Human-Curated, AI-Enabled: A Framework for Reliable AI Deployment

Enterprise AI projects fail at rates between 70% and 95%. The dominant response—more data, larger models, better retrieval—addresses the wrong problem. These are grounding-axis failures misdiagnosed as infrastructure problems. AI systems lack access to the purposes they serve and the wholes their outputs enter. Scaling cannot fix what scaling did not break.

The Human-Curated, AI-Enabled (HCAE) framework provides a design discipline for reliable deployment. Four tiers supply the grounding AI structurally lacks: User-Curated (UCAE) for low-stakes ideation, Professional-Curated (PCAE) for routine domain work, Expert-Curated (ECAE) for high-stakes analysis, and Synthesis-Curated (SCAE) for formally verifiable domains. The framework is a decision tool, not a maturity model; the goal is matching tier to task, not maximizing tier. Building on the AI Dunning-Kruger (AIDK) framework, this paper operationalizes structural epistemic limitations into deployment guidance, addressing hybrid deployments, tier transitions, and the three-axis model explaining why horizontal and vertical solutions alone cannot resolve reliability problems.

Developed under the ECAE model described in this framework, with derivational contributions from Claude.