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.

Score every initiative in an AI BVF v1.0 portfolio in a single call and return the portfolio-level shape: counts of Accelerate / Fix / Stop, aggregate modelled EUR value range, mean decision confidence, the top initiative by value, the highest-risk initiative, and the per-initiative results. Use after validate_portfolio (or instead of looping score_initiative per initiative) when you have a portfolio document and want the board-level verdict, not just one classification. Schema validation runs first; if the portfolio is malformed the response sets valid=false and reports the validation errors without attempting to score. Pure deterministic calculation — no network, auth, or side effects.

For an initiative classified Stop or Fix, return concrete, deterministic recommendations that would flip classification toward Accelerate. Pillar-level targets with named actions and rationale. Answers the "what do I do next" question after score_initiative. Pure deterministic calculation — no network, auth, or side effects.

Calculate annual Organisational Drag Cost — the hidden cost of structural friction from misalignment between AI tier and organisational readiness (NOT the cost of the AI build). Use to quantify the cost of NOT changing the operating model. Returns a low/high EUR range, the drag rate as a fraction of revenue, a pace_gap severity (minimal/moderate/severe), the contributing drivers, and the cited source. Pure deterministic calculation — no network, auth, or side effects.

Check that a BVF portfolio document conforms to the AI BVF v1.0 schema before you score, store, or share it. Returns { valid: true } when well-formed, or { valid: false, errors: [...] } where each error names the failing JSON path and the rule it broke. Use this to catch malformed portfolios early; use score_initiative to evaluate a single initiative, or score_portfolio to score them all in one call. Schema: https://www.aibvf.com/protocol. Pure deterministic validation — no network, auth, or side effects.

Look up the published benchmark rates for a business function and industry. Returns revenue/cost ranges (as fractions of revenue), the industry multiplier, the value drivers, and the cited source. Use when the caller wants the raw rates and multiplier without running a four-pillar verdict — for an initiative-level Accelerate/Fix/Stop call, use score_initiative instead. Pure deterministic lookup — no network, auth, or side effects.

Return every accepted enum value for the AI BVF taxonomy: the full lists of industries, functions, ai_tier levels (gen1/gen2/gen3), and readiness levels. Call this first when unsure which exact strings score_initiative, score_portfolio, recommend_improvements, calculate_pace_layer_drag, get_benchmark, or diagnose_process will accept, so you pass valid values instead of guessing. Takes no parameters and has no side effects.

Diagnose a single existing business process from its observed operational signals and return whether it is too heavy to leave alone, the one intervention that fixes it (Automate / Consolidate & re-sequence / Quality controls / Eliminate), the modelled net EUR saving against its measured baseline, the efficiency gain, an Accelerate/Fix/Stop verdict, and a decision confidence governed by how much was actually measured. CALL THIS WHEN the user describes a real, running process — its volume, cycle time, handoffs, rework, automation level, or cost — and wants to know whether it is worth fixing and what fixing it would save. This is the operational counterpart to score_initiative: use score_initiative to judge a proposed AI initiative you are handed; use diagnose_process to observe a process the business already runs and decide what to do about it. Call list_taxonomy first if unsure which function enum value to pass. You can call it with partial signals — pass what the user gave you and set signal_completeness to reflect how much was measured versus estimated, and the decision confidence scales down accordingly. Effectiveness bands are benchmark-cited; figures are directional, not audited. Pure deterministic calculation — no network, auth, or side effects.