Challenge
Manual interaction sampling constrained coverage, delayed insight, and required substantial analyst and reporting effort. Leaders needed broader visibility without sacrificing accuracy, governance, or operational trust.
Deployment & AI operations portfolio
Enterprise AI implementation through the lens of delivery, adoption, and measurable outcomes.
These anonymized case studies show how I lead AI-enabled change from discovery and platform evaluation through validation, rollout, adoption, governance, and continuous improvement.
No proprietary employer, client, member, customer, or employee information is included. Exact figures are generalized where appropriate.
Case study 01 • AI implementation
Scaling AI-enabled quality operations
Outcome
Trusted, high-coverage AI-supported evaluation embedded into a repeatable operating model.
My role
Led the operating model, adoption, governance, and continuous optimization of AI-enabled Automated Quality Management and customer-insight workflows across multi-client, multi-site operations.
Stakeholders
Operations, Quality, Training, Reporting, Client Services, technology teams, platform vendors, supervisors, analysts, and client stakeholders.
Implementation approach
- Mapped the current state. Documented manual evaluation, reporting, calibration, coaching, and escalation workflows to identify constraints and failure points.
- Translated business requirements. Converted customer-handling expectations, QA standards, compliance needs, and operational goals into workflow requirements and scorecard logic.
- Established deployment gates. Used sampling, human review, calibration, UAT, defect analysis, confidence thresholds, stakeholder signoff, and escalation paths before broader adoption.
- Enabled users. Created SOPs, playbooks, scorecard guidance, calibration materials, dashboards, leader resources, and self-service documentation.
- Measured operational health. Tracked validation agreement, automation coverage, quality, productivity, turnaround time, defect patterns, adoption, stakeholder confidence, risk reduction, and financial impact.
- Optimized after launch. Maintained an enhancement pipeline based on user friction, risk, validation findings, dependencies, expected effort, and operational value.
What this demonstrates
Enterprise AI implementation, cross-functional delivery, human-in-the-loop governance, adoption enablement, operational scaling, KPI design, customer value realization, and continuous improvement.
Case study 02 • Platform strategy
AI platform evaluation and controlled transition
Outcome
A structured, customer-side decision process balancing capability, workflow fit, governance, adoption, continuity, and value.
Context
Evaluated AI-enabled quality and conversation-intelligence platforms, including Observe.AI, CallMiner, and Snowfly/Q-Telligent, in multi-client contact-center environments.
Decision lens
Capability, usability, workflow fit, validation reliability, integration considerations, implementation effort, data and governance needs, support model, adoption risk, cost, and measurable operational value.
Leadership responsibility
Connected frontline user experience, QA requirements, client expectations, operational controls, vendor capabilities, technical dependencies, and business-case considerations.
Requirements
Clarified business outcomes, user groups, evaluation use cases, reporting needs, governance constraints, and critical workflows.
Pilot evidence
Compared output quality, usability, reliability, exception patterns, and workflow friction through controlled review.
Operational fit
Assessed training effort, calibration support, reporting usability, ownership, escalation, and day-to-day sustainability.
Transition controls
Protected continuity through staged change, validation gates, documentation, stakeholder communication, and adoption monitoring.
Value realization
Connected platform decisions to productivity, coverage, quality, risk reduction, stakeholder confidence, and financial impact.
Feedback loop
Converted user friction and validation findings into enhancement priorities for technical partners and vendors.
What this demonstrates
Executive judgment, enterprise discovery, solution design support, vendor partnership, technical and operational tradeoff analysis, controlled change, customer advocacy, and implementation risk management.
Case study 03 • People and operations
Leading operational transformation at enterprise scale
Scope
Approximately 950 agents supported through three supervisors and 18 analysts in a regulated health-insurance environment.
Operating challenge
The program needed stronger execution discipline, clearer performance visibility, faster risk detection, consistent audit delivery, and more accountable cross-functional follow-through.
Leadership approach
- Set operating cadences across dashboards, risk reviews, calibration, action tracking, knowledge updates, training reinforcement, and executive reporting.
- Coordinated priorities across Operations, Training, Workforce, Reporting, Technology, client stakeholders, supervisors, and analysts.
- Maintained clear ownership, timelines, escalation paths, resource priorities, and follow-through expectations.
- Used defect trends, customer impact, complaint themes, escalation patterns, and QA variance to prioritize corrective action.
- Coached supervisors and analysts while reinforcing delivery standards, workload accountability, and measurable improvement.
What this demonstrates
People leadership, resource prioritization, service-delivery standards, portfolio visibility, executive communication, critical escalation management, regulated operations, and measurable performance improvement.
Delivery operating model
A practical system for repeatable enterprise implementations
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Explore synthetic 90-day metrics and leadership-ready action views.
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Review scorecards, AI governance, calibration, insight reporting, and improvement planning.
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I welcome conversations about deployment leadership, customer adoption, AI operations, and contact-center transformation.
Email Brian