Teams | Collaboration | Customer Service | Project Management

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.

In September, our community published around 100 new templates: stakeholder maps and OKR trackers sat alongside accessibility checklists, strategic roadmaps, and a wave of templates landing in Spanish, Portuguese, and French. The throughline for the month was AI finding its way into everyday workflows – Miro Flows and Sidekick-powered boards built for use case ideation, risk brainstorming, and keeping AI-assisted projects on track.

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.

Scaling agentic AI across an enterprise requires more than great technology. It requires trust. This final post in the series explores how governance turns AI from an experiment into a reliable operating model, and what it looks like when operations teams are finally free to focus on the work that actually moves the business forward. At some point in every AI rollout, the same question comes up: how do you help everyone move faster without creating a mess? Operations leaders have seen this story before.

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.

Most governance policies were written for defined records, such as a signed document, a sent email, or a file with one author and one date. That type of work still exists, but it now also takes place in team chats, AI conversations, and across canvases.

For most operations teams, managing incoming requests is itself a full-time job. This post explores how multiplayer AI helps teams move out of reactive mode, automating intake, keeping portfolios on course, and giving leaders the real-time visibility they need to make better decisions, faster. Every operations team knows the feeling. Requests arrive faster than anyone can review them.