I build PMOs, program frameworks, and business operating systems for complex work: portfolio governance, executive cadence, delivery readiness, and practical AI-enabled workflows.

My background is portfolio, program, project, and operations leadership across technology, revenue systems, partner ecosystems, launch readiness, compliance, and infrastructure delivery. I am usually brought in to help fix environments where the work is visible but not yet governable: too many initiatives, unclear ownership, inconsistent intake, competing executive priorities, or delivery risk that only shows up after commitments have already been made.

This GitHub profile is a public portfolio space for examples of how I structure work, not a software engineering portfolio. The artifacts here focus on project-management systems, PMO tools, operating models, templates, and sanitized workflow examples.

• Portfolio landing page: policani.github.io
• Public operating-pattern library: Operating Patterns source pages
• GitHub source bundle for the operating-pattern library: operating-patterns

This portfolio should be evaluated as operating-model and workflow design evidence, not as a traditional software engineering portfolio.

Look for:

• How rough demand becomes structured intake, ownership, risks, dependencies, decisions, and follow-through.
• How AI is used for synthesis, review, drafting, and repeatability without taking over approvals or accountability.
• How each system separates examples, templates, generated outputs, local tooling, and ChatGPT Project runtime files.
• How public examples stay synthetic or generalized so they demonstrate judgment without exposing employer or client details.

• Enterprise PMO, EPMO, portfolio governance, and AI value realization
• Executive operating cadence, tradeoff review, and decision support
• Intake, prioritization, scoring, sequencing, and capacity visibility
• AI resource allocation, workflow economics, and measurable work reduction
• Revenue technology, finance systems, and release-readiness governance
• Partner ecosystem, GTM, launch, and field-readiness operations
• AI-assisted project and portfolio workflows with human review and evidence controls

• Rebuilt portfolio visibility across large initiative sets so leaders could separate active work, stalled demand, readiness gaps, and real capacity constraints.
• Designed governance rhythms for executive sponsors across CTO, CIO, CFO, COO, CMO, and Senior Director environments.
• Built portfolio decision-support models that make prioritization criteria, ownership, risks, dependencies, tradeoffs, and decision rights easier to inspect.
• Built operating models for partner programs, provider networks, launch evidence, pre-release pilots, and customer-facing readiness.
• Used practical AI to improve portfolio hygiene, documentation quality, dependency review, meeting intelligence, content sourcing, scoring consistency, and workflow discipline without handing accountability to the tool.
• Turned messy delivery environments into clearer systems of record, ownership, decision cadence, and follow-through.

I am building this space around public-safe examples such as:

• PMO intake, scoring, prioritization, and sequencing models
• Portfolio scoring matrices and executive decision-support briefs
• Launch readiness and executive status frameworks
• RAID, decision-log, action-register, escalation, meeting-prep, and governance closeout templates
• Portfolio signal-quality checklists
• AI-assisted project and portfolio documentation workflows
• Job-search and career-operations workflow architecture

These repositories are public-safe examples of AI-assisted operating systems, not general software demos. They are organized by the type of decision or workflow they support.

AI Opportunity Intelligence Review System

• An operating system for decomposing rough AI ideas into intelligence architecture, proof requirements, governance controls, and build/buy/wait routes before teams spend time on demos, vendors, proofs of concept, or delivery planning.
• It demonstrates an intelligence review layer for AI portfolio management with a flat ChatGPT Project runtime, synthetic sample data, Mermaid workflows, local tooling, and human accountability guardrails.

Portfolio Prioritization Scoring Agent

• A human-governed, AI-assisted portfolio decision-support system for evaluating approved projects and programs through transparent weighted scoring, portfolio metadata, strategic themes, constraints, risks, dependencies, and ownership.
• The workflow helps stakeholders define scoring criteria, weights, governance cadence, budget and capacity constraints, mandatory-versus-discretionary work, portfolio KPIs, and executive decision views.
• It demonstrates how AI can improve portfolio prioritization, scenario review, and decision readiness without turning funding, sequencing, or tradeoff decisions over to an autonomous tool.

Operating Patterns

• A public-safe library of generalized AI, PMO, and portfolio operating patterns for governance, decision support, value realization, and resource allocation.
• It demonstrates reusable management patterns that can support AI portfolio review, executive cadence design, and practical operating model development.
• Start with the source pages: Operating Patterns

Business Case System

• An agent-assisted business case development system for turning early ideas, notes, spreadsheets, project plans, and source documents into decision-ready business cases.
• It interviews the user, ingests supporting artifacts, challenges weak problem framing, strengthens incomplete ideas, tests assumptions through a critical review council, and generates structured Markdown, richly formatted DOCX, and HTML outputs.
• It demonstrates how practical AI can improve business decision quality without replacing human judgment, sponsor accountability, financial scrutiny, or governance discipline.

Project Charter Initiation Agent

• A PMBOK Guide-aligned, agent-assisted project initiation system for developing sponsor-ready project charters from business cases, notes, spreadsheets, project plans, stakeholder inputs, and source artifacts.
• The workflow acts like a senior initiation and governance advisor: it challenges vague scope, clarifies ownership and decision rights, separates objectives from deliverables, surfaces assumptions, risks, dependencies, and open decisions, and produces polished Markdown, HTML, and DOCX outputs ready for review.

PMO Governance Operations Log

• A human-governed, AI-assisted PMO worklog system for turning rough notes, stakeholder updates, blockers, decisions, risks, scheduling issues, and follow-up items into usable governance artifacts.
• The workflow helps a PMO or portfolio lead classify weekly operating signals, prepare governance meetings, draft stakeholder follow-ups, build executive air-support briefs, recommend project-plan updates, and close out meetings with clear decisions, actions, owners, due dates, unresolved items, and carry-forward topics.
• It demonstrates how practical AI can improve governance hygiene, meeting preparation, escalation framing, and follow-through without becoming a generic meeting-notes template, autonomous project manager, or full PPM platform.

AI Prose-Pattern Detector

• A prose QA toolkit for finding AI-shaped writing patterns, condensed expert language, formulaic contrast, and generic business prose.
• It demonstrates agent-readable quality gates, centralized settings, reusable rubrics, and human-in-the-loop editing.

Job Search Workbench

• An AI-ready local workflow project for job seekers who want to scan public employer job boards, filter roles by title, location, salary, posting age, and job-description evidence, then export a review list.
• It demonstrates workflow design for messy external data, source-health diagnostics, and human-in-the-loop career operations.

Resume Evidence Engine

• An evidence-bound workflow project for job seekers who want AI-assisted help with role scoring, resume drafting, cover letters, proof narratives, local browser builders, and Markdown-to-DOCX handoff.
• It demonstrates practical AI workflow design with source-of-truth files, guardrails, reusable templates, fictional sample outputs, and human review.

• Start with the operating problem, not the artifact.
• Make ownership, constraints, decisions, risks, and tradeoffs visible.
• Separate demand, priority, readiness, and execution so leaders can see what is real.
• Build only enough process to create trust, clarity, and follow-through.
• Favor useful signal over status theater, red tape, or process for its own sake.
• Use AI for structure, synthesis, signal review, follow-through, and repeatability; keep judgment, commitments, approvals, and accountability with people.
• Produce outputs that executives can act on, practitioners can use, and sponsors can defend.

• LinkedIn: https://www.linkedin.com/in/marcpolicani
• GitHub: https://www.github.com/policani