How to Fix Your AI ROI Logjam: A Step-by-Step Guide for Teams

Why Your AI ROI Is Stuck in a Logjam (And How to Clear It)

Many companies are investing heavily in artificial intelligence, but their AI ROI is stuck in a logjam. According to the latest Atlassian State of Teams report, only 6% of executives can confidently point to organization-wide AI returns. Meanwhile, 85% of knowledge workers are using AI at work, often because their leaders told them to. That’s a lot of effort with very little payoff.

So what’s going wrong? In many cases, teams jumped on the AI bandwagon without a clear plan. They wanted to keep up with the competition, but they forgot to ask a simple question: Why are we using this? When new tools are rolled out without a shared goal, clear communication, or team support, adoption becomes messy. It reinforces the silos that already exist, rather than breaking them down.

The Atlassian report backs this up. Of all knowledge workers using AI, only 29% have integrated it into their cross-functional workflows. This creates what Atlassian calls the “fragmentation tax.” I call it the AI logjam. Individuals move fast, but review and approval cycles can’t keep up. The result? Lots of activity, but little real progress.

How to Make AI Work for Your Team, Not Against It

Here’s the good news: you can clear the logjam. It just takes a smarter approach. Below are three practical steps to improve your AI ROI and make artificial intelligence a true asset to your team.

1. Start with a Plan: “Go Slow to Go Fast”

Before rolling out any AI tool, take a step back. Ask your team these key questions:

• What specific problems do we want AI to solve?
• Which tools are best suited for those problems?
• What rules or guardrails do we need (e.g., which tools can access private data)?
• What training and support will our team need to adopt these tools smoothly?

At my firm, we spent a whole summer running collaborative work sessions. We explored which parts of our business could benefit from AI, and we set norms for how to use it. We also made time for experimenting and sharing what worked (and what didn’t). By treating AI adoption as a team effort, we moved forward together—not in scattered, individual bursts.

2. Use AI to Improve Collaboration, Not Just Individual Tasks

AI can do more than help one person work faster. It can also make teamwork easier. Here are a few examples from my daily routine:

• Email scanning: I use a Copilot agent once a day to check if my lack of response is blocking someone else’s progress. This lets me focus on priority work without worrying about being a bottleneck.
• Meeting recaps: We use Copilot within Teams to keep virtual meetings on track. It automatically creates a recap with action items and decisions, so nothing gets lost.

These small changes help the whole team stay aligned and move faster together.

3. Clean Up Your Collaboration Processes First

Adding AI to a broken system will only make things worse. Before you invest in new tools, fix the basics. Strong asynchronous collaboration is key. That means:

• Clear document storage and version control
• Norms for co-creation, reviews, and approvals
• Knowing which channel to use (email, instant message, meeting) for each type of communication

When your foundation is solid, AI becomes a multiplier—not a source of more confusion.

The Bottom Line: Invest in Orchestration, Not Just Tools

The data is clear: leaving AI adoption to individual initiative won’t deliver the returns you want. It creates more logjams than leverage. If you want to see meaningful gains, invest as much in coordination and teamwork as you do in the technology itself. That’s how you turn AI from a shiny distraction into a real driver of business results.