Our leadership team keeps putting AI research as a priority on our V/TO, but it always gets pushed to the next quarter because nobody knows where to start. How do we reframe these initiatives to ensure they actually get executed?

Question

Tyler Smith · Accepted Answer

The main reason AI initiatives stall is because leadership teams treat them as science experiments rather than operational improvements. When you put AI research on your V/TO®, you are inviting planning theater.

To get traction, change your terminology. Refrain from calling these initiatives ML projects. Instead, frame them as operations-improvement projects that use ML. This shifts the focus from the technology to the actual business outcome.

For example, instead of a Rock to explore AI in marketing, write a Rock to automate our competitor research process to save twenty hours a week. Now, the goal is concrete and measurable.

Once the operational goal is clear, use a practical approach to solve it. You might use a thirty-line scraper to extract public confessions of operational pain from competitor reviews, then use a basic AI tool to analyze the data. By keeping the focus on solving a specific bottleneck, you bypass the technology hype and build real, functioning operational assets that show immediate results in your business.