Beyond the Model: The Tools That Will Make Agentic AI Work
AI agents are on the rise, but without the right tools, companies struggle to adopt them. Large Language Models (LLMs) are transforming industries, and businesses are racing to deploy Agentic AI—AI-powered systems capable of autonomous decision-making and execution.
Startups are moving fast, building AI agents as core products. Enterprises, on the other hand, remain cautious, slowed down by concerns over security, compliance, governance, and reliability.
For Agentic AI to scale, companies need better tools—tools for reliability, security, and governance. This presentation examines how nimble startups "move fast" to integrate LLM capabilities, while established enterprises "take it slow" with caution and oversight.
Stephen Li
Enterprises: Taking It Slow
Document Summarization
The most common enterprise use case is making massive amounts of text more digestible, like "chatting with a 1,000-page manual."
Risk Mitigation
Data security, regulatory compliance, and brand reputation concerns lead to controlled pilot programs in isolated environments.
Traditional ML Dominance
Traditional Analytics & Machine Learning still deliver far more business value than LLMs or GenAI—an 80/20 split in favor of traditional methods.
Data Quality Focus
Without strong data management, neither traditional analytics nor LLMs can deliver meaningful results.
Most enterprise AI applications remain internal rather than customer-facing. High-profile failures like Air Canada's chatbot disaster have made many companies hesitant to fully trust AI-driven agents.