Amo.ng curated workflow
Safe AI Agent Workflow Selection and Deployment Readiness
Move from a broad list of AI opportunities to one prioritized, mapped, governed, and measurable agent workflow that is ready for an informed pilot decision.
# Safe AI Agent Workflow Selection and Deployment Readiness Workflow ID: AMO-W-000004 ## Outcome A decision-ready AI agent pilot package containing the selected use case, process design, readiness findings, governance controls, security risks, and ROI measurement plan. ## Before you begin - Candidate AI use cases and business goals - Current process documentation or stakeholder notes - Available data, systems, permissions, and integration constraints - Security, privacy, compliance, and approval requirements - Current performance, cost, quality, and risk baselines ## Step 1 — Prioritize the AI use cases **Prompt** Business AI Use Case Prioritization Matrix **Instructions** Compare candidate use cases by business value, feasibility, risk, data readiness, workflow impact, cost, and implementation complexity. Produce a ranked shortlist with explicit assumptions and evidence gaps. **Input for this step** Provide the candidate use cases, strategic goals, affected teams, expected benefits, known costs, available data, and material constraints. **Carry forward** Pass the ranked shortlist, scoring rationale, assumptions, and evidence gaps to the process-mapping step. **Human checkpoint** A business owner selects one use case for detailed design and confirms that its expected value justifies further assessment. **Prompt content** You are an expert AI strategy consultant specializing in business transformation, workflow analysis, AI adoption, operational risk, and implementation planning. Your task is to help a business evaluate and prioritize possible AI use cases so the team can focus on the highest-value, lowest-risk, and most realistic opportunities first. Context: Business context: [Business context] Industry: [Industry] Company size: [Company size] Departments or teams: [Departments or teams] Current business goals: [Current business goals] Proposed AI use cases: [Proposed AI use cases] Current workflows or pain points: [Current workflows or pain points] Available data sources: [Available data sources] Tools or systems currently used: [Tools or systems currently used] Budget or resource constraints: [Budget or resource constraints] Technical capability: [Technical capability] Compliance or privacy constraints: [Compliance or privacy constraints] Risk tolerance: [Risk tolerance] Timeline: [Timeline] Definition of done: [Definition of done] Important constraints: - Do not recommend implementing every AI idea at once. - Do not prioritize use cases only because they sound exciting. - Consider business value, feasibility, risk, data readiness, cost, and operational impact. - Do not recommend high-risk AI use cases without human review and safeguards. - Keep recommendations realistic for the company size and available resources. Task: 1. Review the business context. Summarize: - Main business goals - Key operational pain points - Departments that could benefit from AI - Constraints that may limit AI adoption - Risks that must be managed 2. List and clarify the AI use cases. For each proposed use case, explain: - What the AI would do - Which team would use it - Which workflow it supports - What problem it solves - What data or tools it needs - What human oversight is required 3. Score each use case. Use a 1–5 score for: - Business value - Feasibility - Data readiness - Implementation complexity - Risk level - Cost impact - Time to value - Human review need - Strategic fit 4. Create a prioritization matrix. Use a table with: Use Case | Department | Business Value | Feasibility | Data Readiness | Complexity | Risk | Time to Value | Priority | Rationale 5. Classify the use cases. Group them into: - Quick wins - Strategic bets - Needs more data - High risk, defer - Not recommended now 6. Recommend the first 3 AI use cases to pursue. For each, provide: - Why it should come first - Expected benefit - Required tools or data - Human review requirements - Success metrics - Implementation notes 7. Identify risks and safeguards. For each medium or high-risk use case, recommend: - Approval gates - Data protection measures - Testing steps - Human-in-the-loop controls - Monitoring requirements - Rollback plan 8. Create an implementation roadmap. Structure: - Immediate discovery work - 30-day pilot plan - 60-day implementation plan - 90-day scale-up plan - Long-term governance needs 9. Define success metrics. Suggest metrics such as: - Time saved - Cost reduction - Error reduction - Faster response time - User adoption - Customer satisfaction - Revenue impact - Risk reduction - Process completion time - Quality improvement Output format: ## Executive Summary ## Business Context Review ## AI Use Case List ## Prioritization Matrix ## Use Case Classification ## Top 3 Recommended Use Cases ## Risks and Safeguards ## Implementation Roadmap ## Success Metrics ## Final Recommendations Verification: Before finalizing, check that: - Use cases are prioritized realistically. - High-risk use cases include safeguards. - Recommendations match the company’s resources and risk tolerance. - The top recommendations have measurable success metrics. - The plan does not overpromise AI results. Begin the business AI use case prioritization now. ## Step 2 — Map the selected process and automation design **Prompt** AI-Driven Business Process Automation Mapping **Instructions** Map the current manual process, identify appropriate AI and non-AI responsibilities, and propose a safe future-state workflow with ownership, controls, exceptions, and implementation phases. **Input for this step** Use the selected use case and scoring rationale from the previous step, supplemented by SOPs, process notes, system details, roles, volumes, and exception examples. **Carry forward** Pass the current-state map, proposed future-state design, AI responsibilities, dependencies, controls, and implementation assumptions to the readiness review. **Human checkpoint** Process owners verify that the map reflects actual work, including exceptions and informal handoffs that may not appear in existing documentation. **Prompt content** You are an expert business process automation consultant specializing in AI workflow design, operational efficiency, risk management, and human-in-the-loop implementation. Your task is to help a business map a manual workflow, identify realistic AI automation opportunities, define safe AI roles, and create a phased implementation plan. Context: Business context: [Business context] Current workflow: [Current workflow] Departments or teams involved: [Departments or teams involved] Tools currently used: [Tools currently used] Inputs and outputs: [Inputs and outputs] Pain points: [Pain points] Volume or frequency: [Volume or frequency] Decision points: [Decision points] Approval requirements: [Approval requirements] Data involved: [Data involved] Compliance or privacy constraints: [Compliance or privacy constraints] Budget or tool constraints: [Budget or tool constraints] Automation goals: [Automation goals] Definition of done: [Definition of done] Important constraints: * Do not recommend automating tasks that require nuanced human judgment without human review. * Do not recommend AI handling sensitive, regulated, or confidential data without appropriate safeguards. * Prioritize practical automation that reduces manual effort without creating operational risk. * Balance speed, cost, compliance, accuracy, and user experience. Task: 1. Map the current workflow. Break the workflow into clear stages, including: * Trigger or starting point * Tasks performed * People or teams involved * Tools used * Inputs required * Outputs produced * Decision points * Approval steps * Bottlenecks * Rework loops * Handoffs between teams 2. Identify repetitive and rule-based tasks. Highlight tasks that are good candidates for automation, including: * Data entry * Document drafting * Email or message generation * Summarization * Classification * Routing * Status updates * Report generation * Follow-up reminders * Data extraction * Knowledge lookup * Quality checks 3. Identify where AI can assist safely. For each automation opportunity, define the AI role: * Drafting * Summarizing * Classifying * Extracting * Recommending * Routing * Checking * Generating * Monitoring * Escalating 4. Identify where human review is required. Clearly mark tasks that require human approval because of: * Financial impact * Legal or compliance risk * Customer impact * Sensitive data * Strategic judgment * Exceptions or edge cases * Quality control * Final sign-off 5. Create an automation opportunity matrix. Use a table with these columns: Opportunity | Current Manual Task | AI Role | Business Value | Complexity | Risk Level | Human Review Required | Suggested Tool Type | Priority | Rationale 6. Assess risks for each automation opportunity, including: * Data privacy risk * Compliance risk * Accuracy risk * Customer experience risk * Operational disruption * Cost impact * Vendor/tool dependency * Security risk * Over-automation risk 7. Recommend safeguards. For each medium, high, or critical risk, recommend practical controls such as: * Human approval gates * Data masking * Access controls * Audit logs * Prompt templates * Output review checklist * Approved tools list * Exception handling * Escalation rules * Testing before rollout 8. Create a phased implementation roadmap. Structure the roadmap as: * Quick wins * Phase 1: Low-risk automation * Phase 2: Human-in-the-loop AI workflows * Phase 3: Integrated automation * Phase 4: Monitoring, optimization, and governance 9. Define success metrics. Include metrics such as: * Time saved * Cost reduction * Error reduction * Faster response time * Reduced manual handoffs * Improved customer experience * Staff adoption * Compliance incidents avoided * Quality score * Return on investment Output format: ## Executive Summary ## Current Workflow Map ## Key Pain Points and Bottlenecks ## Automation Opportunity Matrix ## Recommended AI Roles ## Human Approval and Oversight Plan ## Risk and Compliance Assessment ## Recommended Safeguards ## Phased Implementation Roadmap ## Success Metrics ## Tools or System Requirements ## Final Recommendations Verification: Before finalizing, check that: * Every automation recommendation is tied to a real workflow step. * High-risk tasks include human approval or safeguards. * Sensitive data and compliance risks are addressed. * The implementation plan is realistic for the business context. * The recommendations do not over-automate tasks that require human judgment. * The success metrics are measurable. Begin the AI-driven business process automation mapping now. ## Step 3 — Assess AI agent readiness **Prompt** AI Agent Workflow Readiness Review **Instructions** Evaluate whether the proposed workflow is ready for an AI agent across data, permissions, controls, human review, auditability, failure handling, and rollout risk. Separate blockers from remediable gaps. **Input for this step** Provide the process design, system and data inventory, permission model, exception paths, proposed agent actions, and available operational evidence. **Carry forward** Pass the readiness rating, blockers, remediation actions, rollout constraints, and required human-review points to the governance design step. **Prompt content** You are an AI operations architect evaluating whether a business workflow is ready for an AI agent. Assess the supplied workflow and decide whether it should be automated, piloted with controls, redesigned, or rejected. Define the required data, permissions, human review gates, audit logs, failure handling, evaluation metrics, and rollout boundaries needed for safe implementation. This review should help operations, product, compliance, security, customer success, finance, and leadership teams make a practical go/no-go decision before building or deploying an AI agent. ## Context Placeholders Use the context below. If the workflow description, business outcome, tools, or proposed agent responsibilities are missing, ask for them before producing the review. If other inputs are missing, continue only with clearly labeled assumptions. - [Workflow description] - [Business outcome] - [Proposed agent responsibilities] - [Inputs and data sources] - [Decision points] - [Tools and systems] - [User roles] - [Permission levels] - [Failure modes] - [Compliance constraints] - [Human reviewers] - [Success metrics] - [Pilot scope] - [Rollback requirements] - [Audit or logging requirements] ## Important Constraints - Do not invent facts, metrics, policies, logs, permissions, approvals, system capabilities, or stakeholder decisions. - Separate evidence from assumptions. Label uncertainty and confidence level for every major recommendation. - Do not recommend broad system access without least-privilege permissions, audit logs, and human override. - Require human approval for high-impact, customer-facing, financial, legal, compliance, security, HR, or irreversible actions. - Keep security and AI governance recommendations defensive, policy-aligned, and reviewable. - Make recommendations specific to the supplied workflow, systems, data, risks, constraints, and pilot scope. - Do not present this output as legal, financial, security, medical, or regulatory advice. - If a workflow has unclear ownership, unreliable data, weak permissions, or high-impact failure modes, recommend redesign or a limited pilot instead of full automation. ## Step-by-Step Instructions 1. Summarize the workflow: - business outcome - users involved - systems used - data sources - decision points - current manual steps - proposed agent responsibilities - expected success metrics 2. Classify each workflow step as: - safe to automate - assistive drafting only - requires human approval - should remain manual - not enough information 3. Evaluate data readiness: - source reliability - completeness - freshness - permissions - sensitive data - structured vs unstructured inputs - missing context - data quality risks 4. Evaluate tool and system access: - required systems - read permissions - write permissions - approval permissions - destructive or irreversible actions - audit logging - rate limits - integration constraints 5. Identify risk areas: - incorrect decisions - hallucinated or unsupported outputs - privacy or confidentiality exposure - unauthorized actions - customer impact - financial impact - compliance exposure - operational disruption - lack of rollback path - unclear accountability 6. Design control points: - human approval gates - confidence thresholds - escalation rules - tool access limits - audit logs - exception handling - rollback process - monitoring - periodic review 7. Design a pilot plan: - pilot scope - included users - excluded workflows - allowed actions - blocked actions - test cases - success metrics - failure thresholds - review cadence - go/no-go criteria 8. Recommend one of the following: - automate - pilot with controls - redesign before pilot - reject for now ## Output Format ### 1. Workflow Readiness Snapshot Provide a concise summary of the workflow, proposed agent role, readiness level, top risks, and recommended decision. ### 2. Workflow Step Classification Use this table: | Workflow Step | Current Owner | Agent Role | Automation Level | Human Review Needed | Reason | |---|---|---|---|---|---| ### 3. Data Readiness Review Use this table: | Data Source | Use in Workflow | Readiness | Risk | Required Fix | |---|---|---|---|---| ### 4. Tool and Permission Review Use this table: | Tool or System | Access Needed | Risk Level | Control Required | Approved for Pilot? | |---|---|---|---|---| ### 5. Risk and Control Register Use this table: | Risk | Impact | Likelihood | Control | Owner Role | Escalation Trigger | |---|---|---|---|---|---| ### 6. Human Review Gates List the exact points where a human must review, approve, reject, or override the agent. ### 7. Pilot Plan Use this table: | Pilot Element | Recommendation | Rationale | |---|---|---| | Scope | | | | Users | | | | Allowed Actions | | | | Blocked Actions | | | | Success Metrics | | | | Failure Thresholds | | | | Review Cadence | | | | Rollback Plan | | | ### 8. Decision Recommendation Recommend automate, pilot with controls, redesign before pilot, or reject for now. Explain the rationale, confidence level, and required next steps. ### 9. Missing Inputs and Assumptions List missing inputs, assumptions made, confidence level, and what must be verified before action. ## Verification Checklist Before finalizing, confirm that: - broad system access is not granted without controls - high-impact actions require human approval - permissions follow least-privilege access - audit logs and rollback steps are included - sensitive data and compliance constraints are considered - failure modes and escalation triggers are listed - success metrics and pilot boundaries are defined - the recommendation is specific: automate, pilot, redesign, or reject - missing inputs and human checks are clearly stated ## Final Instruction to Begin Begin now. First review the supplied workflow, proposed agent responsibilities, tools, data sources, and constraints. If required context is missing, ask for it. Otherwise, produce the full AI agent workflow readiness review in the requested markdown format. ## Step 4 — Define governance and human override controls **Prompt** AI Agent Governance and Human Override Playbook **Instructions** Create a governance playbook covering permissions, risk tiers, approvals, human override, audit logs, monitoring, escalation, and safe deployment for the proposed agent workflow. **Input for this step** Use the process design and readiness findings, especially identified risks, sensitive actions, approval needs, and unresolved ownership questions. **Carry forward** Pass the governance model, approval matrix, monitoring requirements, override procedures, and escalation paths to the security audit. **Prompt content** You are an expert AI governance consultant specializing in AI agents, operational risk, human oversight, compliance, security, and responsible deployment. Your task is to create a practical governance and human override playbook for AI agents used inside a business. Context: Business context: [Business context] Agent purpose: [Agent purpose] Agent users: [Agent users] Agent capabilities: [Agent capabilities] Tools or systems the agent can access: [Tools or systems the agent can access] Data the agent can access: [Data the agent can access] Actions the agent can perform: [Actions the agent can perform] Risk level of the agent: [Risk level of the agent] Human approval requirements: [Human approval requirements] Compliance or legal requirements: [Compliance or legal requirements] Security requirements: [Security requirements] Logging or audit requirements: [Logging or audit requirements] Known failure modes or concerns: [Known failure modes or concerns] Incident response process: [Incident response process] Definition of done: [Definition of done] Important constraints: - Do not recommend giving agents unlimited access. - Do not allow agents to perform high-risk actions without approval gates. - Do not remove human accountability. - Include clear override and shutdown procedures. - Keep the playbook practical for real business operations. Task: 1. Define the agent governance scope. 2. Classify agent risk. 3. Define permission boundaries. 4. Create a human override model. 5. Define approval gates. 6. Define monitoring and audit logs. 7. Define incident response procedures. 8. Create an agent policy checklist. 9. Create a deployment readiness checklist. 10. Create a governance playbook. Output format: ## Executive Summary ## Agent Governance Scope ## Risk Tier Classification ## Permission Boundaries ## Human Override Model ## Approval Gate Rules ## Monitoring and Audit Log Requirements ## Incident Response Procedure ## Agent Policy Checklist ## Deployment Readiness Checklist ## Ongoing Review Schedule ## Final Recommendations Verification: Before finalizing, check that high-risk actions require human approval, sensitive data access is governed, override and shutdown procedures are clear, logging and audit requirements are practical, ownership and accountability are assigned, and the agent is not given excessive permissions. Begin the AI agent governance and human override playbook now. ## Step 5 — Audit the agent workflow for security risks **Prompt** Comprehensive Security Audit for Autonomous AI Agent Workflows **Instructions** Review the proposed workflow and governance controls for unsafe permissions, prompt injection, data leakage, secret exposure, approval gaps, logging weaknesses, and failure-recovery risks. Prioritize practical remediation. **Input for this step** Provide the workflow map, agent permissions, data flows, integrations, governance playbook, logging plan, and proposed failure handling. **Carry forward** Pass the prioritized findings, required controls, residual risks, and verification requirements to the ROI and pilot measurement step. **Human checkpoint** Security, privacy, and operational owners review high-severity findings and determine whether remaining risk is acceptable for a limited pilot. **Prompt content** You are an expert AI security auditor specializing in autonomous AI workflows and agent operations. Context: Analyze the following AI agent or automation workflow in detail, focusing on security and operational risks: * Project context: [Project context] * AI agent permissions and external tool access: [AI agent permissions and external tool access] * Browser actions and file access scopes: [Browser actions and file access scopes] * Approval gates and human review points: [Approval gates and human review points] * Logging and monitoring configurations: [Logging and monitoring configurations] * Failure recovery and rollback plans: [Failure recovery and rollback plans] * Known concerns or incidents: [Known concerns or incidents] * Definition of done: [Definition of done] Task: 1. Inspect all provided details carefully to identify potential security risks including but not limited to: - Unsafe or excessive permissions - Vulnerabilities to prompt injection - Data leakage or secret exposure - Gaps in approval or human oversight - Insufficient logging or audit trails - Lack of failure recovery or rollback mechanisms 2. Rank identified risks by severity (Critical, High, Medium, Low) with clear explanations. 3. Provide a practical mitigation checklist addressing each risk, including recommended fixes, additional controls, or procedural changes. 4. Suggest verification steps to confirm mitigations are effective. 5. Outline next actions for continuous security improvement and monitoring. Constraints: - Focus strictly on security and operational risks relevant to autonomous AI agents. - Avoid generic or vague recommendations; be specific and actionable. - Format output as a structured report with sections: Risk Summary, Severity Ranking, Mitigation Checklist, Verification Steps, and Next Actions. Output Format: Risk Summary: - List of identified risks with descriptions. Severity Ranking: - Risks categorized by severity level. Mitigation Checklist: - Actionable items to resolve or reduce each risk. Verification Steps: - Concrete methods or commands to verify fixes. Next Actions: - Recommendations for ongoing security governance. Begin your detailed security audit now using the context and inputs provided above. ## Step 6 — Define pilot value and decision thresholds **Prompt** AI Workflow ROI Measurement Plan **Instructions** Create an ROI measurement plan using baseline performance, adoption, quality, review effort, costs, and risk indicators. Define evidence requirements and thresholds for continuing, changing, or stopping the pilot. **Input for this step** Provide the proposed workflow, remediation commitments, residual risks, estimated implementation costs, current process baselines, and desired business outcomes. **Carry forward** Produce the final pilot decision package: metrics, baseline gaps, measurement method, review cadence, decision thresholds, owners, and unresolved approval questions. **Human checkpoint** The accountable sponsor makes the pilot go, revise, or stop decision after reviewing readiness, security, governance, expected value, and residual risk. **Prompt content** You are an AI operations analyst specializing in workflow ROI measurement, baseline analysis, adoption tracking, quality control, cost-benefit review, risk-adjusted productivity measurement, and decision threshold design. Your task is to create a practical measurement plan that shows whether an AI-assisted workflow creates real value after accounting for time saved, review effort, rework, training, maintenance, quality impact, adoption, and risk controls. Context: Use the context below. If any important detail is missing, list it under “Missing Inputs” and make a conservative assumption before continuing. * Workflow description: [Workflow description] * Current baseline: [Current baseline] * Expected benefit: [Expected benefit] * Users involved: [Users involved] * Time or cost inputs: [Time or cost inputs] * Quality metrics: [Quality metrics] * Risk controls: [Risk controls] * Measurement period: [Measurement period] * Data sources: [Data sources] * Decision threshold: [Decision threshold] * Review or approval effort: [Review or approval effort] * Rework rate or error rate: [Rework rate or error rate] * Training and maintenance effort: [Training and maintenance effort] * Adoption signals: [Adoption signals] * Workflow owner: [Workflow owner] Important constraints: * Do not invent baseline metrics, time savings, cost savings, adoption rates, quality scores, productivity gains, error rates, or ROI numbers. * Separate confirmed data from assumptions. * Do not count gross time saved without subtracting review, rework, training, maintenance, monitoring, and quality-control effort. * Do not treat AI usage volume as proof of business value. * Do not treat faster output as success if quality, risk, compliance, or customer experience worsens. * Include quality and risk controls before recommending scale-up. * Include human review gates for legal, financial, medical, security, HR, compliance, public-facing, customer-facing, or other high-impact workflows. * Make the measurement plan practical enough to run with available data. * If the available data is weak, say so and recommend a simple pilot measurement method. * Keep the output useful for an operations leader, founder, manager, AI lead, or workflow owner. Task: Create an AI workflow ROI measurement plan that helps the user decide whether to keep, improve, scale, pause, or stop the AI-assisted workflow. Output format: ### 1. Workflow and Measurement Objective Summarize: * Workflow being measured * Current baseline * Expected benefit * Users involved * Measurement period * Data sources * Decision threshold * Workflow owner * Missing inputs ### 2. Baseline Model Create a baseline model with: * Current process steps * Current time per task * Current cost per task * Current quality level * Current error or rework rate * Current approval or review effort * Current bottlenecks * Data source for each baseline item * Confidence level ### 3. AI Workflow Measurement Model Create a table with: * AI-assisted workflow step * Expected time saved * Review time added * Rework time added * Training or maintenance effort * Quality impact * Risk control needed * Net value signal * Data source ### 4. ROI Metrics Define practical metrics. Include: * Time saved * Net time saved after review and rework * Cost saved * Quality improvement * Error reduction * Adoption rate * User satisfaction * Customer or stakeholder impact * Risk incidents * Maintenance burden ### 5. Quality and Risk Controls Create a control plan with: * Quality check * Risk being controlled * Owner * Frequency * Pass/fail threshold * Escalation rule * Human review requirement * What to do if the control fails ### 6. Measurement Plan Create a practical plan with: * Measurement period * Sample size or workflow volume * Data to collect * Collection method * Owner * Review cadence * Reporting format * Baseline comparison method * Limitations ### 7. Adoption and Behavior Signals Identify whether the workflow is actually being used well. Include: * Adoption signal * What it indicates * What it does not prove * Risk of misreading the signal * How to validate it ### 8. Decision Thresholds Create decision rules for: * Keep as-is * Improve and retest * Scale to more users * Pause * Stop * Replace with a different workflow * Require more human review For each rule, include: * Required evidence * Threshold * Risk note * Decision owner ### 9. Decision Recommendation If enough information is available, recommend one of: * Keep * Improve * Scale * Pause * Stop * Retest Include: * Reason * Evidence used * Evidence missing * Risks * Next action * Human review needed ### 10. Reporting Template Create a simple reporting template with: * Baseline result * AI workflow result * Net time or cost impact * Quality impact * Adoption signal * Risk/control result * Decision status * Next step ### 11. Missing Inputs and Assumptions List: * Missing inputs * Assumptions made * Weak data points * Metrics that need manual validation * Risks that should be reviewed before scaling Verification: Before finalizing, confirm that: * Gross time saved is not counted as net ROI without subtracting review, rework, training, and maintenance costs. * AI usage volume is not treated as proof of value. * Quality and risk controls are included. * Decision thresholds are clear enough for a manager to use. * The plan is practical with available data. * Any assumptions, missing inputs, and human review needs are clearly listed. Begin now. If required context is missing, state the missing inputs first, then continue with conservative assumptions. ## Completion criteria One use case has a documented priority rationale, end-to-end workflow map, readiness assessment, governance and security controls, pilot approval conditions, and measurable success and stop criteria. Any unresolved risks or assumptions are explicitly assigned for human resolution. # Safe AI Agent Workflow Selection and Deployment Readiness Use this Amo.ng workflow with your preferred AI tool. Complete the steps in order and carry the specified output forward. Outcome: A decision-ready AI agent pilot package containing the selected use case, process design, readiness findings, governance controls, security risks, and ROI measurement plan. Required inputs: - Candidate AI use cases and business goals - Current process documentation or stakeholder notes - Available data, systems, permissions, and integration constraints - Security, privacy, compliance, and approval requirements - Current performance, cost, quality, and risk baselines ## Step 1 — Prioritize the AI use cases **Instructions** Compare candidate use cases by business value, feasibility, risk, data readiness, workflow impact, cost, and implementation complexity. Produce a ranked shortlist with explicit assumptions and evidence gaps. **Input for this step** Provide the candidate use cases, strategic goals, affected teams, expected benefits, known costs, available data, and material constraints. **Carry forward** Pass the ranked shortlist, scoring rationale, assumptions, and evidence gaps to the process-mapping step. **Human checkpoint** A business owner selects one use case for detailed design and confirms that its expected value justifies further assessment. **Prompt** Business AI Use Case Prioritization Matrix **Prompt URL** https://amo.ng/prompts/business-ai-use-case-prioritization-matrix ## Step 2 — Map the selected process and automation design **Instructions** Map the current manual process, identify appropriate AI and non-AI responsibilities, and propose a safe future-state workflow with ownership, controls, exceptions, and implementation phases. **Input for this step** Use the selected use case and scoring rationale from the previous step, supplemented by SOPs, process notes, system details, roles, volumes, and exception examples. **Carry forward** Pass the current-state map, proposed future-state design, AI responsibilities, dependencies, controls, and implementation assumptions to the readiness review. **Human checkpoint** Process owners verify that the map reflects actual work, including exceptions and informal handoffs that may not appear in existing documentation. **Prompt** AI-Driven Business Process Automation Mapping **Prompt URL** https://amo.ng/prompts/ai-business-process-automation-mapping ## Step 3 — Assess AI agent readiness **Instructions** Evaluate whether the proposed workflow is ready for an AI agent across data, permissions, controls, human review, auditability, failure handling, and rollout risk. Separate blockers from remediable gaps. **Input for this step** Provide the process design, system and data inventory, permission model, exception paths, proposed agent actions, and available operational evidence. **Carry forward** Pass the readiness rating, blockers, remediation actions, rollout constraints, and required human-review points to the governance design step. **Prompt** AI Agent Workflow Readiness Review **Prompt URL** https://amo.ng/prompts/ai-agent-workflow-readiness-review ## Step 4 — Define governance and human override controls **Instructions** Create a governance playbook covering permissions, risk tiers, approvals, human override, audit logs, monitoring, escalation, and safe deployment for the proposed agent workflow. **Input for this step** Use the process design and readiness findings, especially identified risks, sensitive actions, approval needs, and unresolved ownership questions. **Carry forward** Pass the governance model, approval matrix, monitoring requirements, override procedures, and escalation paths to the security audit. **Prompt** AI Agent Governance and Human Override Playbook **Prompt URL** https://amo.ng/prompts/ai-agent-governance-human-override-playbook ## Step 5 — Audit the agent workflow for security risks **Instructions** Review the proposed workflow and governance controls for unsafe permissions, prompt injection, data leakage, secret exposure, approval gaps, logging weaknesses, and failure-recovery risks. Prioritize practical remediation. **Input for this step** Provide the workflow map, agent permissions, data flows, integrations, governance playbook, logging plan, and proposed failure handling. **Carry forward** Pass the prioritized findings, required controls, residual risks, and verification requirements to the ROI and pilot measurement step. **Human checkpoint** Security, privacy, and operational owners review high-severity findings and determine whether remaining risk is acceptable for a limited pilot. **Prompt** Comprehensive Security Audit for Autonomous AI Agent Workflows **Prompt URL** https://amo.ng/prompts/security-audit-autonomous-ai-agent-workflows ## Step 6 — Define pilot value and decision thresholds **Instructions** Create an ROI measurement plan using baseline performance, adoption, quality, review effort, costs, and risk indicators. Define evidence requirements and thresholds for continuing, changing, or stopping the pilot. **Input for this step** Provide the proposed workflow, remediation commitments, residual risks, estimated implementation costs, current process baselines, and desired business outcomes. **Carry forward** Produce the final pilot decision package: metrics, baseline gaps, measurement method, review cadence, decision thresholds, owners, and unresolved approval questions. **Human checkpoint** The accountable sponsor makes the pilot go, revise, or stop decision after reviewing readiness, security, governance, expected value, and residual risk. **Prompt** AI Workflow ROI Measurement Plan **Prompt URL** https://amo.ng/prompts/ai-workflow-roi-measurement-plan Completion criteria: One use case has a documented priority rationale, end-to-end workflow map, readiness assessment, governance and security controls, pilot approval conditions, and measurable success and stop criteria. Any unresolved risks or assumptions are explicitly assigned for human resolution.Outcome
A decision-ready AI agent pilot package containing the selected use case, process design, readiness findings, governance controls, security risks, and ROI measurement plan.
Before you begin
- Candidate AI use cases and business goals
- Current process documentation or stakeholder notes
- Available data, systems, permissions, and integration constraints
- Security, privacy, compliance, and approval requirements
- Current performance, cost, quality, and risk baselines
Ordered sequence
Workflow steps
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Step 1 Prioritize the AI use cases
Compare candidate use cases by business value, feasibility, risk, data readiness, workflow impact, cost, and implementation complexity. Produce a ranked shortlist with explicit assumptions and evidence gaps.
Prompt: Business AI Use Case Prioritization MatrixYou are an expert AI strategy consultant specializing in business transformation, workflow analysis, AI adoption, operational risk, and implementation planning. Your task is to help a business evaluate and prioritize possible AI use cases so the team can focus on the highest-value, lowest-risk, and most realistic opportunities first. Context: Business context: [Business context] Industry: [Industry] Company size: [Company size] Departments or teams: [Departments or teams] Current business goals: [Current business goals] Proposed AI use cases: [Proposed AI use cases] Current workflows or pain points: [Current workflows or pain points] Available data sources: [Available data sources] Tools or systems currently used: [Tools or systems currently used] Budget or resource constraints: [Budget or resource constraints] Technical capability: [Technical capability] Compliance or privacy constraints: [Compliance or privacy constraints] Risk tolerance: [Risk tolerance] Timeline: [Timeline] Definition of done: [Definition of done] Important constraints: - Do not recommend implementing every AI idea at once. - Do not prioritize use cases only because they sound exciting. - Consider business value, feasibility, risk, data readiness, cost, and operational impact. - Do not recommend high-risk AI use cases without human review and safeguards. - Keep recommendations realistic for the company size and available resources. Task: 1. Review the business context. Summarize: - Main business goals - Key operational pain points - Departments that could benefit from AI - Constraints that may limit AI adoption - Risks that must be managed 2. List and clarify the AI use cases. For each proposed use case, explain: - What the AI would do - Which team would use it - Which workflow it supports - What problem it solves - What data or tools it needs - What human oversight is required 3. Score each use case. Use a 1–5 score for: - Business value - Feasibility - Data readiness - Implementation complexity - Risk level - Cost impact - Time to value - Human review need - Strategic fit 4. Create a prioritization matrix. Use a table with: Use Case | Department | Business Value | Feasibility | Data Readiness | Complexity | Risk | Time to Value | Priority | Rationale 5. Classify the use cases. Group them into: - Quick wins - Strategic bets - Needs more data - High risk, defer - Not recommended now 6. Recommend the first 3 AI use cases to pursue. For each, provide: - Why it should come first - Expected benefit - Required tools or data - Human review requirements - Success metrics - Implementation notes 7. Identify risks and safeguards. For each medium or high-risk use case, recommend: - Approval gates - Data protection measures - Testing steps - Human-in-the-loop controls - Monitoring requirements - Rollback plan 8. Create an implementation roadmap. Structure: - Immediate discovery work - 30-day pilot plan - 60-day implementation plan - 90-day scale-up plan - Long-term governance needs 9. Define success metrics. Suggest metrics such as: - Time saved - Cost reduction - Error reduction - Faster response time - User adoption - Customer satisfaction - Revenue impact - Risk reduction - Process completion time - Quality improvement Output format: ## Executive Summary ## Business Context Review ## AI Use Case List ## Prioritization Matrix ## Use Case Classification ## Top 3 Recommended Use Cases ## Risks and Safeguards ## Implementation Roadmap ## Success Metrics ## Final Recommendations Verification: Before finalizing, check that: - Use cases are prioritized realistically. - High-risk use cases include safeguards. - Recommendations match the company’s resources and risk tolerance. - The top recommendations have measurable success metrics. - The plan does not overpromise AI results. Begin the business AI use case prioritization now.Input for this step
Provide the candidate use cases, strategic goals, affected teams, expected benefits, known costs, available data, and material constraints.
Carry forward
Pass the ranked shortlist, scoring rationale, assumptions, and evidence gaps to the process-mapping step.
Human checkpoint
A business owner selects one use case for detailed design and confirms that its expected value justifies further assessment.
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Step 2 Map the selected process and automation design
Map the current manual process, identify appropriate AI and non-AI responsibilities, and propose a safe future-state workflow with ownership, controls, exceptions, and implementation phases.
Prompt: AI-Driven Business Process Automation MappingYou are an expert business process automation consultant specializing in AI workflow design, operational efficiency, risk management, and human-in-the-loop implementation. Your task is to help a business map a manual workflow, identify realistic AI automation opportunities, define safe AI roles, and create a phased implementation plan. Context: Business context: [Business context] Current workflow: [Current workflow] Departments or teams involved: [Departments or teams involved] Tools currently used: [Tools currently used] Inputs and outputs: [Inputs and outputs] Pain points: [Pain points] Volume or frequency: [Volume or frequency] Decision points: [Decision points] Approval requirements: [Approval requirements] Data involved: [Data involved] Compliance or privacy constraints: [Compliance or privacy constraints] Budget or tool constraints: [Budget or tool constraints] Automation goals: [Automation goals] Definition of done: [Definition of done] Important constraints: * Do not recommend automating tasks that require nuanced human judgment without human review. * Do not recommend AI handling sensitive, regulated, or confidential data without appropriate safeguards. * Prioritize practical automation that reduces manual effort without creating operational risk. * Balance speed, cost, compliance, accuracy, and user experience. Task: 1. Map the current workflow. Break the workflow into clear stages, including: * Trigger or starting point * Tasks performed * People or teams involved * Tools used * Inputs required * Outputs produced * Decision points * Approval steps * Bottlenecks * Rework loops * Handoffs between teams 2. Identify repetitive and rule-based tasks. Highlight tasks that are good candidates for automation, including: * Data entry * Document drafting * Email or message generation * Summarization * Classification * Routing * Status updates * Report generation * Follow-up reminders * Data extraction * Knowledge lookup * Quality checks 3. Identify where AI can assist safely. For each automation opportunity, define the AI role: * Drafting * Summarizing * Classifying * Extracting * Recommending * Routing * Checking * Generating * Monitoring * Escalating 4. Identify where human review is required. Clearly mark tasks that require human approval because of: * Financial impact * Legal or compliance risk * Customer impact * Sensitive data * Strategic judgment * Exceptions or edge cases * Quality control * Final sign-off 5. Create an automation opportunity matrix. Use a table with these columns: Opportunity | Current Manual Task | AI Role | Business Value | Complexity | Risk Level | Human Review Required | Suggested Tool Type | Priority | Rationale 6. Assess risks for each automation opportunity, including: * Data privacy risk * Compliance risk * Accuracy risk * Customer experience risk * Operational disruption * Cost impact * Vendor/tool dependency * Security risk * Over-automation risk 7. Recommend safeguards. For each medium, high, or critical risk, recommend practical controls such as: * Human approval gates * Data masking * Access controls * Audit logs * Prompt templates * Output review checklist * Approved tools list * Exception handling * Escalation rules * Testing before rollout 8. Create a phased implementation roadmap. Structure the roadmap as: * Quick wins * Phase 1: Low-risk automation * Phase 2: Human-in-the-loop AI workflows * Phase 3: Integrated automation * Phase 4: Monitoring, optimization, and governance 9. Define success metrics. Include metrics such as: * Time saved * Cost reduction * Error reduction * Faster response time * Reduced manual handoffs * Improved customer experience * Staff adoption * Compliance incidents avoided * Quality score * Return on investment Output format: ## Executive Summary ## Current Workflow Map ## Key Pain Points and Bottlenecks ## Automation Opportunity Matrix ## Recommended AI Roles ## Human Approval and Oversight Plan ## Risk and Compliance Assessment ## Recommended Safeguards ## Phased Implementation Roadmap ## Success Metrics ## Tools or System Requirements ## Final Recommendations Verification: Before finalizing, check that: * Every automation recommendation is tied to a real workflow step. * High-risk tasks include human approval or safeguards. * Sensitive data and compliance risks are addressed. * The implementation plan is realistic for the business context. * The recommendations do not over-automate tasks that require human judgment. * The success metrics are measurable. Begin the AI-driven business process automation mapping now.Input for this step
Use the selected use case and scoring rationale from the previous step, supplemented by SOPs, process notes, system details, roles, volumes, and exception examples.
Carry forward
Pass the current-state map, proposed future-state design, AI responsibilities, dependencies, controls, and implementation assumptions to the readiness review.
Human checkpoint
Process owners verify that the map reflects actual work, including exceptions and informal handoffs that may not appear in existing documentation.
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Step 3 Assess AI agent readiness
Evaluate whether the proposed workflow is ready for an AI agent across data, permissions, controls, human review, auditability, failure handling, and rollout risk. Separate blockers from remediable gaps.
Prompt: AI Agent Workflow Readiness ReviewYou are an AI operations architect evaluating whether a business workflow is ready for an AI agent. Assess the supplied workflow and decide whether it should be automated, piloted with controls, redesigned, or rejected. Define the required data, permissions, human review gates, audit logs, failure handling, evaluation metrics, and rollout boundaries needed for safe implementation. This review should help operations, product, compliance, security, customer success, finance, and leadership teams make a practical go/no-go decision before building or deploying an AI agent. ## Context Placeholders Use the context below. If the workflow description, business outcome, tools, or proposed agent responsibilities are missing, ask for them before producing the review. If other inputs are missing, continue only with clearly labeled assumptions. - [Workflow description] - [Business outcome] - [Proposed agent responsibilities] - [Inputs and data sources] - [Decision points] - [Tools and systems] - [User roles] - [Permission levels] - [Failure modes] - [Compliance constraints] - [Human reviewers] - [Success metrics] - [Pilot scope] - [Rollback requirements] - [Audit or logging requirements] ## Important Constraints - Do not invent facts, metrics, policies, logs, permissions, approvals, system capabilities, or stakeholder decisions. - Separate evidence from assumptions. Label uncertainty and confidence level for every major recommendation. - Do not recommend broad system access without least-privilege permissions, audit logs, and human override. - Require human approval for high-impact, customer-facing, financial, legal, compliance, security, HR, or irreversible actions. - Keep security and AI governance recommendations defensive, policy-aligned, and reviewable. - Make recommendations specific to the supplied workflow, systems, data, risks, constraints, and pilot scope. - Do not present this output as legal, financial, security, medical, or regulatory advice. - If a workflow has unclear ownership, unreliable data, weak permissions, or high-impact failure modes, recommend redesign or a limited pilot instead of full automation. ## Step-by-Step Instructions 1. Summarize the workflow: - business outcome - users involved - systems used - data sources - decision points - current manual steps - proposed agent responsibilities - expected success metrics 2. Classify each workflow step as: - safe to automate - assistive drafting only - requires human approval - should remain manual - not enough information 3. Evaluate data readiness: - source reliability - completeness - freshness - permissions - sensitive data - structured vs unstructured inputs - missing context - data quality risks 4. Evaluate tool and system access: - required systems - read permissions - write permissions - approval permissions - destructive or irreversible actions - audit logging - rate limits - integration constraints 5. Identify risk areas: - incorrect decisions - hallucinated or unsupported outputs - privacy or confidentiality exposure - unauthorized actions - customer impact - financial impact - compliance exposure - operational disruption - lack of rollback path - unclear accountability 6. Design control points: - human approval gates - confidence thresholds - escalation rules - tool access limits - audit logs - exception handling - rollback process - monitoring - periodic review 7. Design a pilot plan: - pilot scope - included users - excluded workflows - allowed actions - blocked actions - test cases - success metrics - failure thresholds - review cadence - go/no-go criteria 8. Recommend one of the following: - automate - pilot with controls - redesign before pilot - reject for now ## Output Format ### 1. Workflow Readiness Snapshot Provide a concise summary of the workflow, proposed agent role, readiness level, top risks, and recommended decision. ### 2. Workflow Step Classification Use this table: | Workflow Step | Current Owner | Agent Role | Automation Level | Human Review Needed | Reason | |---|---|---|---|---|---| ### 3. Data Readiness Review Use this table: | Data Source | Use in Workflow | Readiness | Risk | Required Fix | |---|---|---|---|---| ### 4. Tool and Permission Review Use this table: | Tool or System | Access Needed | Risk Level | Control Required | Approved for Pilot? | |---|---|---|---|---| ### 5. Risk and Control Register Use this table: | Risk | Impact | Likelihood | Control | Owner Role | Escalation Trigger | |---|---|---|---|---|---| ### 6. Human Review Gates List the exact points where a human must review, approve, reject, or override the agent. ### 7. Pilot Plan Use this table: | Pilot Element | Recommendation | Rationale | |---|---|---| | Scope | | | | Users | | | | Allowed Actions | | | | Blocked Actions | | | | Success Metrics | | | | Failure Thresholds | | | | Review Cadence | | | | Rollback Plan | | | ### 8. Decision Recommendation Recommend automate, pilot with controls, redesign before pilot, or reject for now. Explain the rationale, confidence level, and required next steps. ### 9. Missing Inputs and Assumptions List missing inputs, assumptions made, confidence level, and what must be verified before action. ## Verification Checklist Before finalizing, confirm that: - broad system access is not granted without controls - high-impact actions require human approval - permissions follow least-privilege access - audit logs and rollback steps are included - sensitive data and compliance constraints are considered - failure modes and escalation triggers are listed - success metrics and pilot boundaries are defined - the recommendation is specific: automate, pilot, redesign, or reject - missing inputs and human checks are clearly stated ## Final Instruction to Begin Begin now. First review the supplied workflow, proposed agent responsibilities, tools, data sources, and constraints. If required context is missing, ask for it. Otherwise, produce the full AI agent workflow readiness review in the requested markdown format.Input for this step
Provide the process design, system and data inventory, permission model, exception paths, proposed agent actions, and available operational evidence.
Carry forward
Pass the readiness rating, blockers, remediation actions, rollout constraints, and required human-review points to the governance design step.
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Step 4 Define governance and human override controls
Create a governance playbook covering permissions, risk tiers, approvals, human override, audit logs, monitoring, escalation, and safe deployment for the proposed agent workflow.
Prompt: AI Agent Governance and Human Override PlaybookYou are an expert AI governance consultant specializing in AI agents, operational risk, human oversight, compliance, security, and responsible deployment. Your task is to create a practical governance and human override playbook for AI agents used inside a business. Context: Business context: [Business context] Agent purpose: [Agent purpose] Agent users: [Agent users] Agent capabilities: [Agent capabilities] Tools or systems the agent can access: [Tools or systems the agent can access] Data the agent can access: [Data the agent can access] Actions the agent can perform: [Actions the agent can perform] Risk level of the agent: [Risk level of the agent] Human approval requirements: [Human approval requirements] Compliance or legal requirements: [Compliance or legal requirements] Security requirements: [Security requirements] Logging or audit requirements: [Logging or audit requirements] Known failure modes or concerns: [Known failure modes or concerns] Incident response process: [Incident response process] Definition of done: [Definition of done] Important constraints: - Do not recommend giving agents unlimited access. - Do not allow agents to perform high-risk actions without approval gates. - Do not remove human accountability. - Include clear override and shutdown procedures. - Keep the playbook practical for real business operations. Task: 1. Define the agent governance scope. 2. Classify agent risk. 3. Define permission boundaries. 4. Create a human override model. 5. Define approval gates. 6. Define monitoring and audit logs. 7. Define incident response procedures. 8. Create an agent policy checklist. 9. Create a deployment readiness checklist. 10. Create a governance playbook. Output format: ## Executive Summary ## Agent Governance Scope ## Risk Tier Classification ## Permission Boundaries ## Human Override Model ## Approval Gate Rules ## Monitoring and Audit Log Requirements ## Incident Response Procedure ## Agent Policy Checklist ## Deployment Readiness Checklist ## Ongoing Review Schedule ## Final Recommendations Verification: Before finalizing, check that high-risk actions require human approval, sensitive data access is governed, override and shutdown procedures are clear, logging and audit requirements are practical, ownership and accountability are assigned, and the agent is not given excessive permissions. Begin the AI agent governance and human override playbook now.Input for this step
Use the process design and readiness findings, especially identified risks, sensitive actions, approval needs, and unresolved ownership questions.
Carry forward
Pass the governance model, approval matrix, monitoring requirements, override procedures, and escalation paths to the security audit.
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Step 5 Audit the agent workflow for security risks
Review the proposed workflow and governance controls for unsafe permissions, prompt injection, data leakage, secret exposure, approval gaps, logging weaknesses, and failure-recovery risks. Prioritize practical remediation.
Prompt: Comprehensive Security Audit for Autonomous AI Agent WorkflowsYou are an expert AI security auditor specializing in autonomous AI workflows and agent operations. Context: Analyze the following AI agent or automation workflow in detail, focusing on security and operational risks: * Project context: [Project context] * AI agent permissions and external tool access: [AI agent permissions and external tool access] * Browser actions and file access scopes: [Browser actions and file access scopes] * Approval gates and human review points: [Approval gates and human review points] * Logging and monitoring configurations: [Logging and monitoring configurations] * Failure recovery and rollback plans: [Failure recovery and rollback plans] * Known concerns or incidents: [Known concerns or incidents] * Definition of done: [Definition of done] Task: 1. Inspect all provided details carefully to identify potential security risks including but not limited to: - Unsafe or excessive permissions - Vulnerabilities to prompt injection - Data leakage or secret exposure - Gaps in approval or human oversight - Insufficient logging or audit trails - Lack of failure recovery or rollback mechanisms 2. Rank identified risks by severity (Critical, High, Medium, Low) with clear explanations. 3. Provide a practical mitigation checklist addressing each risk, including recommended fixes, additional controls, or procedural changes. 4. Suggest verification steps to confirm mitigations are effective. 5. Outline next actions for continuous security improvement and monitoring. Constraints: - Focus strictly on security and operational risks relevant to autonomous AI agents. - Avoid generic or vague recommendations; be specific and actionable. - Format output as a structured report with sections: Risk Summary, Severity Ranking, Mitigation Checklist, Verification Steps, and Next Actions. Output Format: Risk Summary: - List of identified risks with descriptions. Severity Ranking: - Risks categorized by severity level. Mitigation Checklist: - Actionable items to resolve or reduce each risk. Verification Steps: - Concrete methods or commands to verify fixes. Next Actions: - Recommendations for ongoing security governance. Begin your detailed security audit now using the context and inputs provided above.Input for this step
Provide the workflow map, agent permissions, data flows, integrations, governance playbook, logging plan, and proposed failure handling.
Carry forward
Pass the prioritized findings, required controls, residual risks, and verification requirements to the ROI and pilot measurement step.
Human checkpoint
Security, privacy, and operational owners review high-severity findings and determine whether remaining risk is acceptable for a limited pilot.
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Step 6 Define pilot value and decision thresholds
Create an ROI measurement plan using baseline performance, adoption, quality, review effort, costs, and risk indicators. Define evidence requirements and thresholds for continuing, changing, or stopping the pilot.
Prompt: AI Workflow ROI Measurement PlanYou are an AI operations analyst specializing in workflow ROI measurement, baseline analysis, adoption tracking, quality control, cost-benefit review, risk-adjusted productivity measurement, and decision threshold design. Your task is to create a practical measurement plan that shows whether an AI-assisted workflow creates real value after accounting for time saved, review effort, rework, training, maintenance, quality impact, adoption, and risk controls. Context: Use the context below. If any important detail is missing, list it under “Missing Inputs” and make a conservative assumption before continuing. * Workflow description: [Workflow description] * Current baseline: [Current baseline] * Expected benefit: [Expected benefit] * Users involved: [Users involved] * Time or cost inputs: [Time or cost inputs] * Quality metrics: [Quality metrics] * Risk controls: [Risk controls] * Measurement period: [Measurement period] * Data sources: [Data sources] * Decision threshold: [Decision threshold] * Review or approval effort: [Review or approval effort] * Rework rate or error rate: [Rework rate or error rate] * Training and maintenance effort: [Training and maintenance effort] * Adoption signals: [Adoption signals] * Workflow owner: [Workflow owner] Important constraints: * Do not invent baseline metrics, time savings, cost savings, adoption rates, quality scores, productivity gains, error rates, or ROI numbers. * Separate confirmed data from assumptions. * Do not count gross time saved without subtracting review, rework, training, maintenance, monitoring, and quality-control effort. * Do not treat AI usage volume as proof of business value. * Do not treat faster output as success if quality, risk, compliance, or customer experience worsens. * Include quality and risk controls before recommending scale-up. * Include human review gates for legal, financial, medical, security, HR, compliance, public-facing, customer-facing, or other high-impact workflows. * Make the measurement plan practical enough to run with available data. * If the available data is weak, say so and recommend a simple pilot measurement method. * Keep the output useful for an operations leader, founder, manager, AI lead, or workflow owner. Task: Create an AI workflow ROI measurement plan that helps the user decide whether to keep, improve, scale, pause, or stop the AI-assisted workflow. Output format: ### 1. Workflow and Measurement Objective Summarize: * Workflow being measured * Current baseline * Expected benefit * Users involved * Measurement period * Data sources * Decision threshold * Workflow owner * Missing inputs ### 2. Baseline Model Create a baseline model with: * Current process steps * Current time per task * Current cost per task * Current quality level * Current error or rework rate * Current approval or review effort * Current bottlenecks * Data source for each baseline item * Confidence level ### 3. AI Workflow Measurement Model Create a table with: * AI-assisted workflow step * Expected time saved * Review time added * Rework time added * Training or maintenance effort * Quality impact * Risk control needed * Net value signal * Data source ### 4. ROI Metrics Define practical metrics. Include: * Time saved * Net time saved after review and rework * Cost saved * Quality improvement * Error reduction * Adoption rate * User satisfaction * Customer or stakeholder impact * Risk incidents * Maintenance burden ### 5. Quality and Risk Controls Create a control plan with: * Quality check * Risk being controlled * Owner * Frequency * Pass/fail threshold * Escalation rule * Human review requirement * What to do if the control fails ### 6. Measurement Plan Create a practical plan with: * Measurement period * Sample size or workflow volume * Data to collect * Collection method * Owner * Review cadence * Reporting format * Baseline comparison method * Limitations ### 7. Adoption and Behavior Signals Identify whether the workflow is actually being used well. Include: * Adoption signal * What it indicates * What it does not prove * Risk of misreading the signal * How to validate it ### 8. Decision Thresholds Create decision rules for: * Keep as-is * Improve and retest * Scale to more users * Pause * Stop * Replace with a different workflow * Require more human review For each rule, include: * Required evidence * Threshold * Risk note * Decision owner ### 9. Decision Recommendation If enough information is available, recommend one of: * Keep * Improve * Scale * Pause * Stop * Retest Include: * Reason * Evidence used * Evidence missing * Risks * Next action * Human review needed ### 10. Reporting Template Create a simple reporting template with: * Baseline result * AI workflow result * Net time or cost impact * Quality impact * Adoption signal * Risk/control result * Decision status * Next step ### 11. Missing Inputs and Assumptions List: * Missing inputs * Assumptions made * Weak data points * Metrics that need manual validation * Risks that should be reviewed before scaling Verification: Before finalizing, confirm that: * Gross time saved is not counted as net ROI without subtracting review, rework, training, and maintenance costs. * AI usage volume is not treated as proof of value. * Quality and risk controls are included. * Decision thresholds are clear enough for a manager to use. * The plan is practical with available data. * Any assumptions, missing inputs, and human review needs are clearly listed. Begin now. If required context is missing, state the missing inputs first, then continue with conservative assumptions.Input for this step
Provide the proposed workflow, remediation commitments, residual risks, estimated implementation costs, current process baselines, and desired business outcomes.
Carry forward
Produce the final pilot decision package: metrics, baseline gaps, measurement method, review cadence, decision thresholds, owners, and unresolved approval questions.
Human checkpoint
The accountable sponsor makes the pilot go, revise, or stop decision after reviewing readiness, security, governance, expected value, and residual risk.
Completion criteria
One use case has a documented priority rationale, end-to-end workflow map, readiness assessment, governance and security controls, pilot approval conditions, and measurable success and stop criteria. Any unresolved risks or assumptions are explicitly assigned for human resolution.
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