The fundamental model of AI use is changing. It's moving from 'assisted' AI, which helps humans with their tasks, to 'agentic' AI, where autonomous systems perform tasks. This paradigm shift requires new methods for adoption, management, and measuring success, moving from 'seats' to 'tokens'.

The shift from chatbots to agents represents a jump up the 'use case ladder.' Simple chat focuses on individual generation (drafting emails). In contrast, agents tackle systems-level work like workflow automation and process monitoring, moving AI's value from personal productivity to impacting entire business systems.

Anthropic capturing 70% of new enterprise AI buyers indicates a market maturation. Companies are moving beyond chatbot pilots and are now deploying deeper, agentic systems into core workflows, making Anthropic the 'new enterprise default' for production-grade AI.

Early ChatGPT use was dominated by information queries ('asking'). The release of agentic tools like Codex triggered a fundamental shift toward users delegating complex tasks ('doing'), signifying a new era of human-AI collaboration and workflow automation.

The concept of a massive "agentic workforce" is not a distant future scenario but a current reality. Companies are already deploying systems with up to 70,000 AI agents running simultaneously. This rapid, large-scale adoption indicates the transition is happening much faster than commonly perceived, creating urgent infrastructure needs.

The 'call and response' nature of large language models (LLMs) is not truly revolutionary for workflows. The significant shift comes from agentic AI, which can connect to various systems and execute multi-step tasks. This moves AI from a content generator to a powerful workflow automation tool.

AI's role has matured from assisting with trivial tasks like homework to autonomously managing complex, multi-step financial and scientific processes end-to-end at major companies like Coinbase, Salesforce, and Novo Nordisk, signaling a fundamental shift in its capability.

The firm's AI spending is increasingly driven by autonomous agents executing entire workflows, not by individual employees in a chat window. This fundamentally changes corporate budgeting, creating a new challenge of allocating costs to cross-departmental processes rather than to specific people or teams.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.