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Business Analyst · Project Manager · Program Manager · Portfolio Manager

20 years mastering how the work gets done. Now rebuilding it with AI.

AI multiplies the work; the judgment stays mine.

I have operated across every altitude of technology delivery, from understanding the problem to allocating enterprise investment, and I stay current by applying that full range in real product builds.

See How I Deliver See the Proving Ground

1Understand

2Deliver

3Coordinate

4Allocate

The accountability climb

The same rigor, applied at increasing levels of consequence.

The artifact changed shape at every altitude — BRD, user story, backlog, roadmap, portfolio — but the job never did: deciding what gets built, for whom, and in what order.

Business Analyst

Understanding the problem

Closest to the work. Translating organizational ambiguity into precise specification. If the analyst gets this wrong, everything built downstream is wrong.

AI-leverage signature

Synthesis

Turning scattered, contradictory human input into a coherent, structured picture.

Credibility anchors

Requirements across five business lines
75% reduction in documentation cycle time
15% improvement in claims processing efficiency

IN PRODUCT TERMS

Product equivalent: Discovery and requirements ownership — the “what and why” before anything is built.

Artifacts owned: BRDs and functional specs; user stories with acceptance criteria as programs moved to Agile; requirements traceability; the BA standards that cut documentation cycle time 75%.

What stays human

Deciding which requirements actually matter to the business, and reading the room when two executives want incompatible things. AI organizes the inputs. The analyst owns the judgment call about what's real.

The Approach

Human inputs

Stakeholder interviews
Existing documentation
Conflicting priorities

AI-assisted synthesis

Cluster
Compare
Clarify

Structured outputs

Structured requirements
Traceability matrix
Process and options analysis

Before

Weeks of manual reconciliation

Multiplied

Days with structured AI support

Still human

The call about what actually matters

Activity 1

Requirements Elicitation & Synthesis

Pre-AI

Interview stakeholders across five business lines. Hand-capture notes, manually cluster themes, reconcile where executives contradict each other, draft the BRD, circulate it, absorb feedback, and manage versions. Weeks of cycle time before a single requirement is validated.

AI-multiplied

Auto-transcribe every session. AI clusters themes across all interviews, surfaces where stakeholders want incompatible things, drafts the structured BRD into a standard template, and flags ambiguities and gaps before anything circulates. Days, not weeks; same rigor, compressed.

Activity 2

Documentation & Traceability

Pre-AI

Build the requirements traceability matrix by hand. Write functional specifications, map every requirement to acceptance criteria and test cases manually, then keep it all synchronized whenever scope changes.

AI-multiplied

Generate the traceability matrix, first-pass acceptance criteria, and test cases directly from the requirements. When a requirement changes, AI propagates the downstream impact and flags what is now out of sync, leaving no silent gaps.

Activity 3

Process Analysis & Solution Definition

Pre-AI

Map the current-state process through workshops and diagrams, identify inefficiencies by inspection, hand-model the future state, and build the case for the solution.

AI-multiplied

AI drafts current- and future-state process maps from interview transcripts and existing documentation, quantifies the improvement opportunity, and generates the options analysis for stakeholder review.

Activity 4

User Stories & Backlog Readiness

Pre-AI

Decompose each approved requirement into user stories by hand, write acceptance criteria one story at a time, size them with the team, and keep the story map and the BRD in sync manually as scope moved.

AI-multiplied

AI drafts the story set and first-pass acceptance criteria straight from the approved requirements, flags stories that are too large or untestable, and regenerates the story map when a requirement changes. I decide which stories are real and which are noise.

Project Manager

Delivering the work

Owns a single delivery end to end: scope, schedule, risk, and commitment.

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Foresight

Credibility anchors

Cross-functional teams of 8+
Delivery inside complex, multi-project program environments
Agile and hybrid sprint execution
Led onshore and offshore test teams and leads through a major platform migration
99.5% accuracy
40% testing-efficiency gains

IN PRODUCT TERMS

Product equivalent: Sprint and release ownership for a single product line.

Artifacts owned: prioritized project backlog in Jira; user stories groomed with the BA and dev lead; sprint plans; release notes; RAID log.

What stays human

Owning the commitment. When to escalate, when to absorb, when to hold the line on scope, and when to have the hard conversation. That accountability does not delegate to a model.

The Approach

Signals

Task slippage
Velocity decline
Open issue patterns

AI-supported foresight

Detect
Forecast
Recommend

PM decisions

Re-sequence work
Escalate risk
Protect scope

Reactive

Risk discovered in status meetings

Multiplied

Risk surfaced before impact

Still human

The commitment and escalation call

Activity 1

Planning & Scheduling

Pre-AI

Build the WBS, sequence tasks, estimate durations, construct and level the schedule by hand. Re-baseline manually every time reality moves.

AI-multiplied

AI drafts the WBS and first-pass schedule from the scope, proposes dependencies and estimates from comparable past projects, and re-forecasts the timeline automatically the moment a task slips.

Activity 2

Status Reporting & Stakeholder Communication

Pre-AI

Chase the team for updates. Hand-assemble the weekly status deck. Re-translate the same update into different formats for executives versus the working team.

AI-multiplied

AI compiles status directly from the task tracker, drafts both the executive summary and the detailed team version from one source of truth, and tunes tone per audience. The PM reviews and sends in minutes, not hours.

Activity 3

Risk & Issue Management

Pre-AI

Maintain the RAID log by hand. Rely on experience to sense emerging risk. Escalate reactively, usually after it has already surfaced in a meeting.

AI-multiplied

AI scans status trends, velocity, and open-issue patterns to flag risks before they escalate, drafts mitigation options, and keeps the RAID log current directly from project activity.

Activity 4

Distributed Team & Quality Leadership

Pre-AI

Coordinate onshore and offshore testers and test leads by hand across time zones: allocate work, chase test coverage, reconcile defect reports, and hold the quality bar through a high-stakes migration by sheer vigilance.

AI-multiplied

AI drafts test coverage from requirements, clusters and de-duplicates defect reports across distributed teams, summarizes overnight offshore progress for the morning handoff, and flags coverage gaps before a release, so the lead manages exceptions, not spreadsheets.

Activity 5

Backlog Grooming & Sprint Planning

Pre-AI

Weekly grooming sessions, manual prioritization against a spreadsheet of stakeholder asks, velocity tracked by hand, sprint scope negotiated line by line.

AI-multiplied

AI pre-ranks the backlog against stated priorities and dependencies, drafts the sprint proposal, and surfaces the conflicts before the meeting. The meeting shrinks to the decisions only.

Program Manager

Coordinating at scale

Orchestrates many interdependent projects toward a single enterprise outcome. Success is measured across the whole, not any one part.

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Coordination

Credibility anchors

30–40 interrelated projects
SVP-level governance and prioritization
Executive dashboards in Power BI and Smartsheet

IN PRODUCT TERMS

Product equivalent: Backlog ownership across a program — sequencing 30–40 interdependent workstreams like a multi-team product roadmap.

Artifacts owned: program-level backlog and dependency map; release train plan; Power BI and Smartsheet dashboards reporting what shipped and why to SVPs.

I lead people as people to develop, not resources to consume.

What stays human

Holding the line between competing project owners and executives. The negotiation, coalition-building, and judgment about which outcome the program actually exists to serve. AI coordinates the information. The program manager coordinates the people.

The Approach

Project network signals

Schedule changes
Cross-project dependencies
Competing resource demands

AI-supported coordination

Map
Detect collisions
Cascade impact

Program decisions

Reconcile timelines
Sequence dependencies
Align owners

Fragmented

Status held across separate project plans

Multiplied

Dependency collisions surfaced early

Still human

The negotiation and coalition-building

Activity 1

Cross-Project Dependency Management

Pre-AI

Manually map dependencies across dozens of projects, chase project managers for status, reconcile conflicting timelines, and hold the collision map in your head or in a sprawling spreadsheet.

AI-multiplied

AI ingests every project plan, maps the full dependency network, and flags where a slip in one project cascades into three others, surfacing the collision weeks before it lands.

Activity 2

Executive Dashboards & Program Health

Pre-AI

Pull data from every project, build the Power BI or Smartsheet dashboard by hand, refresh it each cycle, and write the narrative interpretation for SVP leadership.

AI-multiplied

AI aggregates program data continuously, drafts the health narrative (what is green, what is red, and why), and generates the executive read-out on demand, not once per reporting cycle.

Activity 3

Governance & Prioritization Facilitation

Pre-AI

Prepare governance materials, frame trade-offs for SVP-level sessions, capture decisions live, then manually cascade them back into every affected project.

AI-multiplied

AI prepares the trade-off analysis and scenario comparisons before the session, captures decisions in real time, and drafts the cascade of updates to each impacted project.

Activity 4

Cross-Team Backlog Sequencing

Pre-AI

Reconcile 30–40 project backlogs into a single program sequence by hand, chase dependency owners, rebuild the plan every time one team slipped.

AI-multiplied

AI holds the dependency graph, re-sequences when a slip is logged, and drafts the impact note to affected leads. I own the trade-off call when two teams can’t both be first.

Portfolio Manager

Allocating the investment

Decides where finite capital and capacity go across the enterprise. The call carries real money and real consequence.

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Decision support

Credibility anchors

$2M+ portfolio
20–30 concurrent initiatives
Governance enhancements that cut project delays by 25%
Influenced multi-million-dollar C-suite decisions

IN PRODUCT TERMS

Product equivalent: Product-portfolio strategy — deciding what gets funded, sequenced, paused, and killed across a $2M+ portfolio.

Artifacts owned: investment cases; prioritization framework; capacity plan; C-suite recommendations; from 2023, the enterprise AI delivery backlog (email summarization, contract and legal document search, bi-weekly AI enablement training).

I govern capital as something entrusted, not owned.

What stays human

The allocation call itself. Which bets the enterprise makes with finite capital, when to kill a sunk-cost initiative, and standing behind a multi-million-dollar recommendation in front of the C-suite. AI sharpens the decision. The portfolio leader owns it.

The Approach

Portfolio inputs

Business cases
Capacity constraints
Strategic priorities

AI-supported decision analysis

Normalize
Compare scenarios
Expose trade-offs

Portfolio decisions

Fund
Defer
Stop

Manual

Weeks to rebuild the prioritization view

Product equivalent: All of it, held by one person. I write the user stories and acceptance criteria, own the GitHub backlog directly, and prioritize it against revenue and funding.

Artifacts owned: jobs-to-be-done → validated workflows → GitHub issues, with AI drafting the first pass and me making the call.

Origin Story

It started with one recruiter running an agency on Gmail, Google Sheets, and a cell phone.

My wife owns a solo recruiting agency. For years it ran the way most solo agencies do: candidates in a spreadsheet, follow-ups in an inbox, and everything else in her head. I did what a business analyst does — sat with the workflow, mapped where time was leaking, and researched what a one-person shop could actually sustain. Then I built it: a CRM on GoHighLevel, a website, social presence, and analytics so she could see what was working.

That became TalentRApp — done-for-you CRM, automation, and technology infrastructure for recruiting agencies who don't have a technology department because they are the department.

CareVizor (2014–2016)

How to ship a marketplace from concept to market

TopCloserR (2025–present)

How to run a company where the operating layer is AI and the judgment layer is me

Dimension	CareVizor (2014–2016)	TopCloserR (2025–present)
The build	Hired developers and marketers; I wrote the specs and managed the sprints	Direct development through Claude Code; I set direction, review every change, and never hand-write the code
The team	Multi-disciplinary human team	One founder, an offshore content team, and a roster of AI agents I design and supervise
Requirements	Business requirements documents and functional specs, written by hand	Jobs-to-be-done translated into acceptance criteria and GitHub issues with AI drafting the first pass
Speed to change	Weeks per iteration	Hours per iteration
What I learned	How to ship a marketplace from concept to market	How to run a company where the operating layer is AI and the judgment layer is me

What stays human

deciding what the product should be, who it serves, what a fair rule looks like for everyone on the platform, and when the AI is confidently wrong. Those calls don't get delegated.

I keep building because the fastest way to understand a technology is to be accountable for it in production. Everything above this section is what I've delivered for employers. This section is what I've delivered for myself — and it's why I can walk into an AI transformation conversation knowing exactly what the hard parts feel like.

The constant

The work changes. The accountability does not.

Across every altitude, AI increases speed, scale, and reach. The human responsibility rises with the stakes.

Define what is real

Decide which requirements actually matter.

Own the commitment

Escalate, absorb, and hold the line on scope.

Coordinate the people

Align competing owners around the outcome.

Stand behind the investment call

Choose where finite capital and capacity go.

All 4

Founder application

Own the whole venture

Decide what should be built and why.

Integrity shows up in the costly call: the honest recommendation, the hard escalation, and standing behind a decision when it is under fire.

AI multiplies the work. It does not inherit the judgment or the accountability.

The judgment you've seen throughout this site doesn't come from a management framework; it comes from convictions. My faith is the root they grow from. It's why I see resources as entrusted rather than owned, why I believe every person carries dignity, and why I hold the line on the costly call. It shows up less in what I say and most in how I decide when a decision is hard. I'm also a husband and father, and the people I'm accountable to at home keep me honest about the ones I'm accountable to at work.

Contact

Start a conversation.

Whether the need is understanding the problem, delivering the work, coordinating at scale, allocating investment, or building something new, start with the challenge in front of you.

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