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AI Delivery Head Cadence examples
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| Your Core Role (Big Picture) | |
| As AI Practice Head, you are NOT the delivery manager and NOT the architect. | |
| 👉 Your real role is to act as the bridge between: | |
| Business outcomes | |
| Technical execution | |
| Delivery governance | |
| Think of yourself as the “Outcome Owner”. | |
| If the project fails, it won’t be because the model didn’t work — | |
| it will be because expectations, priorities, or communication broke. | |
| 2️⃣ Where You Should Concentrate Most (80/20 Rule) | |
| 🔹 A. Business Alignment & Scope Control (VERY IMPORTANT) | |
| This project has high AI risk if scope is not tightly controlled. | |
| You must ensure absolute clarity on: | |
| For UC1 (Safety / PPE): | |
| What counts as helmet compliance? | |
| Partial face visible → violation or not? | |
| False positives tolerance? (e.g., 90% accuracy acceptable?) | |
| Alert fatigue rules (how many alerts per hour?) | |
| For UC2 (Email Automation): | |
| Which email intents are IN scope? | |
| What happens if extraction confidence < threshold? | |
| SLA ownership — AI or humans? | |
| CRM API limitations (create vs update only?) | |
| 📢 What YOU communicate: | |
| “This is a pilot, not an enterprise-wide rollout. Success = defined accuracy + workflow adoption.” | |
| 🔹 B. Outcome-Driven Delivery (Not Task-Driven) | |
| Your PM will track: | |
| Timelines | |
| Tasks | |
| Milestones | |
| 👉 You track outcomes. | |
| Create success metrics like: | |
| UC1: | |
| PPE detection accuracy ≥ X% | |
| Reduction in manual CCTV review | |
| Average alert response time | |
| UC2: | |
| % emails auto-ticketed | |
| Reduction in manual effort | |
| First response time improvement | |
| 📢 What YOU communicate: | |
| “We are not delivering models, we are delivering measurable operational impact.” | |
| 🔹 C. Risk & Dependency Management (Silent Killer Area) | |
| This project depends heavily on customer-side readiness. | |
| Your focus: | |
| Camera angles & lighting (UC1) | |
| SOP clarity & SME availability (UC1 + UC2) | |
| CRM API stability (UC2) | |
| Sample data quality (emails, videos) | |
| 🚩 Typical risks you must surface early: | |
| “Cameras exist” ≠ “Cameras usable for AI” | |
| SOPs are tribal knowledge, not documented | |
| CRM APIs undocumented or unstable | |
| 📢 What YOU communicate: | |
| “AI accuracy depends on real-world data quality — delays here directly delay outcomes.” | |
| 3️⃣ Your Role vs Technical Team vs PM (Clear Separation) | |
| 🧠 You (Practice Head – AI) | |
| Own vision, scope, success definition | |
| Validate architecture direction, not code | |
| Decide trade-offs (accuracy vs speed vs cost) | |
| Escalate business risks | |
| Handle executive communication | |
| 🛠️ Technical Team | |
| Model training & fine-tuning | |
| Integration (RTSP, CRM APIs) | |
| Dashboards & alerts | |
| Performance optimization | |
| 📅 Project Manager | |
| Sprint planning | |
| Tracking milestones | |
| Dependency follow-ups | |
| Status reporting | |
| 👉 If you start doing PM work → you’re underutilized | |
| 👉 If you start coding → you’re misused | |
| 4️⃣ What You Should Communicate at Each Phase | |
| 🔹 Phase 1: Kickoff | |
| Your message: | |
| Clear scope boundaries | |
| Pilot success criteria | |
| Roles & escalation paths | |
| “No scope creep without business approval” | |
| 🔹 Phase 2: Development | |
| Your message: | |
| “Accuracy will improve iteratively” | |
| “Early demos > perfect final demo” | |
| “Feedback loop is mandatory” | |
| You should attend demos, not standups. | |
| 🔹 Phase 3: UAT & Go-Live | |
| Your message: | |
| “This is a human + AI system” | |
| Define fallback rules | |
| Confirm ownership post go-live | |
| Align on hypercare expectations | |
| 5️⃣ One Line That Defines Your Job | |
| “My job is to ensure AI delivers business value predictably, safely, and at scale — not just technically, but operationally.” | |
| If you want, next I can: | |
| Create a 1-page AI Practice Head responsibility checklist | |
| Draft a steering committee update template | |
| Help you define AI success KPIs for CXOs | |
| Or map this into an OKR structure for your AI practice | |
| Just tell me where you want to go next 🚀 | |
| 6️⃣ Your Weekly Operating Rhythm (What YOU should actually do) | |
| Instead of ad-hoc involvement, run the project on a fixed cadence. | |
| 🗓️ Weekly – AI Practice Head Cadence | |
| 1. 30-min Outcome Review (with PM + Tech Lead) | |
| Focus ONLY on: | |
| Are we still aligned to business success metrics? | |
| What assumption broke this week? | |
| Any accuracy / data / dependency risks? | |
| ❌ Not: | |
| Task-level updates | |
| Jira stories | |
| 2. Demo Review (once every 2 weeks) | |
| Insist on: | |
| Live video feed demos (UC1) | |
| Real emails → tickets (UC2) | |
| 📢 Your standard line: | |
| “Show me real data, not test data.” | |
| 3. Stakeholder Touchpoint (Fortnightly) | |
| You speak to: | |
| Ops Head (Safety) | |
| Customer Support Head | |
| IT / Security | |
| Purpose: | |
| Validate adoption | |
| Remove friction | |
| Reset expectations | |
| 7️⃣ What You Must STOP Doing (Very Important) | |
| Many Practice Heads fail because they do this 👇 | |
| ❌ Micromanaging Accuracy | |
| 92% vs 94% doesn’t matter early | |
| Consistency & explainability matter more | |
| ❌ Letting Scope Expand “Casually” | |
| “Can you also detect gloves?” | |
| “Can it auto-close tickets?” | |
| 🚨 This is how AI pilots die. | |
| Your response: | |
| “Noted for Phase-2. Let’s stabilize Phase-1 first.” | |
| ❌ Overpromising AI Intelligence | |
| Never say: | |
| “AI will prevent all incidents” | |
| “Emails will be fully automated” | |
| Say instead: | |
| “AI will significantly reduce manual effort and improve response speed.” | |
| 8️⃣ Steering Committee: What YOU Should Present | |
| You should present only 5 slides, not 50. | |
| 📊 Slide 1: Business Objective | |
| Why this pilot exists | |
| What problem it solves | |
| 📊 Slide 2: Current Status (RAG) | |
| Green / Amber / Red | |
| One-line reason | |
| 📊 Slide 3: Measured Outcomes | |
| Accuracy | |
| Automation % | |
| Time saved | |
| 📊 Slide 4: Risks & Decisions Needed | |
| Data gaps | |
| SOP clarity | |
| Go/No-Go inputs | |
| 📊 Slide 5: Scale Recommendation | |
| Rollout? | |
| Pause & refine? | |
| Extend scope? | |
| 📢 Your closing line: | |
| “Based on pilot evidence, my recommendation is…” | |
| 9️⃣ How You Evaluate Success (Real AI Leadership Test) | |
| The project is successful IF: | |
| ✅ Business users trust alerts | |
| ✅ People actually use dashboards | |
| ✅ Manual effort reduces measurably | |
| ✅ CXO understands value without technical explanation | |
| The project is NOT successful just because: | |
| Model accuracy is high | |
| Project finished on time | |
| 🔟 Your AI Practice Head “North Star” | |
| Always ask yourself: | |
| “If this scales to 10 plants and 100x volume, will it still work operationally?” | |
| If yes → green light | |
| If no → pause & redesign | |
| 🔥 Final Takeaway | |
| Technical teams build AI. | |
| Project managers deliver AI. | |
| You make AI usable, scalable, and defensible. | |
| That’s leadership. | |
| If you want next, I can: | |
| Draft a Practice Head playbook (PDF-style) | |
| Create a risk register specific to AI pilots | |
| Help you define Phase-2 roadmap | |
| Prepare CXO-level talking points | |
| Tell me which one you want next 👌 |
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