Without the right context, checkpoints and controls, teaming up with AI can be shallow, full of errors or even counterproductive.
According to Asana’s Work Innovation Lab—which surveyed 9,236 knowledge workers in the United States, United Kingdom, Germany, Japan and Australia in 2025—the vast majority of peopleAI agents are fundamentally changing how we work as opposed to being just another tool to help us get things done.
While 77% of workers are already using them, that usage isn't leading to real impact yet. The fastest adoption curve in workplace technology is the one we’re least ready for, and it’s stopping us from unlocking the massive opportunity in human-AI collaboration. Without the right context, checkpoints and controls, teaming up with AI can be shallow, full of errors or even counterproductive.An AI agent without the right context is like a new hire with no onboarding—it might get a few tasks done, but it won't move the work forward in a meaningful way. That's why the first step to making AI a truly valuable teammate is to put agents “on rails.” Giving AI the right context means connecting it to the very map of your company's work. It's about giving an agent a foundational "memory" of your team's goals, how projects are connected and the roles people play. With this bigger picture, AI agents stop being simple task runners and can instead proactively help prioritize, anticipate next steps and adapt to shifting goals. This approach ensures they amplify productivity rather than adding to the chaos. For example, a campaign strategist agent, with context pulled from the marketing calendar and performance dashboards, could automatically draft campaign briefs, propose timelines and flag risks. It wouldn't just tell you a deliverable is late; it would analyze project timelines and resource availability to recommend a solution. With this level of insight, the agent can actively help you track campaign progress, connect performance back to spend and ROI and anticipate roadblocks before they slow you down.If your team can't see why an AI agent made a decision, how can you ever truly trust it? That's where checkpoints come in. They aren't about slowing things down; they're about giving you the confidence to trust the pace of change. They're the critical guardrails you put in place to ensure human oversight is baked into your workflow. Checkpoints should give teams visibility into an agent's reasoning. When built right, you should be able to see exactly how an agent reached a recommendation. They might generate subtasks to break down the steps of their work, comment with the direction for what they're doing or pause at key points to ask if they're on the right track. Meanwhile, gated reviews on sensitive workflows act like a brake pedal, allowing a human to approve or adjust a decision before it goes live. These checkpoints are what make accountability possible, ensuring that people remain responsible for key decisions. The goal is to give your team the confidence to step in, course-correct and know that their feedback will be reflected in the final outcome. For example, an IT ticketing specialist agent might automatically categorize and route service requests. A checkpoint could be a human review of all tickets it flags as a service level agreement risk before they are escalated. Similarly, a manager could review a summary of weekly ticket volumes and trends before the report is sent to leadership to inform staffing decisions.While most workers agree humans should be responsible for any errors an agent makes, not many agree on who that human is. Our data shows workers are split on who to blame, with many admitting they simply have no idea. This confusion creates a major roadblock to AI adoption, which is why establishing clear control is so important. Control isn't about adding red tape or slowing things down. It's about defining clear ownership so your team can move faster and build better agents in the future. Just like a team member, an AI agent needs a clear owner who is accountable for its performance. This means establishing a framework to measure performance based on accuracy and error rates, and knowing exactly who to go to when something goes wrong. With clear lines of control, you remove the confusion and build the confidence required to truly scale AI responsibly across your entire company. For example, a spec reviewer agent reviews design docs for clarity and completeness. If it starts flagging too many false positives—identifying issues in specs that are actually clear—the owner is immediately notified. With a clear owner and a dashboard showing the agent’s accuracy, they can quickly adjust its parameters to reduce wasted time for the entire product team.This isn't about simply adopting AI—it's about doing it right. By giving your agents the right context, building in clear checkpoints and establishing concrete control, you can move beyond personal assistants and AI pilots. Empowering people with a trustworthy foundation and clear understanding of how to work with AI doesn't just deliver better results—it enables a meaningful transformation in how work gets done. When done right, this can help increase capacity, boost productivity and lead to higher success rates in achieving objectives.
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