For 15+ years I have built, directed, and scaled client operations into sustainable, high-achieving systems, with AI program management as the co-equal discipline that modernizes how the work runs. My core duty is enterprise discovery: finding the revenue leaks, client performance gaps, and operational bottlenecks that cap growth, and solving them at the root. I build and scale the high-performing teams that execute, direct the executive stakeholder management behind every high-stakes decision, and own the program and project management that moves it across teams and departments, with AI adoption, governance, change management, and workflow automation built into how the work gets done. Client operations and AI program management: two master disciplines I build and run as one operating command.
Executed operating systems I built for specific client environments: the workflow designs, SOP programs, and operating architecture I delivered on the ground, finding the gaps and bottlenecks and building the systems that closed them. These are the applied results of the master frameworks above, the work behind renewal retention up 60% against a 10% churn baseline, 40% ROI and MRR improvement, a customer success organization built from zero to 40, and a 150+ account portfolio.
My most demanding field system, in three parts: the operational master playbook, an executive brief, and a 90–120 day transformation plan. Built across a 150+ account portfolio with cross-functional teams of up to 50, stabilizing 600 to 1,000 high-risk accounts and partnering with municipal officials and executives to drive a full site turnaround.
3-Part System View the system →The founding-operations build for a pre-launch proptech SaaS: a five-system operating framework (account workflow, risk-flag health, support ticketing, revenue retention, SOPs) and a 30/60/90-day roadmap to stand it all up before go-live. Two parts: the strategic plan and the 90-day execution briefing.
2-Part System View the system →Full operational build for CoreForce: a client operations intelligence architecture connecting Account Management, Product, Engineering, Support, and Leadership through shared context, with a NotebookLM intelligence hub, cross-functional workflows, and a 90-day rollout. Executive presentation plus department playbooks.
Full System View the system →The founding build I delivered at Relotz: standing up Client Success and Revenue Operations from zero and scaling it to a 40-person operation. The executed engagement behind the SaaS Client & System Operations executive playbook. Built the customer success and revenue operations function from zero to a 40-person organization, lifting renewal retention 60% against a 10% churn baseline and partnering with Sales, Product, and Engineering leadership.
Delivered Build View the system →The executive systems came later. The foundation came first: portfolio management, account management, stakeholder relationships, and the client cadence I still run on today. Two early roles set it 15+ years ago, and one business I own and operate now shows the same disciplines carrying a full Director-level load today.
As Client Relations Portfolio & Operations Manager, I managed strategic relationships with 100+ real estate investment stakeholders, led portfolio stabilization and operational turnarounds across multifamily and commercial assets, and advised investors on acquisition and disposition decisions. This is where portfolio management, stakeholder relationships, and operational turnaround became my craft.
Portfolio & stakeholder foundationAs Health Benefits Event & Account Associate, I delivered benefits education and member onboarding across healthcare facilities, growing into a trusted account manager for 50+ union member accounts as the primary liaison between members, providers, and fund teams. This is where account management and client relationships began: know the person, resolve the issue, keep the trust.
Account & client foundationAs Owner & Principal Advisor, I carry the full Director load myself: directing a six-person 1099 team across accounting, campaigns, and sales consulting, owning 30 to 50 client engagements a year as discrete projects from intake through funding, and securing $1M+ in small-business and startup financing. I run the AI operations behind it too, NotebookLM as the team's single source of truth, Zapier automations that remove manual workload across the practice, and generative AI driving client strategy and expansion planning.
Director-level ownershipPortfolio management, account ownership, stakeholder relationships, and a healthcare and investor cadence, learned early and built on ever since. The director-level systems above stand on these foundations.
These are the deep operating blueprints behind how I build and run client operations and AI at scale: the closed-loop models, department-level process design, accountability, measurement, and governance I architect into a system. Each is a full executive build with its own architecture, process diagrams, and evidence boundary separating documented fact from reconstructed method.
My strategic executive playbook for field operations, team transformation, and performance recovery: how I enter a regional portfolio or multi-site operation, establish a measurable baseline, retrain managers and teams, rebuild SOP discipline, and govern adoption to revenue. Built for any proptech, regional real-estate, or large-team environment, proven across a 150+ account portfolio.
Executive playbook Explore the framework →My deepest playbook and the core of my career direction. How I design and build AI-powered operations on the BPM lifecycle (design, model, automate, analyze, optimize) with an enterprise automation stack, Zapier, NotebookLM, Asana, ClickUp, Claude, ChatGPT, and Google Gemini, then close the loop with the adoption and adaption lifecycle that makes the team run on it. The build and the adoption together.
Deepest executive playbook Explore the playbook →My VP-level design playbook for building a start-up's client and revenue operations architecture from zero, across seven governed departments: revenue lifecycle, sales-to-CS handoff, account health, support and escalation, renewal and recovery, workforce enablement, and executive intelligence with human-in-the-loop AI.
Executive playbook Explore the playbook →A targeted diagnostic for teams leaking revenue at the seams, or teams that want to scale but cannot see where to start. The link-wheel discovery method that maps the client and revenue journey and names the handoff leaks, paired with the collaboration engine that unites the team to close them. Handoff and leak diagnosis to retain and scale.
Executive playbook Explore the playbook →My system for taking an organization beyond AI pilots to governed, adopted, and measured transformation, the org-wide change program. Three parts: the master playbook (architecture), an executive brief, and a 90–120 day transformation plan (the execution engine). Eight workstreams, AI as the governing method. Companion to my AI Operations playbook.
3-Part System Explore the framework →These are my own executive strategic playbooks, the repeatable operator frameworks I bring into a role, not work done for a single organization. Each is a full designed system with its own architecture, process diagrams, evidence boundary, and governance. Every document is watermarked.
Tracing the client journey through every department handoff, sales, landing, response time, onboarding, software training, and adoption, to find where revenue leaks and close it with targeted training for clients and employees. A 3-part system.
3-Part System Explore the framework →How I train, develop, and build high-performing, AI-enabled teams of managing leaders: development mechanics, accountability and ownership, AI discovery and automation, the AI enablement stack, governance, measurement, and scaling.
Full framework Explore the framework →Treating the team as the revenue engine: reading where revenue leaks, tracing it to the team behavior behind it, and closing the gap so retention, expansion, and MRR grow. The skill-to-revenue diagnostic, the revenue leak map, and a data-led management philosophy.
Full playbook Explore the playbook →The policy and control repository for AI model risk in financial services: validation standards, fairness controls, escalation rules, and risk-tiered review cadence, aligned to SR 11-7, Basel, and the EU AI Act.
Live document View the framework →Closing communication gaps and delays so no client goes unheard, unnoticed, or unengaged: onboarding, queue acknowledgement, continuous response, built as proactive infrastructure or inserted to fix a broken operation.
Full playbook Explore the playbook →Automating visibility across the client journey, from new client to renewal: a self-assembling reporting pipeline, per-stage health signals, and executive and client-facing views that surface risk before a renewal is lost.
Full playbook Explore the playbook →Automating updates, responses, internal tickets, and team file reporting so a team runs on its own rhythm: work that assigns and tracks itself, and reporting leadership can trust without asking.
Full playbook Explore the playbook →Operational judgment, built into working tools. This is a client operations and AI suite spanning the full lifecycle, decide, design, deploy, enable, track, and report. Eight are live now; the rest are in active build. Click into the live tools and try them.
Describe any AI workflow or tool idea and the engine returns a full investment analysis: an overall score index, weighted sub-scores for impact, effort, risk, and readiness, a workflow improvement estimate, pros and cons, a recommendation, and a stress test grounded in current marketplace research.
Live tool Open engine →Enter a company's assessment, troubles, blockages, focus areas, and team structure. The mapper returns a phased operating plan and workflow architecture with owned systems, assigned owners, and measurable outcomes, built on the structure behind real client systems.
Live tool Open mapper →The execution companion to the mapper. It reads the operating plan and produces the transformation sequence: phased actions with owners and checkpoints, plus concrete deployment steps to stand up each workflow pillar.
Live tool Open guide →Not every process is worth automating. Score any workflow on business impact, manual effort, frequency, and automation fit, and get a priority verdict, Automate Now, Plan & Pilot, Backlog, or Leave As-Is, so re-engineering effort goes where it pays off first.
Live tool Open matrix →List a manual process and the mapper classifies each step, flags what AI and automation can take over, keeps human judgment where it belongs, and returns a redesigned workflow with a clear, non-technical deployment recipe for every step.
Live tool Open mapper →A redesign only pays off if people use it. Measure adoption and real time saved together, get a Sustained, At-risk, or Stalled verdict, and a tailored 3-step play to fix whatever is weak, turning pilots into proven, sustained impact.
Live tool Open tracker →Span-of-control visibility: workload versus capacity per person, cycle time, and blocked work, rolled into automatic red/amber/green health flags. See where the team is overloaded before it becomes burnout or missed targets. Ships with an Asana/ClickUp setup guide.
Live tool Open dashboard →Before adding headcount, know whether a team can absorb it. Scores capacity, process maturity, ramp speed, quality, and manager readiness into a readiness tier, then names the one gap to close first. Adding people to an unready team scales the dysfunction, not the output.
Live tool Open scorecard →Input a team's current state across data, skills, governance, and culture. Get a scored maturity report showing whether the organization can actually execute the use cases it wants.
In build Coming soonSubmit a use case and it is scored against governance criteria, returning the risk flags and controls a review board would require before approval, mapped to standards like the EU AI Act and ISO/IEC 42001.
In build Coming soonTurns priorities into a phased Diagnose, Design, Pilot, Scale, Sustain roadmap with milestones, owners, and sequencing, the plan that turns strategy into execution.
In build Coming soonBuilds role-based learning paths with certification checks and the reinforcement cadence research shows makes behavior stick, so training converts into real capability.
Planned Coming soonAssigns champions and an accountable executive sponsor with 30-day review gates, the human engine that research shows drives sustained adoption.
Planned Coming soonTracks adoption across the five-layer model (active users, workflow penetration, engagement depth, trust), with an owner per team, scheduled follow-ups, assessments, and reports.
Planned Coming soonPlateau detection that flags departments whose adoption velocity drops below threshold, so intervention happens in weeks, not quarters.
Planned Coming soonTracks team metrics and performance against targets, turning telemetry into the scorecards and review cadence that keep a transformation accountable.
In build Coming soonBoard-level value reporting that treats ROI as a learning system: outcome hypotheses set upfront, the workflow instrumented, and the case made with data.
Planned Coming soonThe capstone: a single executive dashboard rolling up phase status, adoption health, ROI, and risk into the one view a transformation lead brings to leadership.
Planned Coming soonAbove the tools sits the enterprise layer: the governance infrastructure and portfolio-management capabilities a Director or VP of Client and AI Operations is expected to build inside an organization. These labs demonstrate governing AI at scale, across policy, risk, investment, and value, not one implementation at a time.
The executive layer that governs every AI initiative as a portfolio: a live pipeline from Proposed to Retired, tracking investment, risk tier, ROI, adoption, and governance status, with an auto-generated steering-committee readout.
Live tool Open the office →Design how AI authority and execution operate across an organization, centralized CoE, federated, or hub-and-spoke, defining decision rights, funding and governance authority, ownership, and escalation architecture.
In build Coming soonVerified certifications across AI enablement, governance, cloud, and security. Full transcript available on request.
Trained to direct generative AI as a managed program: identifying high-value AI use cases across business functions, improving model output, and embedding responsible-AI governance and risk practice. This is the competency behind AI program management, running gen AI as leadership and strategy, not a single implementation.
Education: B.S. Cybersecurity Engineering, Minor in International Business, Concentration in Project Management. In Progress (began Jan 2025).
Currently pursuing: Microsoft AI Product Manager Professional Certificate, Advanced Microsoft 365 Copilot AI (Build & Use Agents), Microsoft AI-900, Microsoft Certified: Azure Administrator Associate (AZ-104), Google Associate Cloud Engineer, and IAPP Artificial Intelligence Governance Professional (AIGP).
Across 15+ years I build client operations and AI program management, my two master disciplines, from the ground up into systems that sustain and perform. The core function is discovery: I find the revenue leaks, client performance gaps, and bottlenecks that wall off growth, then architect and build the operating models, workflows, and SOPs that solve them. I build and scale the high-performing teams that run it, and I own the executive stakeholder management and the program and project management that carry change across teams and departments. I build these systems. I do not inherit them.
AI program management is the second discipline I command, not an add-on. Here that means directing AI adoption, enablement, governance, change management, and workflow automation as managed programs, and the cross-functional handoffs that put AI to work across the business, not IT operations. I run AI as a program: identifying high-value use cases across business functions, standing up governance and responsible-AI practice, designing the process and workflow architecture AI runs inside, and building the custom tooling and operating systems that carry it across client operations, in partnership with executive stakeholders and measured on outcomes.
The systems, playbooks, and tools on this page are my own builds, engineered for retention, ROI, and productivity at scale. Client operations and AI program management run as co-equal disciplines I build and own end to end.