Ron Mitchell

Quick summary AI agents — software that can plan, act, and complete multi-step tasks on their own — have moved from experiments to practical tools for companies. Major cloud and AI providers now offer agent frameworks and integrations that make it easier to connect agents to CRMs, data warehouses, and workflow tools. That makes it […]

Quick summary AI “agents” — AI systems that can access your data, take actions, and follow multi-step instructions — have moved from labs into real business pilots. Companies are using agents to draft customer outreach, run cross-system reports, update CRMs, and automate routine approvals. These agents combine large language models with connectors to your tools

Hook: AI agents — autonomous, task-focused assistants that read data, take actions, and learn — are moving out of the lab and into day‑to‑day business operations. That shift matters for every leader who wants faster reporting, fewer manual tasks, and smarter sales motions. What’s happening – A new wave of “agent” tools and platforms is

Short summary AI agents — autonomous, task-focused AI that can act on your behalf — have moved from lab demos to real business use. Companies are now using agents to qualify leads, summarize meetings, automate routine workflows, and generate recurring reports. That shift matters because these agents can cut manual work, speed response times, and

Quick summary AI agents — software that can act autonomously to complete multi-step tasks — have moved from lab demos into everyday business use. Companies are now using agents to qualify leads, automate follow-ups, generate consolidated reports, reconcile data across systems, and even coordinate approvals between teams. These agents combine language models, connectors to CRMs

Quick summary AI agents — software that can act on your behalf (query systems, pull data, write reports, and trigger tasks) — have gone from experimental to business-ready. This year more companies are putting agents into production: connecting them to CRMs, ERPs, knowledge bases and automation tools so agents can handle routine sales work, generate

Short summary AI agents — autonomous or semi-autonomous AI “assistants” that connect to your data and tools — are moving from experiments into everyday business use. Platforms like OpenAI, Microsoft, and the ecosystem of tools (Zapier, LangChain, RPA vendors) have made it easier to build agents that can qualify leads, generate reports, triage support tickets,

Quick summary AI agents — autonomous, task-focused AI that can read your data, take actions, and coordinate tools — are moving from experiments into real business use. Companies are using them to qualify leads, update CRMs, automate replenishment, and generate management reports without manual data wrangling. The result: faster processes, fewer errors, and more time

Quick summary AI agents—software that can act on your behalf across apps, data sources, and workflows—have moved from experiments to real business use. Modern agents combine large language models with retrieval (RAG), connectors to CRMs and BI tools, and low-code automation so they can, for example, clean CRM data, generate weekly sales reports, draft personalized

Summary AI agents — software that understands instructions, connects to systems, and takes actions — are maturing fast. Where they used to be experimental demos, teams are now using agents to qualify leads, draft personalized outreach, automate order processing, and generate on-demand business reports. The result: faster workflows, fewer manual steps, and more scalable personalization