The paper introduces the AI Exposure and Resilience (AI-ER) framework, a two‑dimensional assessment tool for evaluating how artificial intelligence impacts software-based business models. AI exposure measures the pressure AI exerts on a company’s value proposition, competitive stance, margins, and customer access, while AI resilience gauges the firm’s capacity to absorb, adapt to, and economically leverage that pressure. The framework derives metrics from current AI capabilities, deployment contexts, and research on business models and adaptability, and it includes an explicit evaluation of evidence quality and confidence, allowing for a traceable company profile that can be refined from public data to internal insights.
arXiv:2609.21192v1 Announce Type: new
Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
By John Cuneo, David Chun, Gaurav Khanna
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
By Javier Irigoyen, Roberto Daza, Aythami Morales, Julian Fierrez, Ruben Tolosana, Ruben Vera-Rodriguez, Francisco Jurado, Alvaro Ortigosa
arXiv:2607. 23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
By Muhammad Tukur, Hayatullahi B. Adeyemo, Tao Chen, Nour Ali, Anis Zarrad, Rick Kazman, Marco Agus, Rami Bahsoon
Five AI value models show how leaders can sequence AI from workforce fluency to process reinvention and build durable business advantage.
The paper argues that the rapid pace of AI-driven change creates an adaptation gap, making human resilience a crucial capability for the AI era. Resilience is defined as the ability to absorb disruption while maintaining effective action and agency, and is examined at psychological, social, and organizational levels. The authors link resilience research with AI-in-the-loop experiments, showing that AI assistance can boost productivity, empathy, and calibrated reliance, and propose a socio‑technical agenda for education, workplace design, governance, and evaluation.
By Shaoshan Liu, Anina Schwarzenbach, Yiyu Shi