Ron Mitchell

What’s happening Autonomous AI agents — think AutoGPT-style workflows, in-app copilots, and vendor-built “agents” inside CRMs and reporting tools — are no longer just experiments. Companies are using them to run repeatable tasks: draft outreach, stitch together data for reports, automate invoice checks, and surface sales leads. The result: faster workflows, fewer manual handoffs, and […]

Summary AI “agents” — autonomous AI that can carry out multi-step tasks, talk to your systems, and follow business rules — moved from demos into real pilots across sales, operations, and finance over the past year. Instead of a person running each step, agents can research, draft, update CRMs, trigger approvals, and deliver scheduled reports

Quick summary AI “agents” — autonomous workflows that read, decide, and act — have crossed a tipping point. What used to be experimental (proofs-of-concept, one-off automations) is now becoming repeatable, production-ready work in sales, operations, and reporting. Better language models, agent frameworks, and out-of-the-box integrations with CRMs and BI tools mean companies can deploy agents

Short summary Over the past year major enterprise platforms have moved from “AI features” to AI agents — systems that can act autonomously across apps to complete tasks, update records, and generate reports. That shift means businesses can now automate multi-step processes (think: qualifying leads, creating customer reports, or closing routine support tickets) with far

Big idea (the story) Autonomous AI agents — systems that can connect to your CRM, calendar, email, and reporting tools and then act on behalf of users — have moved beyond lab demos. In the last year more teams have deployed agents to qualify leads, schedule demos, run routine outreach, and generate real-time sales and

Short summary AI “agents” — autonomous AI programs that can act across apps, pull data, and complete tasks — are moving from labs into real business use. Instead of one-off chat answers, agents can read your CRM, draft outreach, book meetings, and generate reports with little human hand-holding. That makes them a practical tool for

Short summary This year, AI agents moved from demos into real business use. Unlike single-answer chatbots, these agents can connect to your apps, pull data, run tasks, and follow multi-step workflows — for example, qualify a lead, update the CRM, prepare a tailored proposal, and schedule follow-up all without a human doing every step. Why

Quick summary AI agents — software that can make decisions, take actions, and follow multi-step workflows with little human direction — are moving from pilot projects into real, money-saving business applications. Companies are using them today to auto-generate sales outreach, assemble finance dashboards, reconcile data across systems, and trigger operational actions (e.g., reorder stock, escalate

What’s happening Autonomous AI “agents” — software that can plan, act, and iterate with little human supervision — are moving from experiments into real business use. Teams are using agents to research prospects, draft and personalize outreach, reconcile invoices, monitor supply chains, and auto-generate executive reports. These agents combine large language models, connectors to your

Short summary AI agents — autonomous, goal-driven tools that can research, draft, schedule, and take multi-step actions — are no longer just experiments. Over the last 18–24 months they’ve moved into production at more companies. Sales and revenue teams are using agents to qualify leads, draft personalized outreach, schedule meetings, and update CRMs automatically. Operations