Ron Mitchell

Short summary AI “agents” — customizable assistants that can read your documents, query internal systems, and take actions across apps — are moving from research demos into everyday business use. Major AI platforms now let companies build private agents that connect to CRMs, knowledge bases, and reporting tools. That means teams can automate repetitive work […]

Short summary Autonomous AI agents — smart assistants that can act across apps, make decisions, and complete multi-step tasks — have moved from experiments to practical tools for business. Companies are using agents to automate sales outreach, update CRMs, generate recurring reports, and orchestrate cross-team workflows. This shift lowers manual work, speeds decision-making, and scales

Quick summary AI agents—autonomous, goal-driven software that can read documents, query systems, and take actions—have moved from labs into real business workflows. Teams are using them to draft personalized sales outreach, generate recurring reports with narrative insights, and automate multi-step processes that used to bounce between tools and people. Why this matters for your business

Quick summary AI has moved from “experimental” to “practical.” Over the past year we’ve seen model makers, platforms, and low-code builders deliver tools that let companies create purpose-built AI agents — systems that can access your data, run workflows, and act on behalf of teams. That shift makes it realistic for businesses to automate sales

Summary AI agents — autonomous systems that can run tasks, fetch data, and take multiple steps to complete workflows — have moved from experiments to practical tools for businesses. Over the last 18 months vendors and open-source projects have made it easier to connect LLMs to CRMs, BI tools, calendars, and internal knowledge bases. That

What’s happening More companies are moving from experiments in generative AI to deploying autonomous AI agents that do real work — qualify leads, update CRMs, draft personalized outreach, and generate routine performance reports. These agents combine language models, connectors (to email, calendar, CRM), and simple business rules so they can act on behalf of teams

Quick summary AI agents — systems that combine LLMs with tools, automation flows, and data connectors — are moving from experiments into everyday business use. Think of them as digital teammates that can research prospects, draft and send outreach, update CRMs, generate reports, or orchestrate multi-step processes without constant human hand-holding. Why this matters for

Short summary AI agents — software that can plan, act, and complete multi-step tasks on your behalf — are moving from labs into real business use. Over the past year we’ve seen cloud providers and startups ship agent frameworks and connectors that let these systems read company data, run workflows, update CRMs, and generate reports

Quick summary AI agents — autonomous, task-focused software that can read, act, and follow up across apps — are moving from demos into real business use. Over the past year we’ve seen platforms and vendor-built agents that can qualify leads, summarize meetings, update CRMs, generate regular reports, and even trigger approvals across systems. That means

Summary AI agents—software that can act on your behalf, carry out tasks, and make decisions—are moving from research demos into everyday business use. Sales teams use agents to draft and personalize outreach, operations teams use them to automate follow-ups and invoices, and finance teams use them to auto-generate recurring reports. The result: faster work, fewer