Ron Mitchell

Quick summary In the last 18–24 months businesses have moved from experimenting with chatbots to deploying autonomous AI agents that handle real sales and operations tasks: qualifying leads, drafting personalized outreach, scheduling follow-ups, and auto-generating pipeline and executive reports. These agents combine large language models, CRM data, and automation workflows to act on behalf of […]

Summary AI agents — autonomous, tool-enabled assistants built on large language models — are moving from labs into everyday business use. Companies can now deploy agents that connect to CRMs, calendars, ad platforms, and data warehouses to run tasks end-to-end: qualify leads, book demos, generate weekly performance reports with narrative insights, and even trigger follow-up

Quick story Autonomous AI agents — small programs that use large language models plus connectors to act inside apps — moved from experiments into real business use in the past 12–18 months. Instead of just answering questions, these agents can gather CRM data, draft emails, trigger workflows, and generate routine reports without constant human prompting.

Quick story AI “agents” — autonomous software that can read your systems, take actions, and learn from results — have moved out of labs and into real business workflows. Companies are using agents to draft and send personalized sales outreach, update CRMs, build narrative sales reports, and handle routine approvals without constant human hand‑holding. The

Quick summary AI “agents” — autonomous AI that can run tasks, call tools, and carry out workflows — moved from experiments into real business pilots in 2024. Major platforms and vendors added agent capabilities, and teams are now using them for repetitive tasks like lead qualification, calendar management, invoice reconciliation, and automated reporting. The practical

Why AI agents are becoming business staples — practical steps for sales, reporting, and automation Quick summary Over the last 18 months we’ve moved from chatbots to capable AI agents: systems that combine large language models with retrieval (your company data), task automation (APIs, RPA), and long-term memory to perform multi-step work autonomously. Major vendors

Brief story summary AI “agents” — autonomous workflows that combine language models, data retrieval, and app actions — have moved from demos into real business pilots. Over the last year we’ve seen companies replace manual steps (like compiling reports, triaging leads, and completing routine support tasks) with agent-driven processes that query internal data, run analytics,

Short summary AI “agents” — small, goal-driven AI systems that act on your behalf — are moving out of labs and into everyday business workflows. Today they can draft outreach, schedule meetings, update CRMs, pull data from internal sources, and generate near-real-time reports. Vendors and startups are delivering agent frameworks and connectors that make these

Summary Big vendors and startups are turning generative models into autonomous “AI agents” that can act on behalf of humans — connecting to CRM, calendars, email, knowledge bases and reporting tools to complete multi-step tasks. Instead of just generating text, these agents can qualify leads, book meetings, update records, and push summarized insights into dashboards.

Quick story AI agents — software that can plan, act, and complete multi-step tasks with little human direction — have moved from demos to real business use this year. Companies are now using agents to run outreach sequences, qualify leads, prepare weekly reports, and automate back-office processes. Tooling (agent frameworks, low-code orchestration, and secure connectors)