Ron Mitchell

Quick summary AI agents — autonomous, task-focused systems that can read, act, and communicate across apps — have moved from experiments to practical tools for businesses. Low-code agent builders and tighter integrations with CRMs, ERPs, and BI platforms mean companies can automate routine workflows, generate on-demand reports, and surface insights without a team of data […]

Quick summary – Autonomous AI agents — systems that can carry out multi-step tasks (e.g., qualify leads, update records, generate reports) without constant human prompting — stopped being a novelty and started showing real ROI across sales, operations, and finance. – Organizations are using agents for lead qualification, 24/7 customer triage, automated reporting, and routine

Quick summary AI “agents” — custom, task-focused assistants powered by large language models and connected plugins — have moved from experiment to practical tool for businesses. Platform updates over the last year have made it easier to build agents that safely access company data (CRMs, BI tools, calendars, ticketing systems) and run repeatable tasks: generate

Quick summary AI agents — software that uses large language models to take multi-step actions, connect to your apps, and run tasks autonomously — are no longer just demos and research projects. Over the last year we’ve seen platforms and low‑code tools make agents easier to build, plus tighter integrations with CRMs, BI tools, and

Short summary AI agents — purpose-built, conversational systems that can access your data, run workflows, and act on behalf of users — are moving from research demos into everyday business use. Cloud vendors and startup tools now make it much easier to create custom agents that connect to CRMs, ERPs, ticketing systems, and internal docs.

Summary AI agents — software that uses large language models to read, act, and coordinate across systems — have moved from experiments into real business use. Over the past year we’ve seen platforms and vendor tools that make it far easier to spin up an agent that can personalize sales outreach, assemble weekly revenue reports,

AI story summary AI agents — autonomous workflows that use large language models to read, decide, and act — have moved fast from lab demos into real business work. Organizations now use agents to handle routine sales follow-ups, assemble weekly performance reports, triage support tickets, and automate accounts-payable checks. The combination of better models, integrations

Short summary AI “agents” — autonomous, multi-step AI workflows that can read documents, call APIs, and take actions — are moving from labs into real business use. Companies are already using agents to draft and send personalized outreach, triage customer issues, auto-build weekly performance reports, and update CRMs without manual copy/paste. Why this matters for

Short summary AI “agents” — AI systems that act autonomously across apps, fetch data, call tools, and complete workflows — have moved from labs into real business use. Advances in models, tool integrations, and retrieval-augmented workflows mean these agents can do repeatable tasks like qualifying leads, drafting personalized outreach, triaging support tickets, and building reports

Summary Over the past year we’ve seen a clear shift: autonomous AI agents are moving out of experimentation and into real business impact. Companies are using agents to run repeatable workflows — from personalized sales outreach to automated month-end reporting — and getting measurable time and cost savings. Improvements in models, integrations (APIs, CRM, data