Instead of protecting umpires from anger, MLB's robot system publicly highlights their every mistake on a giant scoreboard. This has turned umpire errors into viral moments of public humiliation, putting individuals under a microscope and increasing vitriol, the opposite of the technology's hoped-for effect.

The initial robot umpire system, which called the 'textbook' strike zone, felt wrong to players and fans. To improve user acceptance, Major League Baseball reprogrammed the system to be less precise and better reflect the slightly larger, human-defined strike zone everyone was accustomed to, prioritizing feel over objective perfection.

Contrary to fears that automation would make baseball sterile, the robot umpire 'challenge system' has introduced new dramatic pauses. When a player challenges a call, the entire stadium collectively looks to the scoreboard for the robot's verdict, creating a suspenseful, shared experience that enhances fan engagement.

With AI, the "human-in-the-loop" is not a fixed role. Leaders must continuously optimize where team members intervene—whether for review, enhancement, or strategic input. A task requiring human oversight today may be fully automated tomorrow, demanding a dynamic approach to workflow design.

The biggest internal barrier to AI adoption is a marketer's reluctance to relinquish control. The solution is to build trust incrementally through rigorous testing. Start with small, automated processes, validate them against manual efforts, build confidence, and then scale.

The most effective use of AI isn't full automation, but "hybrid intelligence." This framework ensures humans always remain central to the decision-making process, with AI serving in a complementary, supporting role to augment human intuition and strategy.

The choice between human-in-the-loop and full automation isn't binary; it's a maturity curve. Evaluate each AI use case using a rubric based on risk, the ability to reverse a decision without harm, and the reproducibility of its outcomes to determine the appropriate level of automation.

Founders shouldn't expect AI to automate a business function instantly. Real-world adoption is a gradual "glide path" where automation scope increases over time. This requires building systems that facilitate human-AI interaction, allowing humans to coach the AI and vice versa for a smooth transition.

During testing of a full robot umpire system, players were less likely to argue with a call. Knowing a machine made the decision, one furious batter stopped himself from yelling at the human umpire. This shows how automation can de-escalate conflict by shifting blame from a person to an impartial system.