Ron Mitchell

Quick summary AI agents — goal-oriented, multi-step AI assistants that can act, decide, and trigger systems — are no longer just research demos. Companies are deploying them to run repeatable business work: qualify leads, route tickets, generate daily sales reports, and trigger follow-up actions in CRMs and ERPs. The payoff: faster decision cycles, lower manual […]

Summary AI agents — autonomous software that can read, decide, and act across apps — have moved from experiments into real business use. Instead of one-off chatbots, companies are building agents that qualify leads, schedule meetings, update CRMs, and generate consistent sales reports without constant human hand-holding. Why this matters for businesses – Faster, repeatable

Over the past year, AI agents and built-in generative AI features from major vendors have shifted from pilot projects to everyday business tools. CRMs, help desks, and reporting platforms now ship with AI agents that can draft personalized outreach, qualify leads, assemble monthly dashboards, and even take multi-step actions across apps. Why this matters for

Quick summary Autonomous AI agents — software that can plan, act, and carry out multi-step tasks with little human intervention — have moved from experiments into real business use. Teams are now using agents to qualify leads, run outreach sequences, reconcile invoices, and generate regular performance reports. The payoff is speed, scale, and more consistent

Summary Over the last year the conversation has moved from “what AI could do” to “what AI is actually doing” inside companies. Autonomous AI agents — tools that can act on your behalf across apps and data (think: orchestrating a sales outreach sequence, generating weekly performance reports, or triaging invoices) — have gone from demos

Quick summary AI agents — autonomous, multi-step AI assistants that can fetch data, run tasks, and take actions — moved from experiments into real business use in 2024. Platforms and frameworks (agent SDKs, function-calling APIs, & orchestration tools) make it easier to build agents that connect to CRMs, ERPs, databases, and reporting tools. The result:

Quick summary AI “agents” — autonomous workflows built on large language models — are no longer just demos. Over the past year, more companies have run pilots that let agents autonomously research prospects, draft outreach, summarize meetings, and generate regular sales and operational reports. The result: faster decision cycles, fewer manual tasks for teams, and

The story in one line AI agents — automated workflows powered by large language models, connectors, and simple business logic — are moving fast from experiments into real business use. They’re already handling tasks like lead qualification, follow-ups, and recurring performance reports, freeing teams to focus on higher-value work. Why this matters for your business

Quick summary AI “agents” — autonomous assistants that connect to your CRM, calendar, email, and reporting systems — have shifted from research demos to real-world enterprise pilots. Major cloud providers and tool vendors now offer agent builders and data connectors, and companies are using them to automate lead outreach, update sales systems, and generate regular

Quick story Over the past year major business apps and platform vendors have embedded generative AI into core workflows — turning simple assistants into autonomous AI agents that can act across your CRM, calendar, email and reporting systems. That means systems can now draft outreach, qualify leads, summarize meetings, update pipelines and generate executive reports