Teams | Collaboration | Customer Service | Project Management

An employee wants Friday off. Another needs a payslip. A new hire is waiting for system access. Someone else has moved house and needs to update their details. None of them really wants to “use Workday.” They want the request resolved.

Hybrid teams run into a documentation problem that purely remote teams rarely face: meetings happen in two places at once. Some people are in the conference room. Others are on a screen. The person taking notes is trying to capture what the room is saying while also following what's being said on the call. Something always gets missed.

Every AI assistant or automation connected to a messaging platform needs a way to act on your behalf: sending a message, managing a channel, pulling context from a conversation. Rocket.Chat 8.8 gives agents a standard way to do that with a native MCP server, so any MCP-compatible agent can work inside your workspace without your team building and maintaining a custom integration for it. This release also brings tighter security and more reliable calling.

What an AI governance framework contains, how to build one in stages, and how to check whether the policy matches what people are actually doing.

Every missed business call represents an unanswered question, a frustrated customer, or a potential sale that may go elsewhere. Yet answering every call is difficult, especially for small teams. Employees may be helping customers, working on-site, attending meetings, or simply unavailable outside normal business hours. Technology can solve part of this problem, but businesses are often presented with two different categories of solutions: business phone systems and AI receptionists.

OpenAI's new Apple Messages plugin gives ChatGPT access to the Messages application on a Mac. Once enabled, users can ask ChatGPT to find conversations, summarize them, draft replies, and send messages. That has understandably raised questions about what it means for iMessage privacy. The starting point is understanding what the plugin does and, just as importantly, what it doesn't do.

When Jerry Weltsch, Head of Analyst Relations at Asana, receives a new industry report, he doesn't read it cover to cover. He assigns it to a custom AI research assistant to analyze. Jerry is responsible for managing relationships with industry analysts who specialize in software markets. That means constantly reading research, making sense of it, and communicating what it means for the business. Before customizing his teammate, Jerry used to process four or five analyst reports a week.

AI makes it quick and easy to turn rough notes into a clean, easy-to-read document. Job well done, right? Think again. Atlassian’s Teamwork Lab suspected that this professional veneer—what we call “AI polish”—makes it harder to spot foundational flaws and give feedback, so we tested it. We wanted to know: Does AI polish make it harder to identify problems in early drafts? How can we solve for this blind spot?