Ron Mitchell

Step 1 — The story in brief Major platform vendors and enterprises are shifting from pilots to production use of AI agents and embedded “Copilot” tools. New, faster models and easier integrations (think built-in copilots in productivity suites and low-code agent platforms) are making it practical to automate everyday workflows: customer triage, lead follow-up, monthly […]

Quick summary AI agents — purpose-built AI that can act, fetch data, and run multi-step tasks across systems — are no longer just experiments. Over the past year businesses have started using agentic workflows to automate sales tasks, generate near real-time reports, and orchestrate processes across CRM, ERP, and BI tools. The result: faster decisions,

Quick summary AI “agents” — small, goal-focused AI systems that combine language models with tools (calendars, CRMs, databases, connectors) — are no longer just a developer curiosity. By 2024 many companies moved beyond single-answer chatbots to multi-step agents that can research a topic, update records, draft outreach, and generate reports automatically. Why this matters for

Summary The past year has accelerated a clear trend: AI agents and enterprise “copilots” are moving from experiments into everyday business work. These are AI systems that can act (with supervision) across apps — drafting emails, generating pipeline reports, updating CRMs, routing tasks, and even initiating low-risk transactions. Major AI platforms now make building or

Quick summary AI agents — software that can take guided actions, hold complex conversations, and chain tasks across tools — are no longer experimental. Over the last year we’ve seen major vendors and startups push enterprise-ready agents that connect to CRM, calendar, email, and reporting systems. That means these tools can now do real work:

Summary AI “agents” — systems that can carry out multi-step tasks (think: draft outreach, update CRM, pull a weekly sales report) — are no longer just a research demo. Over the last couple of years companies have started wiring large language models to real business systems (CRMs, ERPs, analytics tools) and using retrieval-augmented generation, function

Short summary Big software vendors (Microsoft, Google, Salesforce and others) are embedding AI agents — smart assistants that can act on your data, run workflows, and talk to other apps — directly into CRM, productivity, and cloud tools. Those agents are moving beyond chat: they can qualify leads, update records, assemble reports, and trigger workflows

AI agents are moving from “nice to have” to business-critical — here’s how to start Story summary AI agents — autonomous assistants that read data, take actions, and collaborate with people and systems — have crossed an inflection point. Over the last 18–24 months vendor platforms, open-source frameworks, and enterprise connectors have matured enough that

Big picture (the story) AI “agents” — autonomous, goal-directed AI workflows that combine language models, APIs, and data — have moved from experiments into practical business use. Companies are using agents to qualify leads, automate routine customer service tasks, generate sales reports, and run recurring operational checks. Instead of a person manually copying, querying, and

Quick summary AI “agents” — autonomous AI assistants that can carry out tasks, talk to systems, and make decisions — are moving from demos into real business work. Instead of just answering questions, modern agents can pull CRM data, generate sales outreach, run financial reports, and trigger downstream workflows automatically. That shift is making AI