Ron Mitchell

AI agents — autonomous, task-focused systems that combine large language models with retrieval, tools, and automation — are moving from labs into real business use. Today’s agents can read documents, pull answers from company data, trigger systems, and even handle multi-step processes like invoice approvals, customer triage, and field inspections. That means faster decisions, fewer […]

AI trend summary AI agents — small, goal-driven systems built on large language models (LLMs) — are moving from labs into real business workflows. Instead of one-off chatbots or rigid RPA scripts, companies are combining LLMs, retrieval-augmented generation (RAG), connectors to enterprise systems, and simple orchestration logic to create agents that can complete multi-step tasks:

Big tech and enterprise software vendors have pushed “AI copilots” from demos into real deployments. Microsoft, Google and Salesforce — among others — are embedding large language models into office suites, CRM platforms and cloud tools so employees can draft documents, summarize meetings, generate code snippets, and automate routine tasks faster than before. Why this

Quick summary AI agents (autonomous workflows powered by large language models) plus Retrieval-Augmented Generation (RAG) are moving from experiments into everyday business work. Companies are using agents + RAG to automate customer support, speed sales ops, generate real-time reports, and reduce manual triage — while connecting models to company data so answers are accurate and

Big idea: Multimodal large language models (LLMs) and agent-style workflows are moving from demos to real business value. At Google I/O 2024, Google introduced Gemini — a family of multimodal models that read text, images, and more — plus enterprise APIs through Google Cloud. That shift is unlocking practical use cases: automated document review, image-aware

Short summary Enterprises are moving fast from generic public chatbots to private LLMs paired with retrieval-augmented generation (RAG). Instead of copying and pasting documents into a chat window, teams now build secure AI copilots that search company data, pull exact facts, and act inside workflows — all while keeping sensitive information under strict control. This

Big idea: More businesses are moving from experimenting with public chatbots to building private LLMs and retrieval-augmented generation (RAG) systems that power secure, task-focused AI copilots. Vendors like OpenAI, Anthropic, and Google pushed enterprise-grade models and tools in 2024–2025, and organizations respond by combining models with vector databases, access controls, and workflow agents to keep

Short summary The EU AI Act is the world’s first comprehensive law to regulate artificial intelligence. It groups AI systems by risk (unacceptable, high, limited, minimal) and sets strict rules for “high-risk” systems — including requirements for risk management, data governance, transparency, human oversight, and technical documentation. Some uses (like certain biometric social scoring) are

Short summary Companies are rapidly adopting retrieval-augmented generative AI (RAG) to build private, enterprise copilots that can read internal documents, CRM notes, SOPs and databases — then answer questions, draft responses, and automate workflows. Instead of relying only on a base large language model (LLM), businesses combine LLMs with indexed company data (via vector databases

What’s happening Companies are increasingly using Retrieval-Augmented Generation (RAG) and vector databases to build private AI assistants and automate reporting. Instead of relying only on general-purpose LLMs (which can hallucinate or lack context), RAG lets models fetch exact, company-specific facts from internal documents, CRM records, SOPs, and databases. This trend is powering smart customer support