The hype cycle for Generative AI is settling, and the real work of integration has begun. Enterprises are moving beyond "chatbots" to building autonomous agents that can reason, plan, and execute complex workflows.
Privacy First: RAG and Local Models
The biggest barrier to entry for enterprise AI is data privacy. Sending sensitive financial or legal data to a public API is a non-starter. This is where Retrieval-Augmented Generation (RAG) and local models (like Llama 3 or Mistral) come in.
By running models within your own VPC and using vector databases to inject context dynamically, you ensure that your data never leaves your infrastructure.
Agentic Workflows
We are seeing a shift from simple "Q&A" to "Agentic" workflows. Instead of just asking "What were Q3 sales?", an agent can be tasked with "Analyze Q3 sales, identify underperforming regions, and draft a report for the sales director."
The Future is Hybrid
The most effective systems use a hybrid approach: small, fast models for routing and basic tasks, and large, reasoning-capable models for complex analysis. This optimizes both cost and latency.