How to shift your mindset from “AI as a tool” to “AI as a partner”

AI is everywhere, but the value isn’t as apparent…yet.

In Microsoft’s Work Trend Index Report, 75% of professionals say they’re already using generative AI at work, and usage nearly doubled in six months. Atlassian’s AI Collaboration Index, which surveyed 5,000 knowledge workers around the world, shows people think AI makes them 33% more productive and saves about 105 minutes every day – more than an extra workday each week.

But while AI adoption has doubled, many organizations still struggle to point at specific metrics that show company-wide AI ROI.

The gap? Mindset.

Adoption rates matter less than attitude when it comes to improving work quality and innovation. Teams who treat AI as a shared teammate, not just a shortcut for their solo work, are:

• Seeing 2x the ROI on their AI efforts compared to basic users
• Significantly more likely to reinvest time savings into learning new skills and generating new ideas
• 1.8x more likely to say AI has significantly transformed organization‑wide efficiency

That’s the power of treating AI as a team partner, not just a personal productivity tool. It doesn’t just help you move faster. It helps your team do more meaningful, innovative work together.

Personal tool ❌ Shared team capability ✅

When AI lives only in private chats and personal workflows, it may speed up individual work, but it also often leads to more fragmentation and makes it harder to see how individual efforts roll up to shared goals.

The organizations getting higher returns on their AI investments are doing something different. As Molly Sands, Head of Atlassian’s Teamwork Lab, puts it:

These teams are moving from using AI to teaming up with it. That means giving AI a clear role in how your team plans, coordinates, and learns – and making its contributions visible to everyone, not just whoever typed the prompt.

AI teammate tips: 4 ways to start shifting your AI mindset and methods

In Atlassian’s research and daily work, we’ve seen that the highest performers treat AI like a team of expert advisors that’s integrated into shared work and focused on delivering more value. Try these four tips to start thinking differently and working differently with AI as your teammate.

Shift #1: From one‑off prompts to ongoing conversation and collaboration

➜ Iterate, don’t abdicate. Ask follow‑up questions, and challenge AI’s answers. Use it to sharpen your thinking, not replace it.

Someone who views AI as a tool asks it a question, reads the output, and moves on, even if the result isn’t what they were looking for. Someone who treats AI as a thinking partner goes back and forth with it, providing more information and refining the results.

This could be as simple as asking follow‑up questions or prompts instead of accepting the first answer. Or it could be more complex, like feeding it a lot of information (goals, audience, constraints, etc.) and asking it to help hone your thinking, not just your writing.

Either way, adding more history, context, and collaboration means AI can move past being a one‑time tool and start acting like a teammate who remembers what you’ve tried before and considers the details before delivering a more informed response.

3 quick prompts to try it:

1. First prompt: “Write an email summarizing the strategy our team created in this document: [link].”
   Second prompt: “Now rewrite that email for an exec who has 30 seconds to understand the highlights of our plan and the details they’ll care about most.”
2. First prompt: “Challenge this recommendation. What risks are we missing?”
   Second prompt: “What next steps would you recommend to address those risks?”
3. First prompt: “You are a product marketer brainstorming ideas for a new campaign. The audience is [insert audience]. The goals of this campaign are [insert goals]. Here is research we just conducted about this audience: [link]. And here are a few examples of past campaigns that have performed well: [link] [link] [link]. One idea is to [insert idea]. Based on what you know so far, rate this idea, and propose 3 more that might also work well. Ask any clarifying questions before beginning.”
   Second prompt: “Based on your questions and the ideas you just shared, help me refine my original idea and your top 2 ideas so they’re clearer, more specific, and differentiated from each other. Then, suggest 1-2 ways we could test each concept quickly.”

Shift #2: From speed to shared understanding

➜ Use AI for clarity, visibility, and better decisions, not just faster tasks.

Most conversations about the value of AI start around speed. How much time can we save? How many tasks can we automate? How many meetings can we replace?

Those are fair questions, but they only scratch the surface of what’s possible. Atlassian research found that organizations who prioritize personal productivity as their main AI outcome are 16% less likely to see significant innovation gains. Speed alone doesn’t create better ideas. Sometimes, it just creates faster rework.

Your team can take AI usage to the next level by using it to improve:

• Goal clarity, such as mapping work against strategic priorities and flagging projects that don’t clearly connect
• Visibility into who’s doing what and why, such as pulling together information scattered across tools into a single, shareable view
• Decision quality, such as helping teams see trade‑offs, risks, and alternatives before committing

3 quick prompts to try it:

• Goal clarity: “Show me which of my team’s Jira epics don’t connect to any company OKR.”
• Visibility into who’s doing what and why: “Scan our Jira boards, Confluence project docs, and recent status updates to surface cross‑team dependencies. List which teams are involved, links to connected work, why they’re dependent on each other, and risks or blockers if those dependencies aren’t coordinated.”
• Decision quality: “We need to decide which feature to prioritize next. Here’s the context: [product goals, target users, success metrics, timeline, tech constraints, and any revenue or strategic commitments]. Based on this information, tell me the top 3–5 options we should consider and why. Also highlight any assumptions that might be wrong and any additional data or input we should consider before deciding.”

Shift #3: From training for adoption to enablement for transformation

➜ Use training as a starting point. Then design systems, rituals, and roles to embed AI in the way your team works together.

Most organizations start their AI journey with training on how to use AI tools. They roll out a training, launch an AI resource center, and track logins or prompt volume. Training is essential, but awareness‑focused efforts don’t reliably move the metrics that matter most. Real behavior change comes from champions and teams experimenting with AI in the flow of their actual work, not one‑and‑done sessions.

Enabling people to use AI for transformation looks different. It focuses on systems, rituals, and roles, not just skills. We’ve found that the highest returns on AI are coming from companies that:

• Build a connected, company‑wide knowledge base
• Set up systems for AI‑powered coordination
• Make AI part of the team

This requires investing time, effort, and resources, but it makes a difference. Beyond just teaching people how to click the buttons and save time, methods like these help them learn how to collaborate with AI to drive better outcomes.

3 ways to try it:

1. Create AI Working Agreements as a team to align on why, when, and how to use AI so it becomes a seamless, trusted part of your workflow.
2. Ask managers to model using AI. (Atlassian found this made team members 3x more likely to be strategic AI collaborators!) Then, pick one use case or ritual, and commit to using AI during it to see what you learn. For example: “For the next four weeks, we will record our weekly standup and ask AI to automatically create a page with a summary, assign tasks for action items, and flag conversations and decisions that may impact work for other teams in our organization.”
3. Run an AI workshop or AI Innovation Day to help your team learn the basics of how to use AI to save time, build knowledge, and – most of all – create new value.

Shift #4: From “AI will fix this” to “AI changes how we work”

➜ Treat underlying challenges rather than looking to AI as the cure.

AI doesn’t fix broken processes, systems, or team dynamics, but it does amplify what already exists.

Unclear goals? Without guidance, AI may just generate more variations of the wrong thing.
Fragmented knowledge? AI may surface the wrong information or not be able to find the data you need.
Lack of trust? People may hold back from sharing ideas and learnings.

Gartner notes that generative AI can “turn the byproducts of normal interactions into structured knowledge assets.” That’s powerful, but only if a team is willing to share those interactions and act on what AI surfaces.

That’s why the most successful AI collaborators invest in clear documentation and decision logs, make work visible across tools and teams, and encourage healthy skepticism. They treat AI as a strong first draft, not the final word, and still have hard conversations around what’s broken and how to fix it. With AI as your partner, your team can not only gather and understand priorities, conflicts, and feedback faster, but also create a smart path forward.

3 steps to start making this shift:

1. Cultivate trust and psychological safety by listening to and implementing feedback, modeling vulnerability, celebrating lessons learned as much as wins, advocating for your team, avoiding pointing fingers, and setting audacious goals.
2. Make the work you do with AI visible, like sharing AI‑generated summaries, ideas, use cases, and tips with your team.
3. Take a short assessment to measure AI ROI so you can prove its value, secure funding, and keep the momentum going.

Time to team up

The future of work isn’t humans vs. AI. It’s humans with AI. It can help us find, understand, decide, and build what we couldn’t on our own – but only if we shift from treating it as a tool to treating it as a teammate.

With AI by our side, we’ll start asking sharper questions, making braver (but calculated) bets, and delivering more value faster. The teams who make these shifts won’t just keep up with their work today — they’ll help define what work looks like tomorrow.