Ron Mitchell

Short summary AI agents — autonomous, multi-step AI helpers that can access your systems, run processes, and deliver results — are no longer just lab experiments. Over the last year we’ve seen a wave of tools and connectors that let these agents read CRM records, pull ERP data, generate reports, and trigger actions (like scheduling […]

Short summary Over the last year we’ve seen a sharp shift: companies are moving from experimenting with chatbots and reports to deploying autonomous AI agents that perform real, cross-system work — for example qualifying leads, routing orders, generating weekly sales reports, and opening support tickets. These agents combine large language models with connectors to CRMs,

Quick summary In the last year we’ve moved past “chatbot demos” to practical AI agents: LLM-powered systems that can take multi-step actions across apps (schedule meetings, pull CRM data, update records, generate reports) instead of only answering questions. Major platforms and open-source frameworks have made it easier for businesses to build agents that connect to

Hook: AI agents — not just chatbots — are moving from experiments into everyday business work: automating cross-system tasks, creating real-time sales reporting, and speeding decisions. That matters for every leader who wants fewer manual handoffs and clearer revenue signals. The story (short) – Over the past year, AI “agents” — autonomous LLM-powered programs that

What happened AI agents — autonomous software that can research, draft, action, and report on tasks — are no longer just lab experiments. Advances in large models, connector ecosystems (CRMs, calendars, data warehouses), and cheaper compute mean teams can now deploy agents for real business work: prospect research, personalized outreach, pipeline scoring, recurring reporting, and

Summary Major cloud and software vendors are rolling out tools that let companies build custom “copilots” — AI agents trained on your documents, CRM, and apps to do real work. These copilots can answer team questions, generate tailored reports, automate routine tasks, and act as an on-demand expert inside familiar tools like email, chat, or

Quick summary AI agents — small, goal-driven systems built on large language models — have shifted from research demos to practical business tools. Instead of one-off chat responses, modern agents can connect to your calendar, CRM, email, databases, and apps to complete multi-step tasks: qualify leads, book meetings, triage support tickets, and generate regular reports

Summary AI “agents” — software that acts on your behalf across apps (CRM, email, calendars, reporting tools) — are no longer just experiments. Over the past year, more companies have started using agents to automate multi-step workflows: qualifying leads, routing opportunities, scheduling demos, and producing regular sales and operational reports. These agents combine large language

Quick summary AI “agents” — autonomous, task-focused systems built on large language and multimodal models — are moving out of demos and into real business workflows. These agents can connect to calendars, CRM, spreadsheets, email, and APIs to complete multi-step jobs: draft and send outreach, book meetings, update records, pull data and generate reports, or

Quick summary AI agents — autonomous, task-focused assistants built on large language models — are moving from experiments to business tools. They can read emails, pull data from CRMs, create customer outreach, run routine reports, and even trigger downstream workflows without constant human direction. The result: faster decisions, fewer manual steps, and clearer, on-demand insights.