2019 Advocate Honoree – VP Education and Programs, Frontiers of Flight Museum (45K+ students); STEM Education roots powering these agents.
From frameworks to working agents: Operationalizing cross-industry PM insights through agentic workflows.
Anthropic 2026 State of AI Agents Report: 57% enterprises deploy multi-step agents; 80% ROI today.
This repository demonstrates Agentic Literacy Cognitive/Operational/Ethical Fluency for directing autonomous digital workers across augment, automate, agent phases using 4D Framework (Discover, Design, Deploy, Detect).
Identifies governance and execution risks before teams scale analytics or AI systems.
Before the case studies, this framework outlines how I lead transformation work inside existing enterprise and education ecosystems.
📘 Cross-Ecosystem Operating Model
Read the framework →
PMs and leaders know AI governance risks exist—but lack tools to assess/operationalize before scaling.
I solve this with working agents and prompts grounded in NIST RMF + cross-industry PM: identify gaps, automate compliance, prove risk reduction.
Fork, deploy, or adapt to de-risk your AI pilots today.
Use the Prompts → | Agent Overview → | Case Study | See Example
Status: Prompt Library Available (5 prompts + sample assessment) | Full Agent Q1 2026
git clone https://github.com/AliciaMMorgan/Innovation-In-Action.git
cd Innovation-In-Action/agents/pm-risk-assessor
python risk_assessor.py --project "AI Pilot X"/agents/pm-risk-assessor/– Core prompts + case studies/artifacts/– JIRA, Confluence, Power BI examples/notebooks/– Benchmarking data/cyber-ai-profile/– NIST RMF mappings
"9 Steps: Traditional PM → AI-Fluent Leader" LinkedIn carousel (Dec 2025).
🔗 View Carousel
Traditional PM: document → meeting → decision. Agentic: prompt → validate → deploy → monitor.
81% plan complex agents 2026 (multi-step/cross-functional).
Workflow → Intelligence → NIST → Scale
| Pillar | What it Means | Workflow Impact |
|---|---|---|
| Cognitive | How AI "thinks"/where it fails | Reduces AI technical debt |
| Operational | Agent "swarms"/chaining | 10% → 10x gains |
| Ethical | Bias/privacy/dark patterns | EU AI Act compliance |
| Use Agent-First When | Use API-First When |
|---|---|
| Dynamic judgment needed | Static data processing |
| Cross-system orchestration | Single-tool optimization |
| Rapid iteration required | Production stability |
| PM risk assessment | Transactional volume |
47% enterprises use hybrid.
Live Now: 5 production prompts + STEM case study (prompts seed all agents).
Jan 10, 2026: Completed Foundry Fast Track (4 days). Azure Foundry → PM Fluency Agent Challenge live (credits → Feb ship).
- Use the Prompts → | Quarterly agents extension
- Governance: Responsible AI Usage
- JIRA: Import prompts as Kanban issues
- Confluence: Embed outputs + NIST mappings
- Power BI: Risk heatmap from CSV exports
- AI WINS Dashboard: Excel template
- GitHub Issues: Log gaps/customizations
Research → Production Agents:
- Anthropic AI Fluency + 2026 Agents Report: Prompt engineering, 57% deployment/80% ROI
- Microsoft Learn + AWS ML Essentials: Operational chaining, token economics, capacity forecasting
- NIST AI RMF (LinkedIn Learning) + ISO 42001: Govern/Measure functions, continuous monitoring
- "The Coming Wave" + Carousels: Deployment risk containment, 9-Steps (2x engagement)
Solves enterprise blockers: Integration(46%)/Data(42%) + cost/capacity controls.
Report Quote: "In 2026, you aren't paid for what you do; you are paid for the quality of intelligence you direct."
Quarterly rollout (1/quarter post-Foundry for refinement/training):
- Q1 2026 — Cross-Industry AI Agent Prototype((9-steps + prompts) I built and validated a Microsoft Foundry agent that encodes my Cross-Industry PM AI Fluency framework into stage-aware guidance for project and program managers navigating AI adoption. Version 1 proved that cross-industry PM pattern recognition can be translated into consistent, context-sensitive agent behavior that helps teams clarify the adoption stage, translate AI activity into business value, and introduce governance at the right time.
→ View full Q1 Agent Breakdown
ai-agent-q1-2026/README.md
- Q2: Risk Guardrails Agent
/agents/risk-guardrails(NIST extension) - Q3: Stakeholder Alignment
/agents/stakeholder-align(cross-energy) - Q4: Change Readiness
/agents/change-readiness(swarm)
Jan 10, 2026 (post-Foundry): Quarterly vs original Q1 batch.
Original Plan: Q1 deploy 4 agents.
Updated: 1/quarter → deeper prompts/training (Dallas AI Agent classes). Microsoft Azure Credits force Feb ship.
Cloners: Prompts unchanged. Commits | Issues
**Govern → Map → Measure →
Manage**
Cyber AI Profile (IR 8596) – CSF mappings for Secure/Defend/Thwart.
Fortune 500/100 → Nonprofit → Independent AI-PM Consultant.
Master's Industrial Engineering + cross-industry execution = agentic workflows that ship.
MIT
"Based on work by Alicia M. Morgan – github.com/AliciaMMorgan"