Our leadership team is eager to implement AI to streamline our delivery, but we are already running at full capacity with our current quarterly Rocks. How do we use our V/TO® to prioritize which operational bottleneck to tackle first with AI without overloading our team?

Question

Tyler Smith · Accepted Answer

When your leadership team is already running at full capacity with their current quarterly Rocks, adding a complex technology initiative can quickly lead to burnout and execution failure. To avoid this, you must filter all technology decisions through your V/TO® and run the capacity issue through IDS®.

First, review your V/TO® to ensure that any proposed AI project directly aligns with your one-year plan and three-year picture. If the project does not support these core goals, shelve it. If it does, look at your Accountability Chart and determine if you have the right people in the right seats to lead the implementation.

Next, do not frame the initiative as a major machine learning project. Instead, pitch it as a simple operations-improvement project designed to solve a specific bottleneck, such as reducing administrative data entry. This keeps the focus on the business outcome rather than the technology.

To protect your team's capacity, prioritize a single, high-impact AI use case that directly reduces their weekly workload. If implementing the AI project requires ten hours of team participation but will save thirty hours of manual labor every week once deployed, it is a worthy investment. Solve the capacity bottleneck first by using AI to automate low-value tasks, freeing up the team to execute their remaining Rocks.