Ron Mitchell

AI agents — autonomous, task-focused AI tools that can read, act, and learn across systems — are moving from experiments into everyday business use. Companies are using agents to automate end-to-end tasks like customer triage, quote generation, invoice reconciliation, and sales outreach. The result: faster response times, fewer manual steps, and new capacity for teams […]

Short summary Retrieval-augmented generation (RAG) is becoming one of the most practical ways businesses use AI today. Instead of asking a general large language model (LLM) to answer from memory, RAG fetches relevant, company-specific documents (using embeddings and a vector database) and feeds that context into the LLM. The result: faster, more accurate answers that

AI agents—automated systems that plan, act, and connect to tools—are moving from experiments into real business use. Companies are using agents to run multi-step tasks like generating client reports, triaging support tickets, automating order processing, and even coordinating marketing campaigns. The result: faster operations, fewer manual handoffs, and new ways to scale knowledge work. Why

AI trend in focus: autonomous AI agents are moving from experiments to real business use. These are AI systems that can take multi-step actions — like reading a CRM, drafting outreach, scheduling follow-ups, and updating records — with minimal human direction. Companies are using agents to speed sales cycles, automate routine customer service tasks, generate

Short summary Autonomous AI agents (think Auto-GPT, agent frameworks in LangChain, Microsoft/Google “Copilot” style assistants) are moving from demos into real business use. These agents can perform multi-step tasks — like pulling data from CRM, drafting an answer, updating records, and scheduling follow-ups — without a human clicking every step. Companies are piloting agents for

Quick summary Enterprise AI copilots and autonomous agents are moving from experiments into real business use. Companies are building copilots that combine large language models with company data (using retrieval-augmented generation and vector databases) to give staff instant, accurate answers. At the same time, agent frameworks automate routine multi-step tasks—like preparing reports, routing requests, or

Quick summary Companies are increasingly moving from public, general-purpose AI models to private LLM setups that combine fine-tuned models with retrieval-augmented generation (RAG) and vector databases. The shift is driven by data privacy concerns, the need for accurate, auditable answers, and new regulatory pressure (e.g., data protection and AI laws). This trend gives organizations a

AI agents — autonomous, tool‑using AI that can read, act, and follow multi‑step tasks — are moving from labs into real business use. From intelligent sales assistants that draft outreach and update CRMs, to reporting agents that pull live data and deliver board‑ready summaries, companies are using agents to speed up routine work and scale

Trending topic summary AI is moving from experiments to practical business systems because companies are combining large language models with their own data using Retrieval-Augmented Generation (RAG). RAG + vector databases (Pinecone, Weaviate, Milvus and others) lets LLMs search secure company knowledge — docs, CRM records, SOPs — and generate accurate, context-aware answers. That shift

AI agents — autonomous, multi-step AI systems that can act on your behalf — are no longer experimental. Over the last 12–18 months, companies and platform vendors have moved from demos to real deployments: agents that pull information from your knowledge base, run approval workflows, draft emails, schedule follow-ups, and trigger downstream systems. This trend