Launched in August 2025, the AI-Assisted Review Framework (ARF) streamlines manual, multi-level product reviews into a scalable, expert-verified workflow. By combining autonomous assessments, real-time compliance monitoring, and AI-assisted reviewer tools, ARF improves consistency and efficiency across high-volume product categories.
August 2025 - Launched the AI-Assisted Review Framework (ARF), transforming product evaluation workflows across high-volume categories.
Lead UX Designer, Experience Strategy, Human-AI Interaction, UX/UI
Defined the end-to-end reviewer experience strategy, aligning AI autonomy with human expertise to enable a scalable, trustworthy human-in-the-loop review system.
ARF has transformed the way product reviews are conducted, establishing a unified, AI-first framework that scales across high-volume categories such as Clothing, Grocery, and Media. It redefines human–AI collaboration by combining speed, accuracy, and trust, and sets the foundation for an organization-wide intelligent review ecosystem.
To ground the solution in real operational behavior, we conducted 1:1 interviews with Operational Leads and Quality Operators working within a large-scale review platform. The focus was on understanding day-to-day responsibilities, pain points, workarounds, and decision paths involved in assigning classification outcomes to product records.
Operational Leads create daily evaluation batches based on operator availability and active workload.
Both Operational Leads and Quality Operators rely on frequent tool switching, manual tracking, and repetitive setup, increasing cognitive load and reducing throughput.
Daily workload planning is mechanical and time-intensive, often requiring ~1 hour each morning to balance capacity against incoming review volume.
Third-party add-ons compensate for missing core capabilities (e.g., highlighting, translation, cross-market context) but introduce:
Edge cases and historical decisions are scattered across multiple systems and documents, leading to repeated research and inconsistent application.
External translation and image-recognition tools are frequently used, but accuracy and context loss introduce ambiguity, especially in high-risk classifications.
New operators take months to reach the efficiency of tenured peers due to tool sprawl, cognitive overhead, and limited in-product guidance.
In the absence of clear, embedded guidance, final classification decisions often rely on individual judgment, increasing variability across operators.
Conceptual flow generalized for confidentiality.
Note: These personas represent observed behavioral patterns from user research. Terminology and tooling have been generalized to preserve confidentiality.
As operator experience increases, decision speed improves through heuristics, but consistency depends on access to context, guidance, and explainability rather than memory alone.
During research, operators frequently supplemented the core review workflow with third-party
browser tools to compensate for missing or inefficient capabilities. While these tools improved
speed and task completion, they also introduced risks around consistency, traceability, and
long-term scalability.
This section highlights observed patterns, not prescribed workflows.
These workarounds highlight a recurring theme: operators optimize for speed and confidence,
even when tooling gaps force them outside the primary system. At scale, this creates risk —
not because of human behavior, but because critical decision support is fragmented and
ungoverned.
This insight directly informed the need for native, explainable, and traceable AI-assisted
review capabilities within ARF.
Embed guidance directly into the workflow so operators understand why, not just what.
Treat translation and image understanding as core capabilities.
Shift from manual load balancing to system-assisted orchestration.
Reduce tribal knowledge by making expertise visible and transferable.
Replace ad-hoc extensions with a unified, auditable platform that:
Remove external tools without addressing core gaps.
Risk:
Conducted usability testing with six operators across multiple product categories using interactive prototypes to evaluate usability, transparency, and adoption readiness.
Adoption success depends more on human factors—clear explainability (XAI), visible control, and effective change management—than on model quality alone.
Reduction in secondary and tertiary reviews
Of reviews required changes
Of saved time reinvested in training and upskilling
Net capacity gain for expert operators
Throughput (reviews per year)
ARF established a new operating model for high-volume decision systems—one where AI accelerates scale, but humans retain authority, accountability, and judgment. The work reframed review from a manual throughput problem into a design challenge centered on trust, explainability, and change management.
More than a single launch, ARF laid the foundation for future AI-assisted workflows by proving that transparency, traceability, and human oversight are prerequisites for sustainable automation, not optional enhancements.