What I hear and read is consistent: AI has been around for a while, but the value is still unclear. Also, leaders don’t know what kind of team behavior would move the needle.
Imagine a team that is not 100% sure about its AI direction, the individuals use AI tools independently but not together, and the tools seem to take more time (and money) than they give. I read this as a very typical situation that can be improved.
For example, in the Nordics, 79% of organizations report efficiency gains from AI, but only 18% see revenue growth (Deloitte, State of AI in the Nordics 2026). Where does the impact go?
I’ve noticed that most team leaders don’t know where their team stands with AI, or how to guide the team forward with it.
The good news: a team that is moving in the right direction shows it in behavior. And when you can identify the behaviors, you can lead them.
Lead these six behaviors
These are the six behaviors I track when working with teams:
1. The team shows trust
- The team has open dialogue about AI and change, and supports each other
- Evidence: The team voices all their thoughts freely and reports experiencing support
→ If trust is your team’s issue, work with yourself first. What does trust mean to you? What builds or breaks the trust? How do you know it? What’s your role in building trust? When you trust yourself and the team, you can help the team build trust as well.
2. The team takes ownership
- The team proposes next steps and AI experiments itself, the leader doesn’t have to push
- Evidence: The team suggests and defines most of the AI initiatives within the team
→ Ownership is a by-product of trust. Coach your team instead of advising. When you ask – “What could we try next?” – your team becomes more active, confident, and self-guided.
3. The team has a focus
- The team picks AI goals, metrics, and actions that serve the business, and protects them
- Evidence: Something was declined because it didn’t serve the direction
→ You need to know and understand the direction first, before you can guide your team towards the right goals. Then show them the way and the why, and help them figure out the “how” part.
4. The team chases impact
- The team reviews what its AI work delivers and continues only impactful acts
- Evidence: Something was stopped or redirected because it wasn’t impactful
→ “Kill your darlings” and “fail fast” are fantastic strategies with AI. Some AI results might look “nice and pretty”. But are they really impactful in the sense that something has changed? Make sure that you follow your KPIs and metrics, and have an ongoing conversation about real impact.
5. The team leverages impact
- What the team does benefits other teams within the organization
- Evidence: 3–5 named teams report benefits of collaboration or shared practices
→ Organization-wide AI adoption is the most challenging part of the change. If your team is amplifying this, KUDOS! The question is how you need to work as a team, so that you can help the whole organization succeed with AI. Map this together and identify first steps.
6. The team shows growth
- The team keeps learning and developing together
- Evidence: New capabilities in use that nobody mandated
→ High-performing teams grow: they reflect, learn, and develop together. Learning is about trying, failing, and succeeding. Make all of this safe for you and the team.
How many of these six do you see in your team this week?
