Ron Mitchell

Quick summary AI agents — lightweight, task-focused AI programs that can read your data, take actions, and follow up — are moving from proof-of-concept projects into real business use. Over the past year we’ve seen cloud platforms and open-source frameworks make it easier to build agents that connect to internal systems (CRMs, calendars, BI tools) […]

Big-picture story Autonomous AI agents — systems that combine LLMs, tool integration, and retrieval-augmented generation (RAG) — have moved from demos into real business use. Companies are using agents to automate customer follow-up, generate and validate sales reports, triage support tickets, and run day-to-day workflows that used to require manual handoffs. The result: faster reporting,

What happened OpenAI rolled out “Custom GPTs” earlier this year — a no-code way to create branded AI agents that connect to your data and tools. Companies can now build chat assistants that remember context, use plugins or APIs, and pull answers from internal documents without heavy engineering. Why this matters for business – Faster,

Summary Major enterprise vendors are embedding AI agents — often called “copilots” — directly into CRM, productivity, and analytics tools. These agents can draft emails, update records, generate forecast reports, and trigger cross-system actions. What used to be experimental automation is now shipping as product features across Microsoft, Google, Salesforce and other platforms. Why this

Quick summary AI “agents” — autonomous, task-focused AI that can read, act, and integrate with apps — have moved from research demos to real business tools. Over the last couple of years we’ve seen vendors and startups turn agent ideas (AutoGPT-style systems + app connectors) into production-ready assistants that can draft outreach, qualify leads, update

Short summary AI agents — autonomous, task-focused AI that can read, act, and report across systems — went from proof-of-concept to production in 2024–25. Companies are now using agents to qualify leads, automate routine sales outreach, generate monthly P&L reports, and triage support tickets without constant human supervision. Why this matters for business leaders –

Quick summary AI “agents” — autonomous workflows that combine large language models, connectors, and business rules — are finally usable in day-to-day operations. Instead of one-off chat responses, these agents can qualify leads, update CRMs, run daily sales reports, triage support tickets, and trigger downstream processes without constant human direction. Why this matters for your

Quick summary AI “agents” — autonomous, multi-step AI assistants that can access systems, query data, and take actions — moved from lab experiments into real business tools this year. Major vendors (Microsoft Copilot Studio, Google Vertex AI Agents, and platform “GPTs”) are now offering low-code builders and connectors that let teams automate workflows across CRM,

AI story summary AI agents — lightweight, task-focused systems built on large models — are finally moving into everyday business use. Instead of just producing text, modern agents can pull data from your CRM, run queries against dashboards, schedule follow-ups, and draft personalized outreach — all with less manual work. Over the past year we’ve

Quick summary AI agents — purpose-built AI helpers that act on your behalf across apps, data, and workflows — are no longer just lab experiments. More companies are using agents for tasks like lead qualification, order management, and automated reporting. These agents can gather data, take routine actions (e.g., update CRM records, generate invoices), and