Managing resources across ten concurrent projects is a nightmare. I’ve heard about AI tools that handle 'automated resource leveling' to prevent over-allocation. Does this actually work in a dynamic environment where priorities shift daily, or does it just create more manual cleanup for the PMO? Looking for real-world feedback on the current maturity of these AI tools.
3 answers
We’ve been using an AI-based scheduling engine for our PMO for about six months. The biggest advantage isn't that it's "perfect"—it isn't—but that it can run 500 different scenarios in seconds. When a high-priority bug comes in, the AI suggests three different ways to reshuffle the team with the least impact on our critical path. You still need a human to make the final call because the AI doesn't know if a developer is having a bad week, but it eliminates about 80% of the manual spreadsheet work. It has definitely improved our team utilization rates by roughly 12% across the board.
Does the tool you use integrate directly with your time-tracking software to adjust future estimates based on actual hours worked?
AI resource leveling works best when you have a large pool of interchangeable skills; for niche experts, it still struggles with the nuances.
Barbara is right. We found that for our 'Generalist' pools, the AI is a godsend. However, for our Senior Architects, we still do a manual review every Friday to ensure the AI hasn't double-booked them on high-stakes client calls.
David, that integration is the 'secret sauce.' Our AI pulls actuals from Harvest and compares them to the initial estimates in Smartsheet. If it sees a recurring 20% lag in UI tasks, it automatically pads future schedules. This "self-healing" schedule is far more accurate than our manual forecasts ever were. It takes a few months for the machine learning model to get enough data to be accurate, so patience is key during the initial setup phase.