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

My background is portfolio, program, project, and operations leadership across technology, revenue systems, finance workflows, partner ecosystems, launch readiness, compliance, and infrastructure delivery. I am usually brought in when work is visible but not yet governable: too many initiatives, unclear ownership, inconsistent intake, competing executive priorities, weak readiness evidence, 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. It is not a software engineering portfolio and not a product-management portfolio. The artifacts here focus on PMO systems, portfolio governance, operating models, decision-support workflows, templates, and sanitized examples of AI-assisted leadership work.

• Portfolio landing page: policani.github.io
• GitHub profile: github.com/policani
• Public operating-pattern library: Operating Patterns source pages
• Operating-pattern repository: operating-patterns

Evaluate this portfolio as operating-model and workflow design evidence.

Look for:

• How rough demand becomes structured intake, ownership, risks, dependencies, decisions, and follow-through.
• How portfolio noise becomes clearer signal for executive review.
• How readiness, sequencing, capacity, controls, and value realization are made visible before commitments drift.
• How AI is used for synthesis, review, drafting, classification, and repeatability without taking over approvals or accountability.
• How each system separates public examples, runtime files, templates, workflows, configuration, and generated outputs.
• How examples stay synthetic or generalized so they demonstrate judgment without exposing employer or client details.

• Enterprise PMO, EPMO, portfolio governance, and program governance
• Executive operating cadence, tradeoff review, and decision support
• Intake, prioritization, scoring, sequencing, and capacity visibility
• Portfolio signal quality, ownership clarity, and readiness discipline
• Release readiness, UAT governance, launch gates, and signoff controls
• Value realization, benefits tracking, and evidence-confidence review
• Revenue technology, finance systems, controls, and exposure governance
• Partner ecosystem, provider-network, 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 formation, intake, scoring, prioritization, and sequencing models
• Portfolio signal-quality audits and executive decision-support briefs
• Capacity planning, milestone sequencing, and dependency review workflows
• Release readiness, UAT governance, and launch decision packs
• Controls, exposure, remediation, exception, and evidence registers
• Partner ecosystem and provider-network governance models
• Innovation portfolio and AI opportunity governance workflows
• Value realization and benefits governance ledgers
• RAID, decision-log, action-register, escalation, meeting-prep, and closeout templates
• 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. They are organized by the leadership workflow or decision point they support.

Portfolio Signal Quality Auditor

• A signal-quality audit system for portfolio inventories, spreadsheets, Jira exports, Smartsheet tables, governance decks, and project lists.
• It helps identify inconsistent status fields, missing owners or sponsors, stale dates, stalled work labeled active, duplicate demand, unclear readiness, and KPI drift.
• It demonstrates how portfolio leaders can clean the signal before executives make prioritization, sequencing, or capacity decisions.

Portfolio Intake and Readiness Triage System

• A structured intake triage system for new requests, mandates, initiatives, compliance needs, AI ideas, and executive asks.
• It classifies demand before it becomes portfolio noise, premature business cases, unready charters, or false work-in-flight.
• It routes work to clarify, hold, reject, business case, charter, or portfolio scoring paths while keeping approval decisions human-owned.

Executive Portfolio Review Pack Builder

• A decision-support system for turning portfolio data into sponsor-ready executive review packs.
• It produces executive summaries, decision requests, portfolio health views, risk and dependency views, capacity pressure summaries, and follow-up registers.
• It demonstrates how governance reviews can move beyond status reporting into decisions, tradeoffs, constraints, and accountable follow-through.

Portfolio Capacity and Sequencing Planner

• A sequencing workflow for portfolios with constrained capacity, shared resources, fixed dates, dependency chains, or milestone exposure.
• It helps leaders compare sequencing scenarios, surface capacity pressure, identify dependency conflicts, and frame deferral or acceleration options.
• It does not approve priorities or commit resources; it makes tradeoffs visible for human decision-makers.

Portfolio Prioritization Scoring Agent

• A human-governed, AI-assisted portfolio decision-support system for evaluating approved projects and programs through transparent weighted scoring, strategic themes, constraints, risks, dependencies, and ownership.
• It 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.

Release Readiness and UAT Governance Pack

• A readiness-gate system for projects, releases, finance system changes, platform updates, and business process changes approaching validation or launch.
• It creates entry and exit criteria, readiness scores, evidence gaps, environment and data readiness checks, signoff views, blocker escalation, and launch decision briefs.
• It demonstrates how readiness discipline can reduce late-stage ambiguity without replacing QA, business owners, or release approvers.

Value Realization Governance Ledger

• A benefits governance system for tracking whether approved work is producing the expected value.
• It connects benefit hypotheses, baselines, targets, actuals, measure owners, confidence ratings, evidence gaps, realization risks, and continue, hold, scale, or retire recommendations.
• It does not certify savings or replace finance ownership; it creates a reviewable ledger for human decision-making.

Controls and Exposure Governance Toolkit

• A public-safe toolkit for making risk, financial exposure, compliance obligations, supplier remediation, reimbursement controls, exceptions, and audit evidence visible.
• It produces exposure registers, control-owner maps, remediation trackers, exception logs, evidence registers, and escalation paths.
• It supports governance review without making legal, finance, privacy, security, compliance, or audit determinations.

PPMO Formation Kit

• A practical formation package for establishing, resetting, or formalizing a PMO, EPMO, PPMO, transformation office, or portfolio governance function.
• It helps define decision rights, intake, portfolio taxonomy, sponsor councils, reporting, decision logs, and a 30/60/90 operating plan.
• It demonstrates how to create enough structure to govern work without turning PMO formation into bureaucracy.

Partner Ecosystem Governance Pack

• A governance system for partner programs, provider networks, SI channels, certification paths, launch-readiness programs, and external delivery ecosystems.
• It structures partner tiers, qualification rules, evidence workflows, readiness gates, operating cadence, controls, and escalation paths.
• It demonstrates how external delivery networks can be governed without confusing program governance with sales quota ownership, legal authority, or partner contract control.

Innovation Portfolio Governance Model

• A lightweight governance model for R&D, AI exploration, emerging technology, and ambiguous strategic work.
• It helps compare innovation demand through value, feasibility, risk, learning milestones, funding gates, and capacity fit.
• It keeps experimentation alive while preventing novelty, urgency, or executive preference from replacing structured portfolio judgment.

AI Opportunity Intelligence Review System

• An operating system for decomposing rough AI ideas into intelligence architecture, proof requirements, governance controls, and build, buy, or 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.

AI Artifact Lifecycle Governance System

• A lightweight AI artifact governance system for classifying, governing, promoting, demoting, or retiring AI tools, scripts, dashboards, agents, automations, and workflow artifacts that already exist before the business has formally approved them.
• It helps leaders build a prototype commons registry, classify artifacts by production readiness, screen reliance risk, review ownership, data, systems, permissions, and value signal, then route each artifact to the right next step.
• It demonstrates a practical governance layer between informal experimentation and formal business reliance.

Operating Patterns

• A public-safe library of generalized AI, PMO, and portfolio operating patterns for governance, decision support, value realization, resource allocation, executive cadence, and 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 layer, and generates structured 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 challenges vague scope, clarifies ownership and decision rights, separates objectives from deliverables, surfaces assumptions, risks, dependencies, and open decisions, and produces 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.
• It 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 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.

Jobs Scanner

• 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, dependencies, and tradeoffs visible.
• Separate demand, priority, readiness, and execution so leaders can see what is real.
• Clean portfolio signal before asking leaders to make portfolio decisions.
• Build only enough process to create trust, clarity, decision quality, 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, funding, risk acceptance, 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
• Portfolio: https://policani.github.io