Ron Mitchell

Quick take AI “agents” — autonomous or semi-autonomous assistants built on large language models — are moving from lab experiments into real commercial use. Companies are using them to qualify leads, automate follow-ups, generate management reports, and run routine processes. That shift matters because it can cut repetitive work, speed sales cycles, and make reporting […]

What’s happening Autonomous AI agents — software that can plan, act, and complete tasks with minimal human direction — are no longer just experiments. Over the last year we’ve seen more vendors and teams put agents into real business workflows: qualifying leads, drafting outreach, running daily sales reports, monitoring inventory, and handling routine approvals. Why

Summary AI “agents” — software that can act autonomously on behalf of a user (for example: triaging leads, drafting outreach, pulling and summarizing CRM data, or generating recurring reports) have moved from experiments to real business pilots. Over the past year we’ve seen more off-the-shelf agent frameworks and integrations into CRMs and reporting platforms, making

Quick summary AI agents — autonomous, task-focused AI that can read, act, and follow up across apps — are moving from demos to real business use. Companies are using agents to qualify leads, update CRMs, generate weekly sales reports, and trigger follow-up actions without manual handoffs. Advances in retrieval-augmented generation (RAG), vector search, and agent

Short summary AI-driven agents — software that can act on your behalf (draft outreach, book meetings, update CRMs, run reports) — are rapidly moving from experiments into real sales workflows. Companies are taking these agents beyond flashy demos and connecting them to CRMs, analytics tools, and knowledge bases so they can automate repetitive work, surface

Quick summary AI agents — autonomous, multi-step AI “workers” that can read, research, act, and report — have moved from demos to practical tools. Major platforms now offer agent builders and integrations that let businesses connect agents to CRMs, databases, calendars, and reporting systems. That means teams can automate complex tasks (lead qualification, meeting follow-ups,

Summary AI “agents” — systems that can use tools, fetch company data, and act on behalf of users — have moved quickly from lab demos to real business deployments. Companies are using agents to qualify leads, draft outreach, summarize meetings, auto-generate reports, and trigger workflow actions in CRMs and ERPs. Why this matters for business

Quick summary AI “agents” — software that can act autonomously across apps (schedule meetings, qualify leads, pull reports, update CRMs) — are moving from experiments to real business use. Companies are embedding agents into sales and operations to automate routine work, speed reporting, and surface faster insights for decision-makers. Why this matters for business –

Short summary AI agents — autonomous software that can read your systems, draft messages, run workflows, and generate reports — are no longer just R&D projects. Over the past year we’ve seen more companies put agents into production for sales outreach, pipeline management, customer service triage, and automated reporting. The result: faster decisions, fewer manual

Short summary AI “agents” — autonomous software that can read, act, and follow up across tools — have moved from research demos into real business use. Companies are now deploying agents to triage leads, run outreach sequences, generate weekly reports, and automate routine decisions. These agents combine large language models with connectors to CRM, email,