Ron Mitchell

Quick summary – Recent traction in Retrieval-Augmented Generation (RAG) plus vector databases is turning general LLMs into dependable “knowledge workers” for businesses. – Instead of relying only on a model’s internal memory, companies are connecting LLMs to their documents, manuals, CRM records, and databases via vector search. That reduces hallucinations, speeds answers, and unlocks new […]

Short summary (for LinkedIn and business channels) Autonomous AI agents — AI systems that plan, act, and coordinate across apps and data with minimal human direction — are moving fast from demo to real business use. Companies are using them to automate repetitive workflows (customer triage, procurement approvals, IT ticket routing), generate timely reports, and

AI trend snapshot Enterprises are increasingly combining private large language models (LLMs) with Retrieval-Augmented Generation (RAG) and vector databases to build secure, accurate AI agents that automate real work—like answering customer questions from internal docs, creating reports from CRM data, or running parts of back-office workflows. This approach keeps sensitive data on-prem or in a

AI agents — autonomous, workflow-driven AI that can act on behalf of users — are moving from labs into the boardroom. Over the last year, more companies have started using AI agents to qualify leads, automate routine customer service, generate real-time reports, and orchestrate multi-step workflows across CRM, ERP, and collaboration tools. For business leaders,

Short summary Autonomous AI agents — software that uses large language models (LLMs) to plan, act, and carry out multi-step tasks across apps — are moving from experiments to real business use. From sales outreach and financial close to customer support triage, these agents can link to CRMs, ERPs, and databases to automate end-to-end processes

Quick summary Autonomous AI agents — software that can plan, act, and carry out multi-step tasks with little human direction — are moving fast from labs into real business use. From tools built on platforms like LangChain, Auto-GPT-style frameworks, and cloud provider agent services, companies are using agents to automate customer routing, extract and reconcile

Quick take: Enterprises are moving fast from one-size-fits-all cloud APIs to private, tuned large language models (LLMs) combined with retrieval‑augmented generation (RAG) and vector databases. The shift is driven by three business needs: protect sensitive data, control costs at scale, and get more accurate, context-aware outputs from AI. This trend is changing how companies build

Quick summary AI agents powered by large language models (LLMs) combined with retrieval-augmented generation (RAG) and vector databases are moving from pilot projects into mainstream enterprise use. Instead of simple chatbots, these AI agents can search internal documents, run workflows, call APIs, and deliver context-aware answers — automating routine decisions and boosting employee productivity across

A new wave of AI “agents” and copilot tools is moving from tech demos into everyday revenue operations. These AI-driven assistants can draft personalized outreach, book meetings, update CRMs automatically, summarize calls, and surface next-best actions — all while working across email, chat, and your sales systems. Big cloud and AI vendors plus fast-growing startups

AI trend summary (clear & quick) Businesses are increasingly combining Retrieval-Augmented Generation (RAG), vector databases, and autonomous AI agents to build secure, up-to-date assistants and automated workflows. Instead of only relying on generic LLM outputs, companies are indexing their internal knowledge (SOPs, CRM notes, product docs) into vector stores so models can fetch precise, context-rich