Ron Mitchell

Quick summary AI agents — autonomous, goal-driven software that can read, decide, and act across apps — are moving from demos into real business use. Companies are using them to draft outbound emails, qualify leads, update CRMs, generate executive reports, and automate routine workflows. But the difference between a failed pilot and measurable ROI comes […]

Quick summary AI agents — autonomous, task-focused systems built on large language models — have moved from lab demos to real-world business use. Companies are now connecting agents to CRMs, email, calendars, and reporting systems so they can do things like qualify leads, draft customer replies, run sales cadence steps, and generate executive reports without

Quick summary Over the last year the conversation has shifted from “what can generative AI do?” to “what will autonomous AI agents do for my business?” Major platforms and enterprise apps are embedding agent-style assistants and copilots into workflows — automating tasks, writing outreach, generating reports, and triggering downstream processes. That means AI is not

Short summary AI “agents” — autonomous assistants that chain tasks, fetch data, and act on workflows — are moving from experiments to business tools. Thanks to custom-model toolkits and retrieval-augmented systems, companies are now using agents to qualify leads, update CRMs, generate on-demand sales reports, and run recurring operational checks without manual handoffs. Why this

Quick summary – What happened: Autonomous AI agents — models that can take multi-step actions (schedule, summarize, triage, update systems) — have matured fast. Companies are now turning them into live business tools: automating routine workflows, producing faster sales and financial reports, and handling first-line customer interactions. – Why it matters: This isn’t just a

Story summary Over the last year, AI “agents” — autonomous systems that can run tasks, talk to apps, and make decisions — moved out of labs and into real business pilots. Major vendors offered easier ways to build custom agents (for example, custom GPTs and enterprise copilots), and companies started using them for sales outreach,

The story (short) In the last couple of years, “AI agents” — LLM-powered bots that can use tools, access company data, and carry out multi-step tasks — have moved from demos into real business use. Major platforms now make it easier to connect models to calendars, CRMs, databases and APIs, and early adopters report real

Quick summary AI agents — autonomous, tool-using AI that can perform multi-step tasks — are no longer just demos. Over the past year vendors and startups have focused on agent frameworks, easier integrations with CRMs and BI tools, and better retrieval (RAG + vector DBs). That combo makes it simple to build agents that qualify

Summary AI agents — software that can plan, act, and use tools on your behalf — moved from experiment to practical in 2024 and are now showing up in real business workflows. Companies are using agents to automate outreach, summarize and file meeting notes, generate sales playbooks, and run routine reporting. The result: faster execution,

Short summary Major AI vendors and startups are moving from chat-only models to agent-style platforms — persistent, task-focused AI that can carry out workflows, access your systems, and update records. These “AI agents” can do things like draft outreach, run monthly reports, reconcile data, and follow rules without a human prompting every step. Why it