From Tool to Team Member: How AI is Redefining Collaboration
“AI is more than just a personal productivity booster,” says Veit Brücker from Asana in an interview. “When properly integrated, AI helps make collaboration more efficient.”
Mr Brücker, AI is currently seen more as an efficiency tool for individual employee productivity. But you say AI has the potential to be a genuine team member. What exactly do you mean by that?
Veit Brücker: Many companies currently use AI primarily to complete individual tasks faster—checking texts, compiling information, ticking off small to-dos. Sure, that’s handy. But it doesn’t come close to tapping AI’s full potential.
Especially in times of remote work and hybrid teams, the direct connection between colleagues can sometimes be missing. Quick check-ins, clearly assigned tasks, and close collaboration towards the same goal can occasionally fall by the wayside. On top of that, today’s work environment is increasingly shaped by industry-specific expertise, which can easily get lost in silos. This is precisely where AI can do much more than just assist with personal tasks. When properly embedded into workflows, it helps teams better organise themselves, make knowledge accessible, prioritise tasks, and ultimately work together more efficiently.
What does that look like in practice? Studies show, for example, that employees spend many hours per week in meetings and switching between tools. What tasks can AI take on here to measurably lighten the load for teams?
Veit Brücker: Let’s be honest—information is lost far too often these days, whether in meetings without clearly documented outcomes, endless chat threads, or due to using fragmented tools. AI can help capture and make knowledge accessible to everyone. That’s the foundation for truly effective teamwork. The numbers speak for themselves.
According to our State of Innovation Report 2024, employees spend an average of 8.8 hours per week in inefficient meetings and another 8 hours switching between different tools. That’s a lot of time lost to coordination rather than creation. AI steps in here. It automatically summarises meetings, filters relevant information from emails and various tools, creates status updates, and prioritises tasks—all without manual input.
AI is particularly good at recognising patterns. How can this be leveraged for team collaboration, especially when it comes to avoiding friction between different tech teams?
Veit Brücker: AI can also be used to detect early signs of bottlenecks. For example, if approvals regularly take too long or tasks repeatedly get stuck at the same stage, the AI solution flags this and suggests improvements. This saves teams valuable time, which they can then spend on higher-value work.
A good example is identifying typical project management bottlenecks. When multiple tech teams are working on a project, the AI solution recognises when deadlines overlap or which team members are overloaded. Based on this, it can provide early optimisation recommendations, such as reprioritising tasks or adjusting responsibilities. This is particularly helpful in cross-functional teams, where communication can easily become unclear.
What technical prerequisites need to be in place for AI to integrate smoothly into team workflows, particularly regarding data and governance structures or compatibility with existing systems?
Veit Brücker: AI needs context. And that only comes when data is up-to-date, structured, and accessible. Without that, even the best AI can’t provide meaningful recommendations. In practical terms, this means well-maintained project data and clearly defined workflows are essential. There also need to be in-tool feedback loops so teams can easily evaluate and fine-tune the AI’s suggestions.
The user experience must be intuitive. To achieve this, AI should be seamlessly integrated into existing tools—not as an additional system, but as an extension of what teams already use. Only then does it become a genuine daily support, not creating new silos but making existing processes smarter.
Looking at the current state of play in companies, they and their employees primarily see AI as a project for management or the central IT department. Why is it crucial for specialist departments and teams to be involved early on?
Veit Brücker: This is so important because the specialist departments are the ones working with the technology day in, day out. If AI use cases only come "from the top," they remain abstract projects for many. The real added value only emerges when teams themselves recognise: This is where the AI solution helps, here is where time is saved and workload reduced. Because the actual goal is for AI to meaningfully complement teams—supporting where it excels, not replacing the human role.
However, this requires teams to be involved early in the implementation process. Only then does trust develop. When employees, within a defined framework, can work with their own use cases, test the AI solution, and make adjustments, it evolves from a tool into a team member. Then it’s not just central IT and management using the technology—the entire company benefits. And that, ultimately, is the key to successful integration.
is Head of DACH & South EMEA at Asana.
