Teams | Collaboration | Customer Service | Project Management

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.

One point four million real workplace conversations with AI were analysed last year. About 5% of people were using it in ways that improved the quality of their work. The “Are you using AI?” conversation is over. Nobody won it. We just stopped having it. But the question we skipped is, “Is anyone reading this stuff before it gets sent?” Increasingly the answer is no, and we can all tell.

Whatever you need to know is already in Teamwork.com: resourcing, budgets, task status, all of it live. The only question is how many clicks stand between you and it. Ask AI removes them: ask what you'd ask a colleague, typed or said out loud, and get the answer straight from your own data.

With AI assistants like Claude and ChatGPT becoming more deeply integrated into knowledge work I wanted to run a like-for-like comparison in a competency close to my heart: data analysis.

Tamar Yehoshua is Chief Product and AI Officer at Atlassian, where AI agents are now direct users of the software her team builds.

I’m TJ Power, a neuroscientist, no.1 bestselling author of The DOSE Effect, and the founder of DOSE Lab. Over the last 12 months, my team and I have been laser focused on a new question: how can we help society build successful brains that are ready for the AI future? Recently, with Asana, I hosted a series of dinners in Sydney and Melbourne for C-suite executives.

“How much PTO do I have left?” “Where can I find my latest payslip?” “Am I eligible for this benefit?” These are simple employee questions, but resolving them can mean checking the HRIS, searching policies, retrieving live data, chasing approvals, and updating records. At scale, that administrative work adds up.