# AI Economic Value Engine > Use when discovering, designing, prioritizing, or auditing AI-powered products for measurable business value. Applies to AI opportunity mapping, ROI cases, product strategy, client workshops, and deciding whether an AI feature should be built. - Skill: `peterbamuhigire/ai-economic-value-engine` (Agent Skill) - Install (CLI): `npx skillmds@latest add peterbamuhigire/ai-economic-value-engine` - Raw SKILL.md: https://api.skillmd.com/api/skills/peterbamuhigire/ai-economic-value-engine/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: peterbamuhigire (https://skillmd.com/u/peterbamuhigire) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/peterbamuhigire/ai-economic-value-engine --- # AI Economic Value Engine ## Operating contract ## Inputs | Input | Required | Purpose | |---|---|---| | Domain evidence | yes | business outcome, baseline process, demand volume, cost and benefit assumptions, risk constraints, and decision horizon | ## Outputs - Produce: prioritised opportunity register, value model, sensitivity cases, evidence gaps, and build/no-build recommendation. ## Capability and permission boundaries Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly. ## Degraded mode When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass. ## Decision rules | Condition | Action | |---|---| | Scope, owner, or threshold is missing | Stop the affected decision and request it | | Evidence is incomplete but read-only analysis is safe | Produce a qualified partial result and gap list | | A mutation exceeds authority or tenant boundary | Block it and route for approval | | Evidence meets the stated threshold | Issue the output with provenance and owner | ## Anti-Patterns - Treating absent evidence as success. Fix: mark the check unassessed and name the missing source. - Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action. - Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit. - Reporting a metric without population, window, or source. Fix: attach all three. - Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner. Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178. ## Use When - A client or internal team wants AI features, AI transformation, agentic workflows, analytics, automation, or AI-powered apps. - The work needs prioritization by business value, feasibility, data readiness, risk, and maintainability. - You must turn AI capability into a proposal, PRD, roadmap, SRS, architecture, or implementation plan. ## Do Not Use When - The request is not materially about AI value, AI product selection, AI ROI, or AI investment prioritization. - The user already has a committed AI feature and needs a narrower implementation, evaluation, security, RAG, or agent runtime skill. ## Core Principle AI must improve an economic system: revenue, margin, productivity, quality, speed, risk, compliance, retention, or decision accuracy. Do not recommend AI for novelty. ## Required Inputs - Target users and business process. - Current pain, baseline metric, cost of the pain, and decision/action affected. - Available data, data owner, freshness, quality, sensitivity, and access constraints. - Risk tolerance, legal/compliance constraints, and human approval requirements. - Budget, timeline, operating owner, and success metric. ## Workflow 1. **Map the workflow**: Trigger, actors, data, decisions, actions, handoffs, delays, and failure points. 2. **Find AI leverage**: Classify opportunities as insight, prediction, generation, extraction, search, recommendation, decision support, or action automation. 3. **Estimate value**: Quantify time saved, extra revenue, reduced leakage, fewer errors, faster turnaround, lower service cost, or reduced risk exposure. 4. **Assess feasibility**: Check data readiness, integration complexity, model reliability, evaluation difficulty, security, and maintenance burden. 5. **Choose architecture**: Prefer deterministic workflow, RAG, analytics, or simple model call before agents or fine-tuning. 6. **Define evaluation**: Set quality, cost, latency, safety, business, and adoption thresholds. 7. **Plan operations**: Assign owner, monitoring, feedback loop, incident response, model/prompt update cadence, and client reporting. ## Opportunity Scorecard Score each item 1-5. | Dimension | Question | |---|---| | Business value | Does it move a financially or operationally important metric? | | User pull | Will users adopt it inside their real workflow? | | Data advantage | Do we have proprietary or hard-to-copy context/data? | | Feasibility | Can current models and integrations meet the required quality? | | Risk control | Can failures be detected, bounded, reversed, or escalated? | | Evaluation clarity | Can we prove whether it works before and after launch? | | Maintainability | Can the client or agency operate it for years? | Prioritize high-value, high-feasibility, low-regret use cases first. Defer low-value or unmeasurable ideas even if they are technically impressive. ## AI Product Patterns - **Decision cockpit**: Aggregates data, explains options, and recommends next actions. - **Knowledge worker assistant**: Drafts, checks, summarizes, and retrieves from trusted sources. - **Operational copilot**: Guides staff through repeatable workflows with validation and approvals. - **Predictive engine**: Forecasts demand, churn, risk, maintenance, fraud, or cash flow. - **Autonomous workflow**: Executes bounded actions with tool contracts, audit logs, and human approval gates. - **AI analytics layer**: Turns operational data into segmentation, anomaly detection, root-cause analysis, and dashboards. ## Outputs - AI opportunity map with value, feasibility, risk, and recommended sequence. - Business case with assumptions, ROI model, cost drivers, and non-financial benefits. - AI product brief or PRD with user stories, acceptance criteria, data needs, evaluation plan, and rollout stages. - Architecture recommendation with model, RAG/tool/workflow choice, governance, and operating model. - No-build recommendation when AI cannot be justified. ## Quality Standards - Tie every recommendation to a measurable business, operational, risk, quality, or adoption outcome. - Make assumptions, baselines, data readiness, operating owner, and failure modes explicit. - Prefer no-build, deterministic workflow, analytics, or RAG recommendations when they create more value than agents. ## Anti-Patterns - Recommending AI because it is technically possible rather than economically justified. - Treating model accuracy as the only success metric. - Ignoring adoption, ownership, data readiness, cost, risk, and post-launch operations. ## Hard Rules - Never skip baseline measurement. If there is no baseline, propose how to collect it. - Never promise perfect accuracy. State confidence, failure modes, human review, and fallback. - Never build agents where deterministic automation or workflow design is safer. - Never ignore post-launch ownership; AI products degrade without monitoring and feedback. ## References - Use companion AI implementation, evaluation, security, RAG, analytics, or agentic skills after the opportunity is economically justified.