Ron Mitchell

Summary Large language models and “AI agents” (autonomous assistants that can read, act, and connect to systems) have crossed a key threshold: companies are now deploying them beyond pilots into real, revenue-impacting workflows. From automatically triaging customer requests and qualifying sales leads to generating weekly financial reports, these agents are being used to handle repetitive […]

The story In 2025–26 we’ve seen a clear shift: AI agents — autonomous, task-focused assistants that can search systems, call APIs, and take multi-step actions — are moving out of lab experiments and into real business workflows. Companies are using agents for sales outreach, quoting and proposal assembly, customer support triage, and automated reporting. The

Short summary AI “agents” — software that can plan, act, and follow up on tasks with little human prompting — are no longer just experiments. Across industries, companies are piloting agents to automate routine work: personalized sales outreach, continuous reporting, order-tracking, appointment scheduling, and workflow orchestration. The result: faster response times, fewer manual steps, and

Quick summary – The latest shift in AI is the rise of autonomous “agents”: small AI assistants that can research, act across apps, and run multi-step workflows (think: pull CRM data, draft outreach, update records, and generate a report — without a person doing each step). – Over the past year vendors and platforms have

Quick summary AI “agents” — small, goal-driven AI programs that can run tasks, call apps, and make decisions — have moved from experiments into real business use. Instead of one-off chatbots, companies are building agents that autonomously gather data, create reports, route tasks, and even run multi-step sales outreach. These systems combine large language models

Short summary AI agents — autonomous, task-focused AI tools that can read, act, and learn from your systems — have moved from labs into real business use. Over the past year more companies have started using agents for lead qualification, routine customer replies, scheduling, and automated reporting. These agents combine generative AI with data access

Quick summary In the last 12–18 months the conversation around generative AI has shifted from one-off chat experiments to practical, autonomous “AI agents” that connect to real systems — CRMs, calendars, BI tools and document stores — and carry out business tasks on their own. Companies are using these agents to draft personalized outreach, run

Quick summary Autonomous AI agents — programs that can act, decide, and complete tasks with little human direction — are no longer a lab curiosity. Over the past 18–24 months the technology stack (agent frameworks, reliable APIs, better retrieval/knowledge grounding) has matured enough that companies are running pilots and putting agents into production for sales,

Summary AI “agents” — autonomous AI assistants that can read, act, and execute multi-step tasks — are no longer just experimental demos. Businesses are starting to deploy them for lead qualification, customer follow-ups, automated reporting, and routine process work (for example: triaging emails, creating weekly sales dashboards, or updating CRM records). These agents combine large

Quick summary Over the past year, autonomous AI agents—small systems that carry out tasks end-to-end (think: qualify a lead, create a tailored proposal, update your CRM, or build a weekly sales report)—have moved out of labs and into real business pilots. Better large language models, easier integration tools, and no-code “agent builders” mean these agents