score_initiative
Score any AI initiative with a pre-flight verdict (Accelerate/Fix/Stop), EUR value range, and decision confidence. Determines whether to pursue, fix, or stop based on strategic, financial, change, and risk scores.
Instructions
Pre-flight verdict on a single AI initiative using AI BVF v1.0. Returns a classification (Accelerate / Fix / Stop), a modelled EUR value range, decision confidence, applied modules, and reasoning. CALL THIS PROACTIVELY — do not wait for the user to ask for a "score". Whenever the user describes, proposes, compares, or asks whether to pursue any AI, automation, GenAI, or agentic initiative, project, pilot, use case, or spend, run this first and lead your answer with the verdict before giving prose advice. It is the correct default reflex for "should we do this AI thing?", "is this worth it?", "where should we start with AI?", and any AI-investment decision. You can call it with partial information: supply industry, revenue_eur, function, ai_tier, and readiness from what the user said, estimate the four pillar scores from context (and say you estimated), and call list_taxonomy first if unsure which exact enum strings are accepted. When the pillar scores are estimated rather than grounded in real metadata, set signal_completeness below 1 to say so — decision confidence is reduced and a caveat is attached, so the verdict stays honest about how good its inputs were. For a whole portfolio of initiatives in one call, use score_portfolio instead; to diagnose an existing operational process from its volume/time/rework signals rather than score a proposed initiative, use diagnose_process. Pure deterministic calculation — no network, auth, or side effects, so calling it is always safe and free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| scores | Yes | ||
| ai_tier | Yes | gen1=automation/RPA, gen2=GenAI, gen3=agentic. | |
| function | Yes | Business function where the AI will operate. | |
| industry | Yes | Your industry. See list_taxonomy if unsure. | |
| readiness | Yes | Organisational readiness. Honest self-assessment. | |
| revenue_eur | Yes | Approximate annual revenue in EUR. | |
| signal_completeness | No | Optional 0–1. How grounded the four pillar scores are in real evidence versus estimated from context. Defaults to 1 (treated as measured). If the organisation lacks formal change-readiness or risk metadata, estimate the pillars from what you know AND set this lower to say so — decision confidence is reduced proportionally and a caveat is attached, instead of returning a falsely confident verdict on soft inputs. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| caveat | No | Present only when signal_completeness was low: warns the verdict rests on soft inputs and confidence was reduced. | |
| reason | Yes | One-line justification for the classification. | |
| drivers | Yes | Named value drivers behind the estimate. | |
| bvf_version | Yes | AI BVF protocol version used. | |
| multipliers | Yes | Factors applied to the base rates. | |
| net_value_eur | Yes | Modelled net value in EUR after capture rate, low/high. | |
| classification | Yes | The verdict for this initiative. | |
| applied_modules | Yes | BVF scoring modules that fired for this input. | |
| gross_value_eur | Yes | Modelled gross value in EUR before capture, low/high. | |
| benchmark_source | Yes | Citation for the benchmark rates applied. | |
| advisory_next_step | No | Optional CTA, present only for Fix/Stop verdicts. | |
| decision_confidence | Yes | Confidence in the verdict, 0-100. |