Ron Mitchell

Short summary Big AI vendors (OpenAI, Microsoft, Google) have made it easy to build and deploy task-focused AI agents — small, autonomous assistants that can read your systems, run workflows, and generate business outputs (emails, summaries, reports). That means companies can automate more than one-off prompts: agents can handle end-to-end work like personalized sales outreach, […]

Quick summary Over the past year we’ve seen a clear shift: AI agents — persistent, task-focused AI that can act across apps and data — are moving into production at enterprises. Major cloud providers and a wave of startups now offer managed agent platforms that can securely connect to CRMs, ERPs, ticketing systems, and business

Quick summary AI “agents” — small, goal-focused AI systems that act on your behalf — have moved from demos into real business pilots. Companies are using agents to draft personalized sales outreach, assemble regular performance reports from multiple data sources, and automate routine operations like order triage and contract review. Those pilots are showing real

Quick summary AI agents — autonomous, task-focused assistants that can research, act in apps, and generate reports — are no longer just experiments. Over the last year many organizations have started deploying agents in real business workflows: automating sales outreach, triaging support tickets, running inventory checks, and producing regular management reports. That shift is making

Why this story matters now AI agents—autonomous assistants that plan, act, and use tools—have moved from lab demos into real business workflows. Over the last year more vendor platforms, connector marketplaces, and orchestration tools have made it practical to link large language models to CRMs, ERPs, analytics, and email systems. That makes it easier for

AI agents — autonomous software that can read, act, and learn across tools — are no longer just a tech demo. More companies are using them to automate sales outreach, generate management reports, and handle repetitive customer requests. The result: faster decisions, lower costs, and teams that focus on higher-value work. Why this matters for

Quick summary Across industries we’re seeing a rapid shift from single-use AI tools to autonomous AI agents — systems that combine planning, tool use, and access to business data to complete tasks end‑to‑end. Companies are piloting agents for lead qualification, sales outreach, customer triage, invoice processing, and auto-generated reporting. Why this matters for business leaders

Quick summary AI agents — autonomous, app-connected assistants that can research leads, draft outreach, schedule meetings, and update CRMs — are moving from pilots into real business use. Instead of single-step tools (write an email, summarize a call), these agents chain actions across systems and can complete whole sales tasks end-to-end. Why this matters for

Short summary AI is moving from chatbots to autonomous, task-focused agents you can customize and connect to your systems. These agents (built with GPT-style models and low-code “agent builders”) can read your data, take multi-step actions, and generate ready-to-use outputs — for example, qualifying leads in your CRM, drafting personalized outreach, and creating automated sales

Short summary Over the last year we’ve moved from proof-of-concept chatbots to practical AI agents that can run parts of a business workflow end-to-end: drafting personalized outreach, routing support tickets, populating dashboards, and even triggering follow-up actions in your CRM. These agents combine large language models with data connectors, business rules, and simple automation to