There isn't a major conference where dozens of new Explainable AI methods don't appear: feature attributions, counterfactuals, sparse autoencoders. Yet when you look at real-world workflows — in healthcare, legal, finance — those explanations almost always end up discarded. They don't guide decisions, don't correct errors, don't feed back into the models. It's a stream of technical paper that runs parallel to practice, never crossing.

The reason, according to a sharp position paper circulating among ICML, NeurIPS, and ICLR reviewers, isn't the quality of the algorithms but the absence of shared foundations: vague problem formulations, under-specified evaluation objectives, and above all the lack of end-to-end pipelines that integrate explanations into human-in-the-loop feedback cycles. The team analyzed hundreds of papers and surveyed XAI practitioners, finding recurring patterns that block cumulative progress: ad-hoc metrics accumulate without knowing for whom they are designed; attribution faithfulness gets measured without testing whether an operator can turn it into action.

For those now deploying LLMs in on-premise architectures — driven by data sovereignty, GDPR compliance, or simply favorable TCO on steady workloads — this methodological void carries an immediate cost. A self-hosted model that decides on a credit application, a differential diagnosis, or a contract must account for its reasoning not against an abstract checklist, but to feed a decision-making process where the human retains final control. Without structured pipelines to receive, interpret, and return the operational meaning of an explanation to the model, the very concept of “auditability” becomes an ornament: you produce the report, file it, and move on. But the system learns nothing from the interaction, and the illusion of control risks cementing biases rather than resolving them.

The real fault line flagged by the research is between two speeds: on one side, an XAI community that self-evaluates with experiments disconnected from operational reality; on the other, an industry that, in the most regulated settings, needs systems where explainability is a living mechanism, embedded in continuous training and monitoring. It’s a misalignment reminiscent of the early days of software testing, when people settled for coverage metrics without asking what they were actually protecting. Those who move early to build explanation-driven feedback pipelines — perhaps starting from controlled on-premise environments, with full access to data and supervision signals — can gain a competitive lead that’s hard to replicate for anyone clinging to opaque, cloud-hosted black boxes.

The position paper closes with a concrete checklist to reverse course: define the XAI problem starting from the end user and their capacity to act, set success metrics anchored to real decisions, and engineer the feedback loop. For AI-RADAR readers, the deeper message is that the on-premise game isn't played only on GPU and VRAM, but on the ability to build around models an infrastructure of meaning that mainstream still lacks.