AI Post

Quick summary There’s been a clear shift: AI agents — autonomous, task-focused AIs that can call apps, run processes, and follow multi-step workflows — are moving from R&D experiments into real business use. Over the last year we’ve seen lots of vendor and startup activity making these agents easier to connect to CRMs, calendars, and […]

The story (short) Over the past year we’ve moved from flashy demos to real-world deployments of autonomous AI agents — software that can research prospects, draft outreach, update CRMs, prepare weekly reports, and even schedule follow-ups with minimal human supervision. Companies of all sizes are running pilot projects that combine AI agents with existing tools

Quick summary Over the past two years we’ve moved from isolated generative AI experiments to practical, agent-style tools that can act on behalf of users: triage customer requests, generate and distribute sales outreach, and assemble executive reports from multiple data sources. Vendors have baked “Copilot” and agent frameworks into productivity suites and cloud platforms, and

Quick summary AI agents — autonomous software that connects to your apps (CRM, email, calendar, data warehouse) and completes tasks — have moved from lab experiments into real business use. Instead of one-off AI features, teams are deploying agents that qualify leads, draft personalized outreach, update records, and generate recurring reports without constant human supervision.

Quick summary AI agents — autonomous, goal-directed AI “helpers” that can act across apps, fetch data, and complete tasks — have moved from labs into everyday business tools. Major cloud vendors and open-source frameworks now make it practical to deploy agents that do real work: generate sales outreach, consolidate reporting, handle routine customer interactions, and

Quick summary AI agents—autonomous workflows that combine language models, data connectors, and action tools—are moving from experiments into real business use. Instead of a person running multiple apps and copy/paste, an agent can pull CRM data, qualify leads, send personalized outreach, and update your pipeline — or generate weekly performance reports with narrative insights and

Quick story AI “agents” have moved from lab experiments to real business tools. Modern agents can connect to CRMs, calendars, email, knowledge bases and data warehouses to complete multi-step tasks — for example, qualify a lead, create a personalized proposal, update the CRM, and schedule a follow-up — with minimal human handoff. Large vendors and

Recent story (short summary) AI “agents” — software that can take multi-step actions across apps and systems on its own — have moved from demos into real business use. Instead of just answering questions, these agents can pull CRM data, draft personalized outreach, schedule meetings, and generate up-to-date sales and operations reports. Companies are starting

Quick summary AI agents — autonomous, task-focused systems powered by large language models — are moving from experiments to real business tools. When connected to company data (sales CRM, knowledge bases, ERP), these agents can draft proposals, generate up-to-date reports, triage customer requests, and automate repetitive sales and operations tasks with minimal human oversight. Why

Quick summary AI is shifting from chat-based assistants to autonomous, task-focused AI agents. These agents can take multi-step actions — e.g., research leads, draft personalized outreach, update CRM records, generate weekly sales reports, and even trigger follow-up workflows — without a user typing every step. That capability is rapidly being adopted by teams in sales,