Ron Mitchell

Summary AI agents — think persistent, task-focused AI that can read your data, take actions, and follow up — are now practical for everyday business use. Major platforms and toolkits have made it easier to connect agents to email, CRM, BI tools, and file stores. That means tasks like lead triage, follow-up emails, and weekly […]

Quick summary AI agents — software that can carry out multi-step tasks (research, send emails, update systems, generate reports) — have crossed a key threshold. What used to be one-off pilots is now being embedded into day-to-day workflows: sales outreach that researches prospects and creates personalized sequences, finance bots that reconcile invoices and flag exceptions,

Quick story AI is moving from single-answer chatbots to “agents” that can act on your behalf — search your documents, run queries, update systems, and generate regular reports. Combine that with retrieval-augmented generation (RAG) — a way to let models use your own internal files and databases — and you get copilots that actually complete

Quick summary AI “agents” — autonomous workflows powered by large language models — have moved out of the lab and into real business use. Instead of one-off chat responses, these agents chain tasks: they gather data, call internal systems (CRM, ticketing, spreadsheets), generate actions (send outreach, update records), and create final deliverables (reports, summaries, tickets).

Quick summary Major AI platforms and low-code builders have moved autonomous “AI agents” from demos into real business use. These agents can now connect to CRMs, email, calendars, Slack and BI tools to run multi-step tasks — for example: find high-priority leads, draft outreach, schedule follow-ups, and update pipeline reports automatically. Why this matters for

Quick summary AI “agents” — autonomous software that can read your CRM, draft emails, schedule meetings, and pull together reports — have crossed a tipping point. What used to be experimental scripts and proof-of-concepts is now being embedded into mainstream sales and operations workflows. Vendors and startups are shipping connectors to CRMs, BI tools, calendars,

Quick summary AI agents — software that can take multi-step actions across systems, learn from data, and follow simple goals — have moved from experiments to real business use. Companies are now using agents to automate sales outreach, generate operational reports, triage customer issues, and run routine back-office processes without constant human supervision. Why this

AI agents are moving from R&D into sales operations — what leaders should do next Summary Over the past year, AI agents — autonomous software that can carry out multi-step tasks (think: research a lead, draft outreach, schedule a meeting) — have moved from hobby projects to real business tools. Major vendors and a growing

Quick summary – What’s new: Over the past year autonomous AI agents and agent orchestration platforms have moved from lab experiments to real, usable business tools. Vendors and open frameworks now make it easier to connect agents to your data, CRM, calendars, and reporting systems. – Why it matters: These agents can perform multi-step workflows

Quick summary AI agents — autonomous, task-focused AI that can read your systems, take actions, and produce reports — are increasingly being used beyond labs and proofs-of-concept. Companies are wiring agents into CRMs, help desks, finance systems, and reporting pipelines so the AI can gather data, draft emails, run queries, and even trigger routine actions