Have a round table of discussion and let everyone know that they have to back what they say with tangible evidence whether it be data or patterns of history and so forth. The division will subside and a clear view point will be established.

Handling differing opinions on data requires fostering open dialogue and mutual respect. I encourage team members to present their perspectives, back arguments with data, and collaboratively develop evaluation criteria. When needed, I seek a neutral expert's view for an unbiased resolution.

When your team is divided on AI data analysis results, start by fostering open communication. Encourage a data-driven discussion, where team members present their interpretations and back them with evidence. Google often uses this approach, ensuring all viewpoints are heard before reaching a consensus. Establish clear, consistent metrics for evaluating AI results to avoid subjective interpretations. At Netflix, standardized performance metrics ensure everyone evaluates AI data through the same lens. Finally, consider running additional tests or analyses to clarify ambiguous results. By validating data and aligning on shared goals, you can bring the team together and ensure everyone is on the same page.

Make sure everyone on your team agrees on the results of the AI data analysis, hold a meeting where everyone can share their thoughts and questions. ? Review how the data was analyzed so that everyone understands the process. ? Use clear charts and graphs to show results, and encourage team members to ask questions. ? Discuss what the results of your work mean and try to reach a common agreement. ? Write down key points and action steps so everyone has a reference. Plan to check back on the review later as more information comes in, which will help keep everyone connected.