Ron Mitchell

Quick summary Major cloud providers (Microsoft, Google, Salesforce and others) are making it easier for companies to build custom AI agents — branded copilots that connect to your CRM, ERP, Slack, BI tools and internal data. These agents can draft emails, run sales playbooks, summarize meetings, and generate on-demand reports from your systems. Why this […]

Quick summary Autonomous AI agents — software that can plan, act, and integrate with other systems on their own — have moved from demos to real business pilots in the past year. Instead of only answering questions, these agents can schedule follow-ups, pull CRM data, generate and send proposals, and create recurring reports with minimal

Story summary AI agents — autonomous, multi-step AI assistants that can act across apps and data — have moved from research demos into real business pilots. Companies are using agents to qualify leads, pull together complex reports, automate routine operational tasks, and even manage multi-step customer journeys. The result: faster response times, fewer manual handoffs,

What’s happening – Over the last year, AI agents — autonomous assistants that connect to email, CRM, calendars, and business apps — went from demos to practical tools in many companies. – These agents can draft outreach, update records, run pipeline health checks, summarize meetings, and assemble executive reports automatically. – Improvements in large language

Quick summary – Over the last 12–18 months we’ve seen a fast shift: “AI agents” (autonomous, task-oriented AI workflows) and custom GPT-style assistants are moving from demos into real business use. – Tools like Microsoft Copilot, OpenAI’s customizable GPTs, and open-source agent frameworks make it easy to automate repetitive work: sales outreach, meeting summaries, data

Quick summary – What’s happening: In the past 18–24 months we’ve moved from one-off AI pilots to practical, repeatable “AI agents” that connect to CRMs, ERPs, and BI tools. Major vendors (eg, enterprise copilots) plus many startups now let agents run simple workflows — qualify leads, generate reports, triage support, and trigger actions — with

Summary AI “agents” — small autonomous systems that read, decide, act, and communicate across apps — have left the lab and are now practical for everyday business work. Over the past year we’ve seen more enterprise-ready agent frameworks, low-code connectors, and secure orchestration tools that let companies automate routine decisions and multi-step tasks (for example:

Quick summary AI agents — small, task-focused AI programs you can train or configure without writing code — have moved from experiment to everyday tools. Over the past year, major AI platforms and new marketplaces have made it easy for teams to create agents that do things like qualify leads, run reporting workflows, triage customer

What happened (short summary) – Over the past year businesses have shifted from experimenting with single-turn chatbots to deploying autonomous AI agents — models that take multi-step actions (research, create drafts, run queries, call APIs) with minimal human prompting. – Major platforms and open-source toolkits made this easier: agent frameworks, connectors to CRMs and data

Quick summary AI agents — purpose-built, multi-step AI systems that can take actions, access company data, and complete tasks end-to-end — are no longer just demos. Over the last year we’ve seen a wave of real-world deployments where agents automate sales outreach, produce recurring analytics reports, coordinate scheduling, and trigger workflows across CRMs, ticketing systems,