Ron Mitchell

Story (quick summary) There’s a clear trend: AI agents — autonomous, conversational workflows built on large language models plus secure company data — are moving from pilots into day-to-day business use. Vendors and open-source frameworks now make it easy to connect agents to CRMs, knowledge bases, calendars, and reporting systems so the agent can research […]

Summary AI agents — software that can read, decide, act, and connect to your systems — are no longer experimental toys. Over the last 18 months we’ve seen companies move from simple chatbots to agentic workflows that log into CRMs, pull data from ERPs, generate proposals, and even run parts of the sales process autonomously.

AI story in brief AI “agents” — autonomous workflows powered by large language models that act across apps and data — have moved from experimental demos into real business use. Vendors and startups are embedding agents into CRMs, scheduling tools, and reporting platforms so the tech can do tasks like qualify leads, draft personalized outreach,

Quick summary AI “agents” — software that can carry out multi-step tasks (think: research leads, draft outreach, update CRMs, and generate reports without constant human prompts) — are moving from experiments to real business use. These agents combine large language models with data connectors, retrieval systems, and simple automation to act semi-autonomously on routine workflows.

Summary AI “agents” — autonomous, workflow-oriented AI that can read your data, take actions, and talk to other systems — are no longer a lab experiment. Over the past year we’ve seen major cloud vendors and startups productize agent tools and “Copilot”-style assistants that automate tasks like sales outreach, report generation, invoice reconciliation, and customer

Summary AI “agents” — autonomous, task-focused assistants built on large language models — have accelerated from labs into real business workflows. Today they’re being embedded in CRMs, scheduling tools, and reporting platforms to handle tasks like lead research, outbound sequencing, meeting prep, and automated dashboards. Vendors and low‑code builders are shipping agent templates that connect

Summary AI agents — autonomous, task-focused assistants that act across apps and data — are no longer just prototypes. Enterprises are increasingly deploying them to handle sales outreach, customer triage, reporting updates, and routine process work. These agents combine large language models, connectors to business systems, and simple decision rules to do real work with

Quick summary Major vendors (Microsoft, Salesforce, Google) and many fast-moving companies are embedding AI agents and “copilot” features directly into CRM, reporting tools, and workflow apps. Instead of just generating text, these agents are starting, executing, and closing repetitive tasks — like triaging leads, drafting personalized outreach, and auto-generating weekly sales reports — by connecting

Quick take AI agents — autonomous, task-focused AI that can run workflows, qualify leads, or generate reports — have moved rapidly from proofs-of-concept into real, revenue-driving production deployments. Companies are using them to automate sales outreach, qualify leads in CRM, and produce near-real-time business reporting. That shift matters because it turns AI from a nice-to-have

Quick summary AI agents — autonomous software that can take actions, not just answer questions — have moved from demos into real business use. At the same time, AI-powered reporting is changing how teams read data: instead of manual dashboards and weekly slide decks, leaders are getting narrative insights, anomaly alerts, and suggested actions automatically.