The work got faster. The waiting did not
A team can produce a prototype quickly and still spend weeks waiting for a decision about it. From inside the team, the work feels transformed. From outside the company, very little appears to have changed.
Jason M. Riggs describes that mismatch in his return to ChatGTM. The Audivi AI executive and author talks with Michael Beck and Chad Horn about the gap between faster execution and slower organizational decisions. His concern is that a fast process can stall inside a system still organized around a much longer production cycle.
The episode then moves into drive-through ordering, where another form of delay becomes visible to the guest. Across both subjects, speed only matters when the complete process works. Producing an answer quickly is one component. Making the right decision, checking the result and connecting it to the next step remain part of the job.
Find the decision that is actually waiting
Riggs describes organizations enthusiastic about AI while retaining repeated alignment meetings and slow approval processes. Teams can have the capacity to build without the authority or clear direction to move. More output then collects beneath the unresolved decision.
His argument is for deliberate speed, with enough information and critical thinking available to the people doing the work. He does not suggest that strategy or planning disappear. He questions whether repeated deliberation is always adding value, especially when the organization could learn from a bounded next step.
A useful review starts with one stalled project. Name the decision, the person responsible for making it and the information still missing. Distinguish between a question that genuinely needs investigation and a meeting that repeats what everyone already knows. A team cannot improve decision speed if the decision itself remains vague.
Do the reality check before the error grows
Riggs is equally clear that faster production creates a need for judgment. He describes a small unchecked problem being carried forward and built upon until it becomes much harder to undo. His metaphor for that compounding error is the Godzilla effect.
The practical point is that an early review can matter more when work moves quickly. Check whether the starting information is accurate, whether the requirement makes sense and whether the output does what the team intended. A polished first draft still needs someone capable of challenging it.
For a business using AI in product or operating work, that suggests placing review at consequential transitions. Confirm the premise before building on it. Check a customer-facing result before expanding its use. The aim is a process that learns quickly without allowing an unnoticed assumption to become the foundation for every later decision.
Test the order all the way to the restaurant system
The drive-through portion of the conversation makes that end-to-end requirement concrete. Riggs discusses accuracy, conversation timing, the audio environment and connection to the point-of-sale system. A convincing voice alone does not demonstrate that the restaurant will receive the correct order.
He also describes the importance of changing menu conditions, such as an unavailable item, and of letting guests confirm what the system captured. These are ordinary operating situations. They belong in an evaluation alongside the clean demonstration in which every item is available and every sentence is easy to hear.
An operator's test should therefore follow the complete transaction. Check the spoken request, the captured modifications, the displayed confirmation and the order reaching the kitchen workflow. Include the team's response when something goes wrong. The relevant question is whether the system helps the restaurant serve the guest reliably under its actual conditions.
Let the pilot produce the next decision
Riggs describes a progression from understanding the audio environment through testing and a limited pilot. He explains that brands may begin with restricted periods and expand as confidence develops. This is his account of implementation during the recording, rather than a universal rollout requirement.
That approach offers a way to connect the episode's two themes. Leadership can authorize a defined experiment without pretending every uncertainty is resolved. The experiment can then supply evidence for the next decision, provided the team agrees in advance on what it needs to learn.
Specify the outcome, the review point and the person who will decide whether to continue. Preserve room to stop or adjust when the evidence warrants it. Faster work becomes useful when the organization can make that next decision clearly, with human judgment still attached to the result.
