Ron Mitchell

Quick summary AI “agents” — autonomous, goal-driven apps that can read your systems, take actions, and deliver results — are leaving experiments and showing up inside real sales and operations workflows. Companies are using agents to qualify leads, update CRMs, run personalized outreach, and generate automated reports that used to take hours of human work. […]

Quick summary Companies are moving beyond single-purpose AI tools to autonomous AI agents — systems that can combine language models, retrieval (RAG), and task orchestration to handle multi-step work: gather data, analyze it, create a report, and follow up with stakeholders. This shift is showing up across sales, operations, finance, and customer success where teams

Quick summary AI agents — autonomous, task-focused systems built on large language models — are moving from experiments to business-grade tools. Organizations are using agents to automate repetitive sales tasks (outreach, qualification), create near-real-time AI-powered reporting, and run follow-up workflows that used to require human handoffs. The result: faster decision cycles, lower cost per sale,

Hook: Over the past year we’ve seen AI agents — software that can plan, act, and follow up without constant human prompts — move from experiments into real production for sales, operations, and customer support. What’s happening – Vendors and cloud platforms have released purpose-built agent tools that integrate with CRMs, ticketing systems, and data

Quick summary AI agents — autonomous or semi-autonomous AI assistants that can research, write, transact, and interact with apps — have shifted from experimental demos to practical deployments inside companies. Sales teams are using agents to qualify leads, automate follow-ups, and draft proposals. Operations teams use agents to run routine reports, reconcile data, and trigger

Quick summary AI agents — software that can act autonomously on your behalf (think: triaging emails, qualifying leads, updating CRMs, or generating reports) — are moving from experiments into real business use. Over the past year we’ve seen vendors and platforms make these agents easier to build, integrate, and govern. That means more companies are

Summary AI agents — autonomous software that can follow multi-step instructions, access internal systems, and act on behalf of users — are moving out of labs and into real business use. Instead of one-off queries to a chatbot, these agents can run a sales follow-up sequence, generate weekly performance reports, update CRM records, and trigger

Quick summary Big vendors and startups are pushing AI agents from lab demos into real business tools. These “digital workers” can read your CRM, generate sales reports, send follow-up emails, and trigger workflows across apps — often with little human typing. That shift is making AI agents a practical option for routine sales tasks, automated

Quick summary AI “agents” — autonomous software that can use tools, fetch data, and complete multi-step tasks — are moving from labs into real business use. Major AI platforms and startups have made agent frameworks easier to build and integrate, so companies can automate complex workflows (lead qualification, customer follow-up, routine reporting) without rewriting everything.

Quick summary AI agents — autonomous, workflow-capable versions of chatbots — have moved from demos into real business use. Instead of answering one question at a time, these agents can run multi-step tasks: pull CRM data, draft and send outreach, update records, and generate a sales or finance report automatically. That shift is making AI