Ron Mitchell

Quick story AI “agents” — autonomous, multi-step tools that can read your systems, take actions, and learn from feedback — have moved from experiments into real business use. Companies are now using these agents to qualify leads, update CRMs, draft personalized outreach, generate recurring reports, and even handle routine contract reviews. The result: faster responses, […]

Quick summary AI “agents” — autonomous, connected AI workflows that can read your systems, take actions, and learn from results — are no longer just lab experiments. Major vendors and startups are embedding agents into CRMs, helpdesks, and analytics tools so they can draft outreach, update records, run follow-ups, and even generate weekly performance reports

Quick summary There’s a clear shift underway: conversational AI agents (LLM-powered assistants) are being embedded into CRMs, analytics tools, and RPA platforms so they can run real workflows — not just answer questions. Instead of a person pulling data, copying into spreadsheets, and drafting an email, an AI agent can gather the numbers, generate a

Short summary AI “agents” — AI systems that can perform multi-step tasks, make decisions, and call other tools — are no longer just lab experiments. More companies are using them to automate sales outreach, generate regular business reports, triage customer support, and execute repeatable operational work. The result: faster cycles, fewer manual handoffs, and clearer,

Quick summary The European Union’s AI Act (finalized in 2024) is the first major, comprehensive regulation that classifies AI systems by risk and sets specific rules for “high‑risk” uses. Businesses that deploy AI agents, automation workflows, or AI-powered reporting — especially if they operate in or serve customers in the EU — now face new

Short summary AI agents — autonomous, goal-oriented AI that can access systems, run multi-step workflows, and learn from data — are no longer just lab experiments. Businesses are increasingly using them to handle repetitive sales tasks (outreach, meeting scheduling, CRM updates), automate reporting, and run routine operations. The result: faster response times, fewer human errors,

Quick summary Autonomous AI agents — software that can act, decide, and execute tasks with minimal human input — are quickly becoming practical for everyday business work: automating routine processes, preparing sales and financial reports, triaging customer requests, and running outreach campaigns. Instead of just suggesting actions, these agents can read your data, run workflows,

Story summary AI “agents” — small software assistants that can read documents, update systems, and take multi-step actions — are no longer just research demos. Over the last year we’ve seen more companies put these agents into real work: triaging customer requests, qualifying sales leads, building weekly performance reports, and automating routine back-office tasks. Why

There’s been a noticeable shift in how companies use AI: autonomous “AI agents” are moving from demos to real work. These are AI systems that can research, take actions across apps, and complete multi-step tasks with little human prompting. Over the past year, vendors and startups have embedded agent features into products — not for

Big picture summary Autonomous AI agents — software that can autonomously research leads, draft outreach, update CRMs, and run follow-ups — are moving from labs into real sales teams. Vendors and startups now offer agent orchestration platforms that chain tasks (data enrichment → outreach → meeting scheduling → reporting). Early adopters report faster lead qualification,