Making AI Work: Leadership, Lab, and Crowd
This commentary from One Useful Thing proposes a framework for organizational AI adoption centered on three elements: leadership commitment, structured experimentation (lab), and distributed employee engagement (crowd). The piece offers practical guidance for companies navigating AI integration. As a tier-2 commentary source, it reflects practitioner thinking on enterprise AI deployment patterns rather than reporting new technical developments.
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Real AI Agents and Real Work
A commentary piece from One Useful Thing examining the practical deployment of AI agents in real work contexts, framing the tension between human-centered work and AI-generated productivity outputs. The piece appears to analyze how autonomous AI agents are changing knowledge work workflows. Published by a Tier 2 source known for applied AI analysis aimed at practitioners and researchers.
MIT Technology Review: Leadership challenges in hybrid human-AI enterprises
MIT Technology Review examines how leadership teams are adapting to a projected 300% surge in AI agent adoption over the next two years. The piece focuses on the organizational and managerial implications of AI agents that autonomously coordinate complex tasks across tools and environments, distinguishing them from prior automation paradigms. The article addresses strategic and workforce management questions for enterprises integrating agentic AI.
An Opinionated Guide to Using AI Right Now
A tier-2 commentary piece from One Useful Thing offering opinionated guidance on which AI tools to use in late 2025. The piece likely surveys the current landscape of frontier models and recommends specific tools for specific tasks. As a practitioner-facing guide, it reflects the state of the AI tooling ecosystem as perceived by an influential commentator.
A Guide to Which AI to Use in the Agentic Era
A tier-2 commentary piece from One Useful Thing offering guidance on selecting AI systems in the current agentic era, signaling a shift in framing from chatbots to agents as the primary use-case paradigm. The piece appears to survey the landscape of available AI tools and their appropriate applications. As a practitioner-oriented guide, it reflects the growing complexity of the AI tooling ecosystem as agentic capabilities proliferate.
Rethinking Organizational Design in the Age of Agentic AI
A MIT Technology Review commentary examines the gap between enterprise ambition and readiness for agentic AI adoption, citing survey data showing 85% of organizations want to be agentic within three years but 76% say their current infrastructure cannot support that transition. The piece focuses on organizational design challenges—people, processes, and workflows—as the primary barriers to agentic AI deployment at scale.
Management as AI Superpower
This commentary from One Useful Thing argues that management skills are becoming a critical capability for individuals working with AI agents. The piece frames the ability to direct, coordinate, and evaluate AI agents as analogous to managing human teams, suggesting that organizational and managerial competencies will differentiate effective AI users. It positions this as a key survival skill for the emerging era of agentic AI systems.
On Working with Wizards
A commentary piece from One Useful Thing exploring the metaphor of AI systems as 'wizards' and the challenge of working with them on the 'jagged frontier' of capabilities. The piece likely addresses how users can effectively verify and leverage AI outputs given the uneven and unpredictable nature of current model capabilities. As a tier-2 commentary source, it offers practitioner-level perspective on human-AI collaboration patterns.
Giving your AI a Job Interview
This commentary piece argues that as AI-generated advice becomes more consequential, users need systematic methods to evaluate AI reliability and quality—analogous to a job interview process. The author proposes frameworks for assessing AI outputs before trusting them for important decisions. The piece addresses the practical challenge of calibrating trust in AI systems across different use cases.