Generative artificial intelligence (GenAI) is moving from a novelty confined to chatbots and content drafting into something enterprises are beginning to fold into how they actually decide things: pricing, hiring, supply chain routing, capital allocation. This paper examines that shift through the lens of decision intelligence, the discipline concerned with engineering better organizational decisions by combining data, models, and human judgment. Using a PRISMA-informed narrative review of academic and industry literature published mainly between 2019 and 2026, the paper traces how large language models and related generative systems are being embedded into enterprise decision workflows, what measurable value they are producing, and where they fall short. The review finds genuine opportunities: compressed analysis cycles, wider access to sophisticated reasoning for non-specialist decision-makers, and new forms of scenario generation once reserved for expert analysts. At the same time, the literature converges on a stubborn set of challenges, including hallucinated or unreliable outputs, algorithmic bias, unclear governance accountability, and a persistent gap between pilot-stage enthusiasm and enterprise-level financial return. The paper argues that organizations capturing durable value are not necessarily those with the most advanced models, but those that have redesigned decision workflows, built human-in-the-loop verification into high-stakes processes, and treated GenAI as a collaborator rather than an oracle. It closes with practical implications and a short research agenda.
Naresh Sharma, Rohit Kumar, Himanshu Verma et al.· Journal of Intelligent Decis...· 0 citations
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5 = 77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.
Raunak Kumar, Anuj Pal, D. Solanki et al.· bioRxiv· 0 citations
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