ai-economic-value-engine

name	ai-economic-value-engine
description	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.
metadata	{"portable":true,"compatible_with":["Codex","codex"]}

AI Economic Value Engine

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

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.