Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
McKinsey's 2024 Global Survey found that 78% of organizations now use AI in at least one business function — up from 20% in 2017. Generative AI adoption surged from 33% to 72% of organizations in a single year. Customer service (56%), content creation (46%), and software development are the most common enterprise use cases. Early adopters report an average 35% productivity improvement in AI-augmented functions.
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Related Terms
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Generative AI
AI systems that can create new content — text, images, code, audio, video — rather than simply analyzing or classifying existing data. Large language models and diffusion models are the primary architectures.
GPU (Graphics Processing Unit)
A specialized processor originally designed for rendering graphics, now the primary hardware used for training and running AI models due to its parallel processing capabilities.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can understand, generate, and manipulate human language. LLMs power chatbots, coding assistants, and content generation tools.
NVIDIA
The dominant manufacturer of GPUs used for AI training and inference, commanding approximately 64% of the $66.2 billion AI chip market as of 2024.
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