# POC Template - Microsoft AI Projects **Last updated:** 2026-01 (research via microsoft-learn MCP) --- Dette dokumentet tilbyr en strukturert mal for å planlegge, gjennomføre og evaluere Proof of Concept (POC) prosjekter for Microsoft AI-løsninger. Malen er tilpasset Azure AI Foundry, Copilot Studio, Power Platform AI, og andre Microsoft AI-plattformer. ## Innhold 1. [POC Plan Template](#poc-plan-template) 2. [Success Criteria Framework](#success-criteria-framework) 3. [Evaluation Rubric](#evaluation-rubric) 4. [Platform-Specific Checklists](#platform-specific-checklists) 5. [Risk Assessment Template](#risk-assessment-template) 6. [Timeline Templates](#timeline-templates) 7. [Stakeholder Communication Template](#stakeholder-communication-template) 8. [Go/No-Go Decision Framework](#gono-go-decision-framework) 9. [Example POC Plan](#example-poc-plan) --- ## POC Plan Template Bruk denne malen for å strukturere din POC-plan. Fyll ut hver seksjon basert på ditt spesifikke use case. ### 1. Executive Summary **Hensikt med POC:** _[1-2 setninger: Hva skal POC bevise eller validere?]_ **Forventet varighet:** _[1 uke / 2 uker / 4 uker]_ **Estimert ressursbehov:** _[Antall personer, roller, budsjett]_ **Beslutningspunkt:** _[Dato for go/no-go beslutning]_ --- ### 2. Business Case #### 2.1 Problem Statement _[Beskriv forretningsproblemet eller ineffektiviteten som AI kan løse.]_ **Eksempel:** > Kundestøtte bruker 40% av tiden på repetitive spørsmål om ordrestatus, produktreturer og leveringsinformasjon. Dette binder opp ressurser som kunne brukes på mer komplekse kundehenvendelser. #### 2.2 Target Outcome _[Hva er det ønskede resultatet? Vær konkret og målbar.]_ **Eksempel:** > Automatisere 60% av repetitive kundehenvendelser via chatbot, redusere gjennomsnittlig responstid fra 15 minutter til 2 minutter, og frigjøre 16 timer per uke for støtteteamet. #### 2.3 Strategic Value Ranger strategisk verdi (1-5, hvor 5 er høyest): - **Business Impact:** _[1-5]_ — Hvor stor påvirkning har dette på forretningen? - **User Value:** _[1-5]_ — Hvor mye verdi gir dette til sluttbrukere? - **Innovation Potential:** _[1-5]_ — Hvor innovativt er dette for organisasjonen? - **Strategic Alignment:** _[1-5]_ — Hvor godt aligner dette med organisasjonens AI-strategi? --- ### 3. Technical Scope #### 3.1 AI Maturity Assessment Identifiser din organisasjons AI-modningsnivå (basert på Microsoft CAF): | Level | Skills Required | Data Readiness | Feasible Use Cases | |-------|-----------------|----------------|-------------------| | **Level 1** | Basic AI-forståelse, data-integrasjon | Minimal data, enterprise data tilgjengelig | Azure quickstart, Copilot-løsninger | | **Level 2** | Model selection, deployment, data cleaning | Små strukturerte datasett, domene-spesifikk data | Analytical AI (Foundry Tools), Custom gen AI chat uten RAG, Fine-tuning | | **Level 3** | Prompt engineering, data chunking, preprocessing | Store historiske datasett, domene-spesifikk data | Gen AI med RAG, ML model training, small AI models på VMs | | **Level 4** | Advanced AI/ML, infra management, orchestration | Store treningsdatasett | Large gen AI/ML apps på VMs, AKS, Container Apps | **Din organisasjon er på:** _[Level 1 / 2 / 3 / 4]_ #### 3.2 Chosen AI Solution _[Velg én eller flere:]_ - [ ] **Microsoft 365 Copilot** (extensions/agents) - [ ] **Copilot Studio** (custom agents) - [ ] **Azure AI Foundry** (custom gen AI apps) - [ ] **Power Platform AI** (AI Builder, Power Automate AI) - [ ] **Azure Machine Learning** (custom ML models) - [ ] **Analytical AI** (Content Safety, Document Intelligence, Custom Vision) **Rationale:** _[Hvorfor er denne løsningen valgt? Hva gjør den til det beste valget for dette use case?]_ #### 3.3 Data Requirements **Data Sources:** 1. _[Source 1: Type, format, quality, accessibility]_ 2. _[Source 2: Type, format, quality, accessibility]_ 3. _[Source 3: ...]_ **Data Preparation Needed:** - [ ] Data cleaning/normalization - [ ] Data labeling - [ ] Data chunking (for RAG) - [ ] Privacy/security review (PII removal, anonymization) - [ ] Data governance approvals **Estimated Data Volume:** _[Small (<100 MB) / Medium (100 MB - 10 GB) / Large (>10 GB)]_ #### 3.4 Infrastructure Requirements **Compute:** - [ ] Azure OpenAI capacity (model, region, quota) - [ ] Azure Machine Learning compute (SKU, vCPUs, GPU) - [ ] Power Platform capacity (Copilot Studio, AI Builder credits) **Storage:** - [ ] Azure Storage (type, size) - [ ] Vector database (Azure AI Search, Cosmos DB) **Network:** - [ ] VNet integration - [ ] Private endpoints - [ ] Bandwidth requirements **Security & Compliance:** - [ ] Azure Policy enforcement - [ ] Content Safety filters - [ ] Data residency requirements - [ ] Authentication/authorization (Entra ID, RBAC) --- ### 4. Success Criteria Definer spesifikke, målbare kriterier for POC-suksess. Se [Success Criteria Framework](#success-criteria-framework) for detaljerte KPIer. #### 4.1 Technical Success Criteria 1. **Functional Requirements:** - _[Eksempel: Chatbotten skal kunne svare på 80% av testspørsmålene korrekt.]_ - _[Eksempel: Systemet skal kunne håndtere 100 samtidige forespørsler.]_ 2. **Performance Metrics:** - **Response Time:** _[Target: < 3 sekunder for 95% av forespørslene]_ - **Accuracy:** _[Target: 85% nøyaktighet på validasjonsdatasett]_ - **Availability:** _[Target: 99% uptime under testperioden]_ 3. **Quality Metrics (for Gen AI):** - **Groundedness:** _[Target: 90% av svarene skal være faktabaserte]_ - **Relevance:** _[Target: 85% av svarene skal være relevante for brukerens spørsmål]_ - **Content Safety:** _[Target: 0% harmful content, 100% moderate risk filtered]_ #### 4.2 Business Success Criteria 1. **Efficiency Gains:** - _[Eksempel: Redusere behandlingstid med 50%]_ 2. **Cost Savings:** - _[Eksempel: Redusere driftskostnader med 20%]_ 3. **User Satisfaction:** - _[Eksempel: Oppnå 70% user satisfaction score]_ #### 4.3 Responsible AI Criteria - [ ] **Fairness:** Løsningen skal ikke diskriminere basert på alder, kjønn, etnisitet, etc. - [ ] **Transparency:** Brukere skal forstå når de interagerer med AI - [ ] **Privacy:** Persondata skal beskyttes i henhold til GDPR/compliance-krav - [ ] **Accountability:** Klare roller og ansvar for AI-beslutninger - [ ] **Safety:** Content Safety filters implementert og testet - [ ] **Inclusiveness:** Løsningen skal fungere for alle brukergrupper --- ### 5. Implementation Plan #### 5.1 Phases & Milestones **Phase 1: Prepare (Duration: _[X dager]_)** - [ ] Data collection and preparation - [ ] Environment setup (Azure, Power Platform) - [ ] Team onboarding - [ ] Security/compliance approvals **Deliverable:** _[Data ready for use, infrastructure provisioned]_ --- **Phase 2: Build (Duration: _[X dager]_)** - [ ] Develop initial prototype/POC - [ ] Implement core functionality - [ ] Integrate data sources - [ ] Configure AI model/agent **Deliverable:** _[Working prototype in dev environment]_ --- **Phase 3: Evaluate & Iterate (Duration: _[X dager]_)** - [ ] Functional testing - [ ] Performance testing - [ ] Responsible AI testing (fairness, safety, bias) - [ ] User acceptance testing (UAT) - [ ] Iterate based on feedback **Deliverable:** _[Validated POC with test results]_ --- **Phase 4: Document & Decide (Duration: _[X dager]_)** - [ ] Document lessons learned - [ ] Compile evaluation report - [ ] Prepare go/no-go recommendation - [ ] Present to stakeholders **Deliverable:** _[POC report + go/no-go decision]_ --- #### 5.2 Team Roles & Responsibilities | Role | Responsible For | Time Commitment | |------|-----------------|-----------------| | **Project Lead** | Overall POC coordination, stakeholder communication | _[X hours/week]_ | | **Solution Architect** | Technical design, platform selection | _[X hours/week]_ | | **Data Scientist/Engineer** | Data preparation, model evaluation | _[X hours/week]_ | | **Developer/Maker** | Building prototype (Copilot Studio, Power Platform, code) | _[X hours/week]_ | | **Subject Matter Expert (SME)** | Domain knowledge, validation | _[X hours/week]_ | | **Security/Compliance Officer** | Responsible AI review, compliance validation | _[X hours/week]_ | | **End-User Representative** | User testing, feedback | _[X hours/week]_ | --- ### 6. Testing & Validation Plan #### 6.1 Functional Testing - [ ] Unit tests for individual components - [ ] Integration tests for data pipelines - [ ] End-to-end scenario testing **Test Cases:** _[Liste av testscenarier, f.eks. "User asks about order status"]_ #### 6.2 Performance Testing - [ ] Load testing (concurrent users/requests) - [ ] Latency testing (response times) - [ ] Throughput testing (requests per second) #### 6.3 Responsible AI Testing - [ ] **Fairness Assessment:** Test på diverse brukergrupper - [ ] **Content Safety:** Test adversarial prompts (jailbreak, harmful content) - [ ] **Bias Detection:** Evaluate model outputs for bias - [ ] **Explainability:** Validate that model decisions are understandable **Tools:** - Azure AI Foundry evaluation tools - Azure AI Content Safety - Responsible AI Dashboard (Azure ML) #### 6.4 User Acceptance Testing (UAT) - [ ] Recruit representative users - [ ] Define UAT scenarios - [ ] Collect qualitative feedback (surveys, interviews) - [ ] Measure user satisfaction (NPS, CSAT) --- ### 7. Risk Management Se [Risk Assessment Template](#risk-assessment-template) for detaljert risikovurdering. **High-Priority Risks:** 1. _[Risk 1: Description + Mitigation Plan]_ 2. _[Risk 2: Description + Mitigation Plan]_ 3. _[Risk 3: Description + Mitigation Plan]_ --- ### 8. Budget & Resources **Estimated Costs:** | Category | Estimated Cost | Notes | |----------|---------------|-------| | **Azure Compute** | _[NOK/USD]_ | OpenAI quota, VM SKUs, AML compute | | **Storage** | _[NOK/USD]_ | Blob Storage, AI Search | | **Licensing** | _[NOK/USD]_ | Copilot Studio, Power Platform | | **Personnel** | _[NOK/USD]_ | Team member time (internal/external) | | **Contingency (20%)** | _[NOK/USD]_ | Buffer for unexpected costs | | **TOTAL** | _[NOK/USD]_ | | --- ### 9. Go/No-Go Decision Criteria Ved slutten av POC, evaluer mot disse kriteriene: - [ ] **Technical Feasibility:** Løsningen fungerer som forventet (>80% success criteria oppfylt) - [ ] **Business Value:** ROI er positiv, eller verdi er dokumentert - [ ] **User Acceptance:** Brukere er fornøyde (>70% satisfaction) - [ ] **Responsible AI:** Ingen kritiske fairness/safety issues - [ ] **Risk Acceptable:** Identifiserte risikoer kan håndteres - [ ] **Budget Viable:** Production deployment er innenfor budsjett **Decision:** _[GO / NO-GO / CONDITIONAL GO (specify conditions)]_ --- ## Success Criteria Framework ### Technical KPIs (Generative AI) | Metric | Definition | Target Range | Measurement Method | |--------|------------|--------------|-------------------| | **Groundedness** | % of responses supported by source data | >85% | Azure AI Foundry evaluation | | **Relevance** | % of responses relevant to user query | >80% | Azure AI Foundry evaluation | | **Fluency** | % of responses that are coherent and grammatical | >90% | Azure AI Foundry evaluation | | **Content Safety** | % of harmful content blocked | 100% | Azure AI Content Safety | | **Response Time** | Average latency (seconds) | <3s (p95) | Application Insights | | **Throughput** | Requests per second handled | >100 rps | Load testing | | **Availability** | Uptime during test period | >99% | Azure Monitor | ### Business KPIs | Metric | Definition | Target | Measurement Method | |--------|------------|--------|-------------------| | **Time Saved** | Hours saved per week | _[X hours]_ | Before/after comparison | | **Cost Reduction** | % reduction in operational costs | _[X%]_ | Financial analysis | | **User Satisfaction (CSAT)** | Customer satisfaction score (1-5) | >4.0 | Survey | | **Net Promoter Score (NPS)** | Likelihood to recommend (0-10) | >7.0 | Survey | | **Task Completion Rate** | % of user tasks successfully completed | >80% | Analytics | | **Adoption Rate** | % of target users actively using solution | >60% | Usage analytics | ### Responsible AI KPIs | Metric | Definition | Target | Measurement Method | |--------|------------|--------|-------------------| | **Fairness (Demographic Parity)** | Max difference in positive prediction rates across groups | <10% | Responsible AI Dashboard | | **Bias Detection** | No significant bias detected in outputs | 0 critical issues | Manual review + automated tools | | **Privacy Compliance** | % of PII correctly handled (removed/anonymized) | 100% | Data audit | | **Content Safety Pass Rate** | % of responses passing content safety filters | 100% | Azure AI Content Safety | | **Explainability Score** | % of users who understand AI decisions | >70% | User survey | --- ## Evaluation Rubric Bruk denne matrisen for å score POC-resultater: ### Technical Performance | Criterion | Score 1 (Poor) | Score 3 (Fair) | Score 5 (Good) | Score 7 (Excellent) | Score | |-----------|---------------|----------------|----------------|---------------------|-------| | **Accuracy/Quality** | <60% | 60-74% | 75-89% | ≥90% | _[X]_ | | **Performance** | Frequent failures, >5s latency | Occasional failures, 3-5s latency | Stable, 2-3s latency | Highly stable, <2s latency | _[X]_ | | **Reliability** | <95% uptime | 95-97% uptime | 97-99% uptime | >99% uptime | _[X]_ | | **Scalability** | Cannot scale beyond POC | Limited scalability | Scales to production | Easily scales | _[X]_ | **Technical Score:** _[Sum / 28]_ → _[%]_ --- ### Business Value | Criterion | Score 1 (Poor) | Score 3 (Fair) | Score 5 (Good) | Score 7 (Excellent) | Score | |-----------|---------------|----------------|----------------|---------------------|-------| | **Efficiency Gains** | <20% improvement | 20-40% | 40-60% | >60% | _[X]_ | | **User Satisfaction** | <50% satisfied | 50-65% | 65-80% | >80% | _[X]_ | | **Cost-Effectiveness** | ROI negative | ROI break-even | ROI 1-2x | ROI >2x | _[X]_ | | **Strategic Fit** | Misaligned | Partially aligned | Well aligned | Critical priority | _[X]_ | **Business Score:** _[Sum / 28]_ → _[%]_ --- ### Responsible AI | Criterion | Score 1 (Poor) | Score 3 (Fair) | Score 5 (Good) | Score 7 (Excellent) | Score | |-----------|---------------|----------------|----------------|---------------------|-------| | **Fairness** | Significant bias issues | Minor bias detected | Fair across groups | Highly fair | _[X]_ | | **Safety** | Harmful content generated | Moderate safety issues | Safe with minor exceptions | 100% safe | _[X]_ | | **Privacy** | PII leaks detected | Minor privacy concerns | Privacy compliant | Exceeds compliance | _[X]_ | | **Transparency** | Opaque, users confused | Somewhat transparent | Transparent | Highly transparent | _[X]_ | **Responsible AI Score:** _[Sum / 28]_ → _[%]_ --- ### Overall POC Score | Dimension | Weight | Score (%) | Weighted Score | |-----------|--------|-----------|----------------| | Technical Performance | 40% | _[X%]_ | _[X]_ | | Business Value | 40% | _[X%]_ | _[X]_ | | Responsible AI | 20% | _[X%]_ | _[X]_ | | **TOTAL** | **100%** | | **_[X%]_** | **Recommendation:** - **>80%:** Strong GO — Proceed to production - **60-80%:** Conditional GO — Address gaps before production - **<60%:** NO-GO — Re-evaluate or pivot --- ## Platform-Specific Checklists ### Copilot Studio POC Checklist **Pre-Development:** - [ ] Define agent scope (which topics/intents) - [ ] Identify data sources for grounding (SharePoint, Dataverse, APIs) - [ ] Determine deployment channels (Teams, website, custom) - [ ] Configure Copilot Studio environment (dev, test, prod) - [ ] Set up authentication (if required) **Development:** - [ ] Build initial topics using conversational design best practices - [ ] Configure generative orchestration (if using gen AI) - [ ] Integrate data sources (connections, AI Search) - [ ] Implement content moderation (Azure AI Content Safety) - [ ] Test conversation flows with representative users **Evaluation:** - [ ] Test intent recognition accuracy - [ ] Measure conversation abandonment rate - [ ] Validate grounding accuracy (if using data sources) - [ ] Test escalation paths (handoff to human) - [ ] Collect user feedback via surveys **Governance:** - [ ] Apply content filters (Azure Policy) - [ ] Configure security groups (Entra ID) - [ ] Review compliance (data residency, privacy) - [ ] Document agent behavior and limitations --- ### Azure AI Foundry POC Checklist **Pre-Development:** - [ ] Select foundation model (GPT-4o, GPT-4, custom) - [ ] Provision Azure OpenAI capacity (region, quota) - [ ] Define prompt engineering strategy - [ ] Identify grounding data (if RAG) - [ ] Set up Azure AI Search (if RAG) **Development:** - [ ] Build prompt flow orchestration - [ ] Implement RAG pipeline (chunking, embedding, retrieval) - [ ] Configure content safety filters - [ ] Develop evaluation dataset (test queries + expected outputs) - [ ] Deploy to pre-production endpoint **Evaluation:** - [ ] Run Azure AI Foundry evaluation suite (groundedness, relevance, fluency) - [ ] Test adversarial prompts (jailbreak attempts) - [ ] Measure latency and throughput - [ ] Validate cost per request - [ ] Collect SME feedback on output quality **Governance:** - [ ] Enforce Azure Policy (allowed models, regions) - [ ] Configure RBAC for deployment - [ ] Enable monitoring (Application Insights, Azure Monitor) - [ ] Document model version and configuration --- ### Power Platform AI (AI Builder) POC Checklist **Pre-Development:** - [ ] Identify AI Builder capability (document processing, text classification, object detection) - [ ] Prepare training data (labeled datasets) - [ ] Validate Power Platform capacity (AI Builder credits) - [ ] Define integration points (Power Apps, Power Automate) **Development:** - [ ] Train AI Builder model - [ ] Validate model accuracy on test dataset - [ ] Build Power Automate flow or Power App integration - [ ] Test end-to-end automation **Evaluation:** - [ ] Measure model precision/recall - [ ] Test on real-world data - [ ] Validate processing speed - [ ] Collect user feedback **Governance:** - [ ] Configure DLP policies - [ ] Review data residency - [ ] Document model performance metrics --- ## Risk Assessment Template ### Risk Identification Matrix | Risk Category | Risk Description | Likelihood (1-5) | Impact (1-5) | Risk Score (L×I) | Mitigation Plan | |---------------|------------------|------------------|--------------|------------------|-----------------| | **Technical** | _[Example: Model accuracy below target]_ | _[X]_ | _[X]_ | _[X]_ | _[Retrain with more data, fine-tune prompts]_ | | **Data** | _[Example: Insufficient training data]_ | _[X]_ | _[X]_ | _[X]_ | _[Synthetic data generation, expand data sources]_ | | **Security** | _[Example: PII leakage in outputs]_ | _[X]_ | _[X]_ | _[X]_ | _[Implement PII detection, anonymization]_ | | **Compliance** | _[Example: GDPR violation]_ | _[X]_ | _[X]_ | _[X]_ | _[Legal review, data residency controls]_ | | **Organizational** | _[Example: Lack of user adoption]_ | _[X]_ | _[X]_ | _[X]_ | _[Change management, training, communication]_ | | **Budget** | _[Example: Cost overruns]_ | _[X]_ | _[X]_ | _[X]_ | _[Monitor spending, set cost alerts]_ | | **Responsible AI** | _[Example: Bias in model outputs]_ | _[X]_ | _[X]_ | _[X]_ | _[Fairness testing, diverse training data]_ | **Risk Scoring:** - 1-5: Low risk (monitor) - 6-10: Medium risk (active mitigation required) - 11-25: High risk (escalate, consider showstopper) --- ### Common AI POC Risks & Mitigations #### Technical Risks **Risk:** Model decay over time (accuracy degrades) - **Mitigation:** Implement continuous monitoring, plan for retraining cadence **Risk:** Latency exceeds user expectations - **Mitigation:** Optimize prompt length, use faster models, implement caching **Risk:** Integration failures with existing systems - **Mitigation:** Early integration testing, API contract validation --- #### Data Risks **Risk:** Data quality issues (missing, incomplete, biased data) - **Mitigation:** Data profiling upfront, data cleaning pipelines, diverse data sources **Risk:** Insufficient data volume for training - **Mitigation:** Synthetic data generation, transfer learning, start with simpler models **Risk:** Data access blocked by governance/compliance - **Mitigation:** Early stakeholder engagement, privacy-preserving techniques (anonymization) --- #### Security & Compliance Risks **Risk:** Prompt injection attacks - **Mitigation:** Input validation, content filtering, prompt engineering defenses **Risk:** Data residency violations - **Mitigation:** Use compliant Azure regions, review data flow architecture **Risk:** Unauthorized data access - **Mitigation:** RBAC, private endpoints, encryption at rest/in transit --- #### Organizational Risks **Risk:** User resistance to AI adoption - **Mitigation:** Involve users early, transparent communication about AI capabilities/limitations **Risk:** Insufficient team skills - **Mitigation:** Training programs, external consultants, phased learning approach **Risk:** Unclear ownership and accountability - **Mitigation:** Define RACI matrix, establish AI governance board --- ## Timeline Templates ### 1-Week Sprint POC **Anbefalt for:** Simple use cases (Copilot Studio med pre-built connectors, basic chatbot) | Day | Activities | Deliverables | |-----|------------|--------------| | **Day 1** | Kickoff, scope definition, environment setup | Approved scope, dev environment ready | | **Day 2-3** | Build prototype, integrate data sources | Working prototype | | **Day 4** | Testing (functional, UAT) | Test results, feedback collected | | **Day 5** | Document findings, prepare recommendation | POC report, go/no-go decision | **Total Effort:** ~40 person-hours --- ### 2-Week Standard POC **Anbefalt for:** Moderate complexity (Azure AI Foundry RAG, Copilot Studio med custom topics) | Week | Activities | Deliverables | |------|------------|--------------| | **Week 1** | - Kickoff & planning (Day 1-2)
- Data preparation (Day 2-3)
- Environment setup (Day 3-4)
- Initial prototype build (Day 4-5) | Data ready, dev environment, initial prototype | | **Week 2** | - Iterate on prototype (Day 1-2)
- Testing & validation (Day 3-4)
- Documentation & presentation (Day 5) | Validated POC, test results, final report, go/no-go decision | **Total Effort:** ~80-120 person-hours --- ### 4-Week Extended POC **Anbefalt for:** Complex use cases (Azure ML model training, multi-agent systems, advanced RAG) | Week | Phase | Activities | Deliverables | |------|-------|------------|--------------| | **Week 1** | **Prepare** | - Kickoff, detailed planning
- Data collection & preparation
- Infrastructure setup
- Team onboarding | Data pipeline ready, infra provisioned, team aligned | | **Week 2** | **Build** | - Develop core functionality
- Model training/fine-tuning
- Integration with systems | Working prototype (alpha) | | **Week 3** | **Evaluate** | - Functional testing
- Performance testing
- Responsible AI evaluation
- User acceptance testing
- Iterate based on feedback | Validated prototype (beta), test reports | | **Week 4** | **Decide** | - Final validation
- Documentation (lessons learned, architecture)
- Stakeholder presentation
- Go/no-go decision | POC final report, production roadmap, decision | **Total Effort:** ~200-320 person-hours --- ### Timeline Adjustment Factors Legg til ekstra tid hvis: - [ ] **Data ikke er klar:** +1-2 uker for data cleaning, labeling - [ ] **Komplekse integrasjoner:** +1 uke per critical integration - [ ] **Compliance reviews:** +1-2 uker for legal/security approvals - [ ] **New team to AI:** +1 uke for onboarding/training - [ ] **Custom model training:** +2-4 uker for ML model development **Anbefaling:** Legg til 20-30% buffer for uforutsette utfordringer. --- ## Stakeholder Communication Template ### POC Kickoff Email **Subject:** [POC Name] - Kickoff & Plan **To:** Project team, stakeholders **Body:** > Vi starter POC for [use case name] med mål om å [brief objective]. POC vil løpe fra [start date] til [end date] ([X uker]). > > **Mål:** > - [Goal 1] > - [Goal 2] > > **Team:** > - Project Lead: [Name] > - Solution Architect: [Name] > - Developer: [Name] > > **Neste steg:** > - [Action 1] > - [Action 2] > > **Beslutningspunkt:** [Date for go/no-go decision] > > Spørsmål? Kontakt [Lead]. --- ### Weekly Status Update Template **Subject:** [POC Name] - Week [X] Status **Progress This Week:** - [Completed item 1] - [Completed item 2] **Blockers/Risks:** - [Risk 1 + mitigation plan] **Next Week:** - [Planned item 1] - [Planned item 2] **On Track?** [Yes / No / At Risk] --- ### Final POC Report Template **Executive Summary:** - **Objective:** [What we set out to prove] - **Outcome:** [What we learned/achieved] - **Recommendation:** [GO / NO-GO / CONDITIONAL GO] **Technical Results:** - Accuracy: [X%] (Target: [Y%]) - Performance: [X seconds] (Target: [Y seconds]) - [Other KPIs] **Business Value:** - Efficiency gains: [X hours/week saved] - User satisfaction: [X% CSAT] - Estimated ROI: [X] **Responsible AI:** - Fairness: [Pass/Fail + details] - Safety: [Pass/Fail + details] - Privacy: [Pass/Fail + details] **Risks & Mitigations:** - [Risk 1 + status] - [Risk 2 + status] **Next Steps:** - If GO: [Production roadmap, timeline, budget] - If NO-GO: [Reasons, alternative approaches] **Attachments:** - Test results - User feedback summary - Cost analysis --- ## Go/No-Go Decision Framework ### Decision Criteria Scorecard | Category | Weight | Pass Threshold | Actual Score | Weighted | Pass? | |----------|--------|---------------|--------------|----------|-------| | **Technical Feasibility** | 30% | >75% | _[X%]_ | _[X]_ | _[Y/N]_ | | **Business Value** | 30% | >70% | _[X%]_ | _[X]_ | _[Y/N]_ | | **Responsible AI** | 20% | >80% | _[X%]_ | _[X]_ | _[Y/N]_ | | **User Acceptance** | 10% | >70% | _[X%]_ | _[X]_ | _[Y/N]_ | | **Risk Management** | 10% | No critical risks | _[X/5 risks mitigated]_ | _[X]_ | _[Y/N]_ | | **TOTAL** | **100%** | **>75%** | | **_[X%]_** | **_[Y/N]_** | --- ### Decision Paths #### GO Decision **Criteria:** - Overall score >75% - All critical dimensions pass threshold - No unmitigated high risks (score >15) - Stakeholder approval obtained **Next Steps:** 1. Finalize production architecture 2. Secure production budget 3. Define production roadmap (6-12 months) 4. Establish MLOps/GenAIOps processes 5. Plan change management/training **Timeline to Production:** _[X weeks/months]_ --- #### CONDITIONAL GO Decision **Criteria:** - Overall score 60-75% - Some dimensions below threshold - High risks present but mitigatable **Conditions to Meet:** - _[Condition 1: e.g., Improve accuracy to 85% before production]_ - _[Condition 2: e.g., Complete security audit]_ - _[Condition 3: e.g., Obtain legal approval for data usage]_ **Re-evaluation Date:** _[Date]_ --- #### NO-GO Decision **Criteria:** - Overall score <60% - Critical dimension failures - Unmitigatable high risks - Stakeholder concerns unresolved **Reasons:** - _[Reason 1]_ - _[Reason 2]_ **Alternatives:** 1. **Pivot:** Change approach (different platform, simpler use case) 2. **Delay:** Address blockers, re-run POC in [X months] 3. **Cancel:** Not viable, explore non-AI solutions --- ## Example POC Plan ### POC: Customer Support Chatbot (Copilot Studio) **Executive Summary:** - **Hensikt:** Automatisere repetitive kundehenvendninger (ordrestatus, returer, leveringsspørsmål) via chatbot i Teams og på web. - **Varighet:** 2 uker - **Ressurser:** 3 personer (1 solution architect, 1 developer, 1 SME) - **Beslutningsdato:** 2025-02-14 --- **Business Case:** **Problem:** Kundestøtte bruker 40% av tiden (16 timer/uke) på repetitive spørsmål. Gjennomsnittlig responstid er 15 minutter. **Målsetting:** - Automatisere 60% av repetitive henvendelser - Redusere responstid til <2 minutter - Frigjøre 10 timer/uke for støtteteamet **Strategic Value:** - Business Impact: 4/5 (betydelig effektivisering) - User Value: 5/5 (raskere svar for kunder) - Innovation: 3/5 (standard chatbot-løsning) - Strategic Alignment: 4/5 (aligner med AI-strategi) --- **Technical Scope:** **AI Maturity:** Level 2 (har litt erfaring med Power Platform, basic AI-forståelse) **Chosen Solution:** Copilot Studio - Hvorfor: Low-code, rask utvikling, godt integrert med Teams/Dataverse, møter compliance-krav **Data Sources:** 1. **Dataverse:** Ordredata (Order Status, Tracking Numbers) 2. **SharePoint:** FAQ-dokumenter, return policies 3. **Customer Service API:** Live order lookup **Infrastructure:** - Copilot Studio capacity: 1000 conversations/month - Azure AI Search: For FAQ grounding - Dataverse: For order data - Content Safety: Azure AI Content Safety filters --- **Success Criteria:** **Technical:** - Intent recognition accuracy: >85% - Response time: <3 seconds - Availability: >99% - Content Safety: 100% pass rate **Business:** - Automation rate: >60% of test queries handled without human - User satisfaction: >70% CSAT - Cost per conversation: <5 NOK **Responsible AI:** - No bias in responses across customer demographics - All PII handled securely - Transparent AI disclosure to users --- **Implementation Plan (2 weeks):** **Week 1:** - Day 1-2: Setup Copilot Studio environment, define topics (Order Status, Returns, Shipping) - Day 3-4: Integrate Dataverse + SharePoint, configure gen AI orchestration - Day 5: Build initial conversation flows **Week 2:** - Day 1-2: Test with internal users, iterate on prompts - Day 3-4: User acceptance testing (10 customer service reps), collect feedback - Day 5: Document results, prepare go/no-go recommendation --- **Team:** - **Project Lead:** Kari Nordmann (10 timer/uke) - **Solution Architect:** Ola Hansen (15 timer/uke) - **Developer (Copilot Studio):** Emma Larsen (20 timer/uke) - **SME (Customer Service):** Per Johansen (5 timer/uke) --- **Testing Plan:** **Functional Tests:** - Test all 3 main topics (Order Status, Returns, Shipping) - Test escalation to human agent **Performance:** - Load test: 50 concurrent conversations - Latency: Measure p50, p95, p99 **Responsible AI:** - Test 20 adversarial prompts (jailbreak attempts) - Validate content filters active **UAT:** - 10 customer service reps test for 2 days - Survey: CSAT, ease of use, accuracy --- **Risks:** | Risk | Likelihood | Impact | Score | Mitigation | |------|-----------|--------|-------|------------| | Low intent recognition accuracy | 3 | 4 | 12 | Add more training phrases, use gen AI fallback | | Dataverse integration delays | 2 | 3 | 6 | Start integration early, have mock data ready | | User resistance (prefer human support) | 2 | 2 | 4 | Change management, involve users early | --- **Budget:** | Item | Cost | |------|------| | Copilot Studio license (1 month) | 5,000 NOK | | Azure AI Search (dev tier) | 500 NOK | | Personnel (80 hours × 1000 NOK/hr) | 80,000 NOK | | **TOTAL** | **85,500 NOK** | --- **Go/No-Go Criteria:** - [ ] Intent accuracy >85% - [ ] Response time <3s - [ ] User satisfaction >70% - [ ] No critical safety issues - [ ] Budget for production <50,000 NOK/year **Expected Outcome:** GO (90% confidence based on similar implementations) --- ## Vedlegg: Nyttige Ressurser ### Microsoft Documentation - [AI Adoption Framework (CAF)](https://learn.microsoft.com/azure/cloud-adoption-framework/scenarios/ai/) - [Copilot Studio Implementation Guidance](https://learn.microsoft.com/microsoft-copilot-studio/guidance/overview) - [Azure AI Foundry Evaluation](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/) - [Responsible AI Standard](https://www.microsoft.com/ai/responsible-ai) ### Tools - **Azure AI Foundry:** Model evaluation, deployment - **Copilot Studio:** Agent development, testing - **Azure AI Content Safety:** Content moderation - **Responsible AI Dashboard:** Fairness, bias detection (Azure ML) ### Templates - [AI Impact Assessment Template](https://www.microsoft.com/ai/tools-practices) - [Responsible AI Maturity Model](https://www.microsoft.com/research/publication/responsible-ai-maturity-model/) --- **Sist oppdatert:** 2026-01-XX **Versjon:** 1.0 **Eier:** AI Architect Plugin