Fusion BPO audited under 15% of calls by hand in a high-liability environment. Violations surfaced days late. AI had been tried and rejected — QA teams wouldn't trust black-box flags. In four weeks I led the UX and governance design that turned sampling into continuous, explainable monitoring of 100% of calls. The breakthrough wasn't accuracy. It was explainability.
The governance view — 100% coverage, violations triaged by severity and confidence, feedback in minutes.
The organization wasn't operating inefficiently. It was operating blindly.
In a high-volume, high-liability contact center, policies against abusive language, regulatory breaches and brand conduct were strict — on paper. Enforcement ran on manual audits, random sampling, delayed feedback and subjective judgment. That combination created blind spots where violations went undetected and leadership could only react after the damage was already done.
That reframe moved the project from "automate audits" to "build governance infrastructure." Discovery ran tight and deep under a four-week clock: 12 QA analysts shadowed, 18 past violation cases walked through in retrospect, and dedicated AI-trust and mental-model sessions. Three insights set the entire direction.
An AI flag with no reasoning triggered immediate distrust. Analysts would either ignore it or rubber-stamp it — both useless. "Why" wasn't a feature request; it was the precondition for any action at all. Reason, evidence, severity and confidence had to ship together or the system was dead on arrival.
The 50–60% resistance was rooted in false-positive fear and accountability anxiety, not missing capabilities. A tool that disrupted the existing QA flow would lose, however clever. Seamless integration beat breadth every time.
To shift from lagging to proactive governance, QA managers and VPs needed severity-based trends and live escalation visibility — the difference between counting fires and preventing them.
The architecture rested on one principle that came straight out of research: never show a verdict without its reasoning. Every flag carried four things together — reason, evidence, severity and a confidence score. That single structure is what turned AI from a surveillance tool the team feared into governance infrastructure they trusted.
Continuous AI analysis of every call at 95% accuracy, eliminating the sampling blind spots that hid risk until it became a lawsuit.
Each violation arrives with its reason, the supporting evidence, a severity level and a confidence indicator — the "why" that makes a flag actionable.
Auto-triggered training, policy updates and escalation rules close the loop, turning each detection into a continuous-improvement signal for ops.
Measured in the beta
Projections & targets
A more accurate black box still gets ignored. I forced explainability as non-negotiable — even at the cost of headline accuracy gains.
The tempting path was to chase a higher accuracy number, because it's easy to put on a slide. But early trust assessments were unambiguous: non-technical QA users would not act on flags they couldn't understand, no matter how accurate. So I made explainability the non-negotiable core — reason, evidence, severity and confidence on every signal — and defended it to the C-suite in their own language: every undetected violation carried real legal and churn exposure, so adoption was the ROI, not a soft extra.
Two more calls made the four weeks possible. I pushed for a staged beta over a big-bang launch, controlling false positives first and earning leadership confidence with Week 2 metrics. And I scoped ruthlessly — cutting multi-language and edge cases to ship a trustworthy single-language core on time. Those trade-offs are exactly why the SUS sits at a v1 baseline; I traded polish for a system people would actually adopt under deadline.
Tune per-persona earlier to push SUS past 80 — the Q4/Q5 integration scores were mixed and foreseeable. And run legal co-design workshops up front, so regulatory constraints shaped the design from week one instead of being reconciled later.
Client — Fusion BPO Services (large-scale contact center operations)
Role — UX Leader: strategy, research, experience architecture, trust & explainability governance
Timeline — 4 weeks, strategic discovery to live beta
Team — Me (UX ownership), 2 Sr. Product Designers (UI execution), 1 UX Researcher (ops), with AI Engineering, Ops & Legal collaborators
What I owned — End-to-end research strategy on AI trust and adoption, system-level experience architecture across analysts, managers and agents, the trust/explainability metrics, translation of legal and regulatory risk into prioritized UX requirements, and the executive narrative shift from efficiency tool to enterprise risk-control platform.