not depend on one hidden optimistic assumption
  • ✓A downside scenario covers quality, adoption, or cost misses

4. Connect economics to quality, risk, and rollout

Outcome: The go/no-go decision reflects evidence and control readiness, not only expected value.

Tasks

  • →Define quality, safety, and business thresholds
  • →Tie autonomy and rollout stage to control evidence
  • →Add canary or holdback and authoritative outcome tracking
  • →Record rollback and kill triggers plus review cadence

Checks

  • ✓Positive ROI does not override a critical safety or control failure
  • ✓Business KPIs are separated from model-quality KPIs
  • ✓Production corrections update the evaluation set and financial assumptions

Acceptance criteria

  • Baseline, AI cost, and expected benefit have explicit denominators and sources
  • Cost per successful task includes retries, failures, review, and operational overhead
  • Provider-reported metrics are separated from internal or independent evidence and assumptions
  • Sensitivity analysis shows downside under worse quality, adoption, or cost assumptions
  • The go/no-go memo has quality, risk, economics, rollout, and kill thresholds with owners

Materials before execution

Human-in-the-loop for AI

A practical production architecture for human oversight: involve a person at a specific risk point with enough context for real, not ceremonial, control. Covers contracts, authority boundaries, failure modes, evaluation, and controlled rollout.