Search for a command to run...
Current AI systems—particularly Large Language Models—are increasingly assuming critical decision-making functions in organizations, yet they are structurally unable to provide traceable justifications for their outputs. This paper argues that the explainability gap is not a fixable implementation deficiency but a fundamental architectural problem: LLMs optimize for statistical plausibility rather than factual correctness, which accounts for empirically documented hallucination rates of up to 88% for legal queries. Post-hoc explanation methods such as SHAP and LIME, while widely adopted, are shown to be structurally inadequate for complex language models, often producing misleading pseudo-transparency rather than faithful representations of model behavior. In light of the EU AI Act (effective from 2026), tightening liability jurisprudence, and growing trust requirements from stakeholders, this paper examines the technical foundations of the black-box problem, evaluates the capabilities and limitations of current Explainable AI approaches, and analyzes the multi-layered regulatory landscape. It then formulates architectural principles for inherently explainable decision systems—including neurosymbolic AI, Retrieval-Augmented Generation with provenance tracking, and Concept Bottleneck Models—and derives concrete implications for corporate governance, risk management, and strategic planning. The paper concludes that a paradigm shift from systems that generate plausible answers to architectures that derive traceable decisions represents both a regulatory necessity and a strategic opportunity.