Ron Mitchell

Short summary AI “agents” — autonomous AI workflows that fetch data, make decisions, and act across apps — moved from lab experiments to real business deployments in 2024. Tools and frameworks (think agent orchestration, retrieval-augmented generation, and connector libraries) make it easier to automate multi-step tasks: generating personalized sales outreach, producing up-to-date performance reports, or […]

Short summary AI agents — autonomous models that can perform sequences of tasks (like sourcing data, writing emails, running reports, and scheduling follow-ups) — have moved from demos into real-world business use. Over the last year many platforms and vendors have released agent frameworks and low-code tools that make it far easier for teams to

Quick summary AI agents — small, goal-directed AI programs that can act across apps and APIs — are shifting from experimental demos into real business use. Companies are using them to run end-to-end tasks like scheduling and follow-ups, triaging customer requests, compiling sales reports, and even handling parts of financial close. Vendors and cloud platforms

Quick summary AI “agents” — task-focused, autonomous AI that can read data, take actions, and talk to other systems — are now being used in real business workflows, not just labs. Teams are wiring agents into CRMs, calendars, and analytics systems so a single AI can qualify leads, book demos, update records, and produce accurate

Quick summary AI agents — autonomous, goal-driven software that can read systems, take actions, and deliver reports — have moved from prototypes to everyday business tools. Sales and operations teams are using them to triage leads, draft and send personalized outreach, update CRMs, and generate automated sales and pipeline reporting. Improvements in large language models,

AI story (short): Over the past year businesses have moved beyond experimenting with standalone chatbots or single-model proofs of concept. The real momentum now is behind AI agents — systems that combine large language models, retrieval (RAG), connectors to your apps, and simple orchestration so the AI can complete multi-step tasks (research, generate, act). Companies

Summary AI agents — software that can act on your behalf (researching, messaging, updating systems) — have moved fast from hobby projects to real business use. Over the past year we’ve seen more production-ready agent platforms, easier integrations with CRMs and databases, and stronger enterprise controls for security and compliance. That means companies can now

Summary AI “agents” — small, purpose-built virtual assistants that can act on your data and systems — moved from demos into everyday use in 2024. Big vendors (OpenAI, Microsoft, Google) made it easy to create custom agents (often called “GPTs,” Copilots, or autonomous agents) that can search company docs, draft outreach, run workflows, and generate

Quick summary – What’s happening: AI agents — software that plans and executes multi-step tasks (think: research prospects, draft outreach, update your CRM, and produce a sales report) — are now being deployed in real business workflows, not just labs. – Why it matters for business leaders: These agents cut manual work, speed sales cycles,

Quick summary AI agents — models that can take multi-step actions (researching leads, updating systems, creating reports, even making follow-up decisions) — are no longer just demos. Over the past year organizations have moved from experimenting to running production agents that handle real work: pulling data, filling CRM fields, generating sales decks, and kicking off