Ron Mitchell

AI trend summary (for business leaders) Autonomous AI agents — software “digital workers” that can perform multi-step tasks, make decisions, and talk to your systems — are moving from labs into real business use. Big cloud providers and startups now offer agent frameworks and low-code studios (think Copilot/Custom GPT-style tools and agent orchestration platforms) that […]

Short summary A fast-growing AI trend is enterprise use of retrieval-augmented generation (RAG) powered by vector databases. Instead of asking a general LLM to guess answers, companies connect large language models to their internal documents, FAQs, CRM records, and knowledge bases. The LLM uses embeddings stored in a vector database to retrieve the most relevant

Quick take Companies are rapidly adopting Retrieval-Augmented Generation (RAG) and vector databases to connect large language models (LLMs) to private documents, CRM records, and SOPs. Instead of relying on generic web-trained models, businesses embed their own data into vector stores (Pinecone, Chroma, Milvus, etc.) and use RAG to provide accurate, context-aware answers for customer support,

AI agents — autonomous systems built on large language models — are moving fast from experimentation into real business use. Today’s agents can read documents, query internal databases, draft emails, run routine workflows, and even coordinate multi-step processes across tools like CRMs, ERPs, and ticketing systems. Frameworks such as Auto-GPT, LangChain and vendor offerings from

Quick summary – There’s a clear surge in business adoption of autonomous AI agents and retrieval-augmented generation (RAG). Instead of simple chatbots, companies are building AI agents that act across apps (CRM, email, ticketing), pull facts from private data stores, and complete multi-step tasks automatically. – These agents combine large language models, vector databases (for

Quick summary In recent months, many businesses have shifted from generic chatbots to systems that answer questions from a company’s own data. This trend is driven by Retrieval‑Augmented Generation (RAG) — a technique that pairs large language models with vector databases to fetch and use relevant documents before generating an answer. The result: AI that

Quick summary Meta’s Llama 3 (released 2024) is a major step in enterprise-ready large language models (LLMs). It delivers stronger reasoning, larger context windows, and model options that make private or hosted deployment easier for businesses. That means faster, more relevant AI assistants, better document search and summarization, and improved automation for sales, support, and

Quick summary A new wave of “AI agents” and enterprise copilots (think Microsoft Copilot, Google Gemini for Workspace, and a growing set of specialist agents) is moving from experiments into real business use. These systems combine large language models (LLMs), retrieval-augmented generation (RAG), and connectors to internal apps to automate tasks, draft reports, summarize meetings,

Recent trend: Autonomous AI agents — not just chatbots — are moving into real business workflows. Major AI vendors and startups have been releasing tools that let companies build agents that can read your systems, fetch documents, draft actions, and actually execute tasks (like updating CRMs, booking meetings, or generating reports). These agents combine generative

AI trend snapshot AI “agents” — autonomous, goal-driven systems built on large language models and tool integrations — are moving from labs into real business use. Major platforms and startups are adding agent frameworks that can read documents, query databases, call APIs, update CRMs, and trigger workflows without constant human prompts. That means faster reporting,