Ron Mitchell

Short summary AI agents — autonomous, task-oriented AI that can read your tools, take actions, and learn from feedback — have moved from demos to real business use. Over the past year the technology has matured: agents now connect to CRMs, calendars, email, and BI tools to run workflows like lead outreach, opportunity qualification, and […]

Quick summary – What’s happening: Over the last year we’ve seen a clear shift: autonomous AI agents and agent platforms are no longer just R&D demos. Companies are deploying agents to qualify leads, handle routine customer requests, automate procurement checks, and run daily reporting—often by combining agents with RPA, CRM systems, and data warehouses. –

Quick summary Over the past year organizations have shifted from piloting chatbots to deploying autonomous AI agents that can act — not just answer questions. These agents connect to calendars, CRMs, email, and reporting tools to qualify leads, draft personalized outreach, update records, and generate weekly performance reports without constant human direction. Early adopters report

Quick summary AI “agents” — autonomous workflows built from large language models plus connectors to your apps — have moved fast from tech demos into practical business use. Advances in agent frameworks (think LangChain-style orchestration), better retrieval from company data, and pre-built integrations with CRMs, ERPs and reporting tools mean agents can now research leads,

Quick summary AI agents — autonomous, workflow-focused AI that can act on your behalf (e.g., triage leads, update CRMs, run multi-step reports) — have moved from experiments into real business tools. Over the last year we’ve seen low-code builders, tighter integrations with CRMs and cloud apps, and more businesses pilot agents for follow-up, scheduling, and

Quick summary AI “agents” — autonomous, goal-driven tools that chain together LLMs, connectors, and business logic — have moved from proof-of-concept demos into real, repeatable business workflows. Tools and frameworks (think LangChain-style orchestration, low-code connectors, and platform-built agents in the Power Platform and other clouds) are making it easier to automate multi-step tasks: lead enrichment,

Short summary AI “agents” — autonomous workflows that combine large language models with tools, data access, and decision logic — have moved from lab demos into real business use. Companies are now using agents to automatically triage leads, generate and deliver sales outreach, refresh dashboards, run daily operational checks, and even handle parts of customer

Quick summary – The latest wave of AI tools isn’t just chatbots anymore — it’s autonomous AI agents that can carry out multi-step tasks: qualify leads, update CRMs, generate weekly sales reports, and even schedule follow-ups. – These agent platforms plug into your existing systems (CRM, calendar, BI) and combine automation with large language models

Summary AI “agents” — autonomous workflows built from large language models plus tools (connectors, search, and actions) — have moved from demos to real business use. Over the past year we’ve seen no‑code agent builders and enterprise copilots let teams create agents that browse internal docs, draft outreach, pull data from CRMs, and generate recurring

Short summary AI agents — autonomous, tool-using AI that can act on your behalf (think: read your calendar, pull CRM data, draft outreach, and execute follow-ups) — are no longer just demos. Over the last year we’ve seen platforms make it practical to connect these agents to internal systems, pipelines, and dashboards so they can