Teams | Collaboration | Customer Service | Project Management

When Andrew King, who heads up competitive intelligence at Asana, needs to help a sales representative win a deal, the information has to be exactly right. This means sourcing specific, current details customized for each individual rep. King supports a 400-person sales team. Each rep is working different deals, facing different competitors, and at different stages of the sales cycle.

AI is changing both how employees work and how IT teams manage the tools they rely on. We are building for both. This week, we added two new AI capabilities to CurrentWare Cloud. AI Usage Insights is the workplace side that shows you which AI tools your employees are actually using, how adoption is trending across teams, and where unapproved tools are showing up.

Open the Log Time modal in Teamwork.com, click over to the Log my day tab, and the entries are already there: an hour on a client kickoff call, thirty minutes on competitor research, two hours drafting a homepage design. Teamwork.com built each of these from the tasks and projects you actually worked in today, before you typed a single word.

As AI accelerates work, leaders need to be more deliberate about what technology takes off people’s minds and where human attention can create the most value. AI promises to help organisations move faster. But at two Asana executive dinners held in Sydney and Melbourne in August, a different leadership question emerged: what should we choose not to accelerate? The challenge is not finding more hours in the day, but creating better conditions for the hours people already spend.

Here’s a stat that’s been rattling around in my head. In our 2026 AI SDLC study, 94% of engineering leaders told us they’re using AI, but only 6% have the systems to actually scale it across their whole software lifecycle. Almost everyone is playing with agents, yet almost no one can let agents run at scale without things breaking.

Most vendors sell "AI work management" as a prettier board with a chat box. That framing is too small for anyone running paid client delivery. AI work management is the operating layer that decides who does what, when you can safely promise dates, and what the work actually costs. It also decides which parts a supervised AI agent can take without wrecking trust. Before I joined Teamwork.com, I saw the spreadsheet version of that job repeatedly across agency environments.

AI financial insights for professional services leaders are not a chatbot bolted onto last month's export. At Teamwork.com, we see leaders need margin, utilisation, and forecast signals while work is still movable. For example, a delivery lead can change staffing mid-project before an overrun hits the P&L; I've watched firms mistake a prettier dashboard for insight.