Ron Mitchell

Step 1 — The story in one line Autonomous AI agents—small, task-focused AI systems that can act, learn, and coordinate—have moved from experiments to real business pilots. Companies are now using them to qualify leads, automate reporting, and run routine operational tasks. Step 2 — What this means for business Why it matters: AI agents […]

AI agents are moving into the enterprise — what business leaders should do next Opening (hook) AI agents — autonomous, workflow-focused AI that can read, act, and integrate across tools — are no longer just demos. Businesses are starting to use them in sales, operations, and reporting to reduce manual work and speed decisions. If

Quick summary AI agents — autonomous, task-focused AI that can carry out multi-step work (think: draft an email, pull sales data, update the CRM, and create a report) — moved from lab demos to real business pilots in 2023–2024. Improvements in LLMs, function-calling/plugins, and retrieval-augmented generation (RAG) make agents more reliable and easier to connect

Summary A growing wave of businesses are deploying AI agents — software that combines large language models, retrieval systems, and automated actions — to run routine workflows end-to-end. Instead of just generating text, these agents can read your CRM, pull data from your warehouse, create a report, update records, and trigger follow-up tasks without constant

Short summary AI agents — autonomous, multi-step AI programs that read your data, call APIs, and take actions — are no longer just lab experiments. Over the past year more companies have started deploying agents for tasks like lead research, quote generation, automated reporting, and workflow handoffs. The result: faster sales cycles, fewer manual reports,

Short summary AI agents — autonomous software that completes tasks, makes decisions, and talks to other systems — have moved from lab demos into real business use. Over the last year we’ve seen companies deploy agents for lead qualification, dynamic pricing, invoice processing, and live operational reporting. These agents combine large language models, connectors to

Summary AI agents — systems that act on your behalf across apps (CRM, email, calendar, BI tools) — have moved from proofs-of-concept into real business use. Instead of giving a single answer, these agents can execute multi-step workflows: follow up with prospects, update records, generate weekly sales reports, or open and assign tickets. That means

Summary AI “agents” — software that can act autonomously to complete tasks by combining large models, retrieval, and workflow logic — are moving from R&D into everyday business use. Major cloud and AI providers now offer low‑code agent tools and copilots that let teams automate multi-step work: routing leads, drafting tailored outreach, summarizing meetings, updating

Quick summary AI agents — software that combines large language models with data connectors and automation — are no longer just experiments. Across sales, customer service, and operations, vendors and in-house teams are shipping agents that can qualify leads, generate personalized outreach, run routine workflows, and produce automated reports from live data. Why this matters

Quick summary AI “agents” — autonomous, workflow-focused AI assistants that can call systems, fetch data, and take multi-step actions — have moved from niche experiments into practical tools for businesses. Major platform vendors and startups have released toolkits and integrations that make building agents faster and cheaper. The result: companies are using agents to automate