People leadership
Develop supervisors and analysts through expectation setting, calibration, feedback, performance reviews, and direct support on complex quality issues.
AI deployment • customer operations • contact-center transformation
Quality, CX & AI Operations Leader
I lead quality organizations, AI-supported QA transformation, and customer-experience improvement across large, regulated service operations.
12+ years of leadership experience | Programs supporting approximately 950 agents | Distributed and multi-industry operations
12+years of quality, CX, and operations leadership
950agent program scale supported
78% → 94.3%quality performance improvement
34% → 99%audit execution and accountability
30Kmonthly interactions supported
95%+AI-output validation accuracy
2×QA productivity
>50%lower manual effort
~90%automated evaluation coverage
~$260Kannualized savings
Representative, anonymized outcomes. Figures are generalized to protect client and employer confidentiality.
Leadership at scale
Quality, CX, and AI operations leadership for regulated service teams
Lead quality and customer-experience operations for a regulated program supporting approximately 950 agents through three supervisors and 18 quality analysts. Establish performance expectations, review cadences, calibration standards, coaching priorities, escalation controls, and corrective-action accountability across distributed teams.
Operational governance
Own audit execution, scorecard interpretation, disputes, risk visibility, action plans, and leadership reporting.
Cross-functional influence
Partner with Operations, Training, Workforce, Technology, vendors, and client stakeholders to resolve recurring customer and process issues.
What I lead
Quality strategy, AI-supported QA, and customer-experience improvement
I lead quality organizations, AI-supported QA transformation, and customer-experience improvement across large, regulated service operations.
Quality strategy and governance
Set direction for scorecards, standards, review criteria, calibration, and executive visibility so quality stays aligned to business goals.
QA teams and performance systems
Build the routines, expectations, coaching cadence, and accountability that help supervisors and analysts improve performance consistently.
AI-supported evaluation and validation
Use AI where it strengthens review coverage, exception handling, and validation accuracy without losing human judgment or trust.
Calibration and scorecard governance
Keep scoring defensible, reduce drift, and make disputed criteria visible so teams can calibrate around shared standards.
CX analytics and root-cause analysis
Translate contact patterns into clear reason codes, issue clusters, and action plans that leadership can use quickly.
Coaching effectiveness and corrective action
Make sure findings turn into follow-up, coaching, and closure so improvements stick instead of living only in reports.
How I create value
Detect → Validate → Diagnose → Assign → Improve → Measure
- 01
Detect
Surface trends, defects, and exceptions early before they become larger operational problems.
- 02
Validate
Confirm the signal with calibration, human review, and a clear understanding of the operating context.
- 03
Diagnose
Translate symptoms into root causes, issue clusters, and actionable patterns.
- 04
Assign
Route each issue to a clear owner, due date, and measurable next step.
- 05
Improve
Use coaching, process changes, knowledge updates, and tool changes to address the issue at its source.
- 06
Measure
Track whether the fix worked and whether the business result improved.
Selected case studies
Proof across deployment, adoption, and operations
These public-safe examples show how I turn quality signals into validated, owned, measurable action with clear business results.
Quality turnaround
Turning audit performance into a stronger operating system
Challenge: Quality performance was 78%, and audit completion was 34%.
Leadership actions: Strengthened standards, performance visibility, coaching accountability, audit controls, and corrective-action follow-up.
- Result: Quality improved to 94.3%
- Audit completion reached 99%
AI-enabled transformation
Scaling AI-supported quality review
Challenge: Manual sampling limited visibility and required substantial review effort.
Leadership actions: Implemented AI-supported evaluation, human validation, calibration, exception handling, reporting, and adoption routines.
- Result: ~30,000 monthly interactions covered
- 95%+ validation accuracy
- ~2× productivity and >50% lower manual effort
Risk and financial improvement
Reducing exposure through faster ownership and closure
Challenge: Recurring defects and delayed corrective actions created avoidable client exposure.
Leadership actions: Improved early detection, RCA, ownership, escalation, and closure verification.
- Result: Reduced penalty exposure by approximately $12,500 per month
- Clearer ownership and faster remediation
Why this background matters
I understand what enterprise AI has to survive in the real operation.
I have worked where platform capability meets frontline behavior, policy, knowledge, customer expectations, compliance, reporting, and executive accountability.
That experience helps me identify deployment risk early, ask better technical and operational questions, and keep implementation teams focused on outcomes—not just configuration completion.
Proof of work
Explore the systems behind the results
Quality, AI Operations & Deployment Dashboard
An interactive operating view connecting quality performance, AI validation, deployment readiness, customer-impacting defects, ownership, and measurable improvement.
Open DashboardDeployment & AI Operations Case Studies
Detailed implementation stories, delivery lifecycle, governance controls, platform evaluation approach, operating metrics, and adoption practices.
View Case StudiesQuality Management & AI Operations Portfolio
An anonymized portfolio covering scorecards, interaction analysis, calibration governance, AI-output validation, insight reporting, dashboards, and 90-day planning.
Open PDF PortfolioTechnical fluency
Credible with technical teams. Grounded in business operations.
I am not positioning myself as a software engineer. I bring enough hands-on technical depth to understand implementation dependencies, evaluate workflow behavior, collaborate effectively with Product and Engineering, and translate technical decisions into usable operating practices.
Observe.AI pilotsSnowfly / Q-TelligentCallMiner pilotsChatGPTClaudeGeminiLocal LLMsHuman-in-the-loopUATJiraConfluenceSalesforceFive9Power BITableauLinuxDockerAPI / JSON concepts
Best-fit roles
Where this experience creates the most value
Primary roles
Quality Manager
Senior Quality Manager
Quality Operations Manager
CX Quality Manager
Adjacent leadership roles
AI Quality / AI Operations Manager
Conversation Analytics Manager
QA Governance Lead
AI Implementation or Process Improvement Manager
Let’s connect
Open to Quality Manager, Senior Quality Manager, Quality Operations, AI Quality, and CX Performance leadership opportunities.
I bring the combination of people leadership, quality governance, AI-supported operations, and measurable transformation needed to strengthen both frontline execution and executive visibility.