- AI-Native QA
- A QA program designed from the ground up around AI capabilities, where AI is the primary evaluator and humans act as the calibration and escalation layer.
AI-Native QA is a quality assurance architecture in which AI is not a supplemental tool bolted onto an existing manual process, but the foundational layer that handles evaluation at scale—with humans operating as calibration leads and escalation reviewers rather than primary scorers.
**AI-Native vs. AI-Assisted QA**: AI-assisted QA takes a traditional manual program and adds AI features—auto-transcription, suggested scores, or keyword flags—to speed up human reviewers. AI-native QA inverts the model entirely: AI evaluates every interaction by default, and humans engage only to calibrate criteria, resolve edge cases, and validate scoring accuracy over time.
**What AI-Native QA Looks Like Architecturally**: - AI scores 100% of interactions against dynamic, configurable rubrics - Human QA analysts focus on calibration sessions, rubric refinement, and disputed scores - Feedback loops are real time: agents receive coaching signals within hours, not weeks - Criteria evolve continuously as products, policies, and customer expectations change
**Why It Matters for CX Teams**: Traditional QA programs cannot scale with growing interaction volumes. AI-native QA removes the reviewer-to-interaction ratio as a constraint, enabling consistent, high-coverage quality programs regardless of team size. It also closes the feedback gap that limits coaching effectiveness in manual programs.