Asana

A lot of operational processes look scalable until the person holding them together isn’t available. For a while, that was the case with our RevOps intake process. Requests from reps and partners would come through Slack, and I would manually review them, work out what the actual issue was, find the relevant context, categorise the request, and route it to the right person. When I stepped into the RevOps role, resolution times were taking three to four days at best. The issue wasn’t necessarily the complexity of the requests. It was that too much of the process depended on one person knowing where to look, who to involve, and what should happen next. By quarter-end, the deadlines were piling up, the pressure was building, and I was probably losing more hair than I was saving time. That was when I knew something had to change. So I rebuilt my workflow using Slack, Asana, and an AI Teammate. Requests automatically moved directly from Slack into a shared Asana intake project. From there, they were categorised by department, effort level, and request type by AI-assisted rules. The AI Teammate then completed an initial assessment, pulled in relevant context from similar cases, identified the right escalation path, and provided the person who submitted the request with initial guidance directly in Slack. By the time the request would even reach me, a lot of the investigation and triage had already been completed, giving me the time back to focus on actually solving the issue at hand. The result was a reduction in our average resolution times from four days to less than two hours. We quantified the time savings and the math was clear. We were saving the business equivalent to around $100K per year just by optimising our process in Asana with AI Teammates. But the more important outcome was that the process no longer depended entirely on one person in our region. Anyone can now raise a request and get a structured, researched response, regardless of whether I’m available at that exact moment. For me, that’s one of the most practical uses of AI in RevOps in the era of Agentic Work Management. It isn’t about adding another tool or automating work for the sake of it. It’s about giving AI agents enough context to understand a request, coordinate work across systems, guide the person asking for help, and bring a human in when judgement is actually required. The human still owns the outcome. The agent helps move the work forward. And knowledge that previously lived with one person becomes a scalable process the wider team can use. If you're interested in learning more, click on the link in the comments below👇