Ron Mitchell

AI agents are the next big wave in business tech. These are software “assistants” that can act on your behalf: pull data, run reports, draft emails, update CRMs, and even trigger backend processes. Over the past year we’ve seen big interest from enterprise tools (copilots, Power Platform automations, and agent frameworks) and startups building domain-specific […]

AI trend snapshot Autonomous AI agents — systems that can plan, execute, and follow up on tasks with minimal human prompting — are moving from labs into real business use. Think agents that draft and send personalized sales outreach, run multi-step analytics, auto-update CRMs, or manage simple vendor orders. Platforms like LangChain, Zapier AI Workflows,

AI trend snapshot Autonomous AI agents — low-code tools that can act on behalf of users across apps and systems — are moving fast from labs into real business operations. These agents can read documents, pull data from CRM/ERP, draft emails, and trigger downstream actions (like approvals or ticket creation) without constant human direction. That

Big idea in 2025: autonomous AI agents are moving from demos into real business use. These are software “workers” powered by large language models that can read documents, call APIs, route approvals, and complete multi-step tasks end to end. Companies are using agents to automate invoice processing, customer follow-ups, research and summary tasks, and repeatable

Big idea in AI right now: Retrieval‑Augmented Generation (RAG) + vector databases are the most practical path for companies to get real value from large language models (LLMs). Instead of asking a model to “know everything,” RAG lets LLMs fetch the right pieces of your company data (documents, manuals, CRM notes, product specs) and combine

Quick summary AI “agents” — autonomous, goal-driven AI systems that can use tools, call APIs, read company data, and carry out multi-step tasks — are moving from labs into business pilots and early production. Vendors, startups, and open-source toolkits (agent frameworks, RAG/vector search, and workflow orchestrators) make it easier to build agents that can handle

AI trend in brief Autonomous AI agents — software that can plan, act, and complete multi-step tasks with minimal human input — are moving from labs into real business use. Tools and frameworks (think Auto-GPT-style agents, vendor copilots, and agent builders from major cloud providers) now make it easier to connect AI to your CRM,

AI trend: Why hybrid AI (local + cloud LLMs) is the next big move for businesses Enterprises are moving from “cloud‑only” AI to hybrid deployments that combine local (on‑prem or private cloud) large language models with public cloud models and retrieval‑augmented generation (RAG). The goal: keep sensitive data in your control, cut latency for real‑time

AI agents — goal-driven, autonomous assistants powered by large language models — are moving fast from labs into the enterprise. Over the past year, major vendors and open-source frameworks have made it easier to build agents that can run tasks, talk to systems (CRM, ERP, databases), and make decisions with little human supervision. That means

Quick take: A growing number of companies are combining large language models (LLMs) with vector databases and retrieval-augmented generation (RAG) to deliver accurate, context-aware answers from their own data. Instead of feeding everything to an LLM and hoping for the best, businesses index documents, convert them into vector embeddings, and fetch the most relevant passages