Teams | Collaboration | Customer Service | Project Management

Most organizations have access to a large amount of workforce data. They track: Yet operational problems still happen. The problem is not a lack of data. The problem is that traditional workforce reporting mainly looks at the past. Modern operations need visibility into what is happening right now. This is why more organizations are adopting operational intelligence software.

HR teams handle hundreds of employee requests every week. Without a system to manage them, requests arrive through email, chat, walk-ups, and forms — spread across channels with no single owner, no SLA, and no visibility into what is open or overdue. The consequences are direct: tickets get missed, the same question gets answered differently by different agents, and sensitive employee data sits in inboxes with no access controls. The median HR request resolution time is 82 hours.

To reduce bench time, IT consulting firms need to forecast staffing needs weeks ahead of project end dates, maintain a live searchable skills inventory, structure idle time into productive "active bench" work, and replace spreadsheet-based tracking with real-time visibility tools. Firms that implement these practices typically bring bench rates down from the 20–30%+ range seen during poor management to the 5–10% "frictional" range considered healthy. ‍

AI makes your organization’s knowledge easier to find and use. That’s the whole point. But it also means sensitive data moves faster, surfaces in more places, and becomes harder to track. The pressure to act is real, but the playbook isn’t new. You still need to know where sensitive data lives, govern how it’s being used, and prevent it from unauthorized exposure. AI is now giving you a reason to revisit your data security posture and make sure it’s strong enough to keep up.

Less than six months ago, the Atlassian Rovo Model Context Protocol (MCP) server went GA, giving Claude, Cursor, and every major AI agent direct access to Atlassian for our customers. Today, over one million users trust it every month to do real work through agents. But that number isn’t the story. The story is what’s happening inside those interactions: how AI agents are actually being used at enterprise scale, and who’s getting the most value.

AI-powered insights into how teams work, recognized by the people who use them every day.

I've watched it happen more times than I'd like. A group comes together, everyone nods through the kickoff call, and then somewhere between that first screen-shared agenda and the actual deadline, two people are carrying everything while the rest quietly coast. Nobody says anything. The work still gets done, technically. But the people doing it know exactly what happened. That's social loafing. It feels unfair the moment you experience it, but somehow gets filed under "group dynamics" and left there.

Employee availability refers to the days and times an employee is able or willing to work. Teams use availability to support staffing, shift coordination, schedule planning, and attendance visibility. The term differs from a work schedule, which refers to the specific shifts or hours an employee is assigned and expected to work.

Before joining Teamwork.com, I spent years running delivery at agency firms. Back then, "PSA" meant either an overpriced system nobody wanted to use or a Frankenstein stack of spreadsheets and disconnected apps. The ticketing tool didn't talk to the billing tool. The billing tool didn't talk to the project tracker. And the project tracker was, let's be honest, a shared Google Sheet held together by hope.

A few years back, I was managing six client accounts at once with a stack of tools that didn't talk to each other. Time tracking in one app, project tasks in another, and a spreadsheet held together by hope to figure out whether any of it was actually profitable. That setup cost us hours every week in manual reconciliation, and we still missed billing for work that fell through the cracks.