An arXiv paper argues that while most organizations have adopted AI, their governance infrastructure lags behind, creating an "attestation deficit" where compliance claims cannot be verified. The author outlines an adaptive intelligence architecture intended to enforce AI policy continuously in real time rather than through periodic review.
A new arXiv paper frames the decision of when firms should adopt artificial intelligence as a timing problem rather than a simple cost-benefit calculation. The author argues that because the technology frontier keeps improving, implementation is partly irreversible, and firms build capabilities only by deploying systems, companies face a trade-off between adopting early and waiting for better options. The work applies a real-options model to this setting.
A new arXiv preprint argues that enterprise AI agents frequently work in demos but fail once they are asked to run continuously in production. The author attributes this to pilots that never ship and to deployed systems that discard feedback instead of learning from it, and proposes an approach called substrate inversion to address the gap. The work is categorized under cs.AI and is cross-listed on arXiv.
Salesforce has announced an Enterprise AI Harness designed to link AI systems with enterprise data in a controlled manner. Alongside it, the company is rolling out new Agentforce agents aimed at sales, customer service, and e-commerce workflows. The offering targets businesses that want to deploy AI agents while keeping data access governed.
A report finds that businesses in Germany overwhelmingly deploy AI applications from American providers such as OpenAI, Google and Microsoft, while Chinese models see virtually no corporate use. It also notes that the international lead once held by those US vendors has largely disappeared. The piece frames this as a gap between German corporate adoption patterns and the broader global model landscape.
A cross-listed arXiv paper introduces IBIB, a protocol for evaluating AI systems as they are actually deployed inside enterprises rather than as bare model checkpoints. The authors argue that real-world capability emerges from the combination of weights, serving configuration, precision, output contract, and harness, so scoring an advertised model name alone is a measurement error. After auditing 18 existing benchmarks and finding that every one grades model identifiers, the paper proposes routing-based measurement instead.
Nvidia and Palantir announced a partnership to apply AI to supply chain management, beginning with Nvidia's own network of roughly one million parts. The collaboration aims to improve planning and logistics visibility for large-scale manufacturing operations. Palantir's software platform will work alongside Nvidia's computing infrastructure for the effort.