Ron Mitchell

What’s happening Autonomous AI agents — systems that carry out multi-step tasks by connecting to calendars, CRMs, email, and reporting tools — are moving into everyday business use. Companies are using them to qualify leads, draft personalized outreach, prepare proposals, and produce near-real-time sales and operational reports. The combination of private LLMs, vector search on […]

Quick summary AI agents — software that can plan, act, and connect multiple tools on their own — are no longer a niche experiment. Over the past year we’ve seen more companies move agent-driven workflows into everyday operations: automating sales outreach, running routine financial reconciliations, and generating near-real-time management reports. These agents link LLMs to

1‑line summary A new wave of practical AI agents — built with large language models, retrieval (RAG), and connectors to enterprise systems — is moving from labs into real-world sales, ops, and reporting workflows. That means smarter automation, faster reporting, and hands‑off task execution for routine but high‑impact work. Why this matters to business leaders

The story (short) AI “agents” — autonomous AI tools that can read your systems, take actions, and follow multi-step workflows — have moved from research demos to practical business tools. Over the past year we’ve seen a wave of platforms and frameworks that make it easier to connect language models to CRMs, ticketing systems, databases,

Quick summary AI “agents” — autonomous assistants that can read your apps, run workflows, and take actions (send emails, pull reports, update CRMs) — are no longer just demos. Over the past year organizations have started piloting agentic workflows to speed up sales outreach, automate customer triage, and produce near-real-time business reporting. These agents chain

Summary AI agents — autonomous workflows powered by large language models, connectors, and retrieval-augmented search — are rapidly moving out of pilots and into production. Businesses are using agents to qualify leads, automate follow-up, generate sales-ready briefs, and automate recurring reports. The result: faster response times, fewer manual handoffs, and more consistent customer engagement. Why

Quick summary Autonomous AI agents — small, task-focused software that can read your data, take actions (send emails, update CRMs, build reports), and learn from outcomes — have moved from research demos to real business tools. Low-code builders and prebuilt connectors mean teams can deploy agents that qualify leads, follow up with prospects, and generate

Quick summary – What’s happening: AI “agents” — task-focused, conversational AI that can act on your data and systems — are moving from demos into real business tools. Major vendors have already embedded agent-like assistants into CRMs and productivity apps to draft emails, update records, and generate reports automatically. – Why it matters: These agents

Summary AI agents and AI-powered reporting tools are no longer experimental. Leading vendors and enterprise teams are rolling out autonomous assistants that connect to CRM, calendar, email, and BI systems to perform tasks (like qualifying leads, scheduling follow-ups, and generating monthly dashboards) and produce natural‑language insights from your data. The result: faster decisions, fewer manual

The story (quick): Over the past 18 months we’ve moved past proofs-of-concept. Autonomous AI agents — tools that can research, act on data, and carry out multi-step tasks without constant human prompting — are being embedded into real business workflows: qualifying leads, drafting proposals, updating CRM records, and generating on-demand sales and performance reports. Commercial