Teams | Collaboration | Customer Service | Project Management

A workforce analytics MCP server lets AI assistants read and report on work data. See what it can reach, what it can't, and how it works.

Every team communicates more than it realises. There are weekly updates, process changes, onboarding guides, event invitations, policy reminders and project announcements. Most of these messages are written quickly and shared in chat channels or email, and many of them are skimmed rather than read. A well-chosen visual can change that. A clear diagram explains a new process faster than three paragraphs, and a friendly header makes a weekly update feel worth opening.

In 2025, companies spent a collective $581 billion on AI. The results were underwhelming: according to a study by MIT, 95% of Enterprise AI pilots showed zero impact on organization’s bottom lines. In short, the vast majority of AI pilots are unsuccessful. The reflex is to blame the models. The evidence points somewhere easier to control: context. Every answer AI produces is assembled from a context packet—the material used to respond to the presented prompt.

An agent pulls information from your other systems to finish a job, like gathering project updates and drafting a status report for someone to approve. Its permissions determine which actions it can take. This 10-minute guide goes over different types of agents and how they work. It also explores workplace examples and how agents support AI work management.

Every ticket your team closes today makes the next one a little easier. That's not a metaphor, it's literally how Dotty works. Dotty is the AI Teammate built into Teamwork Desk. It drafts replies from your help docs and resolved tickets, surfaces the ticket that already solved this exact problem, and summarizes hand-offs so context isn't lost. Your agents review and send: nothing goes out untouched. But the real story isn't any one of those skills. It's what they're building together.