The article suggests that the first step to develop deeper trust in AI solutions is to broaden the understanding of AI utility. Transparency about how a system works can lead to greater trust and increased usage. It also highlights successful AI implementations, such as Google Ads and Netflix, which use AI to deliver personalized content based on user behavior. The author concludes by emphasizing the importance of evaluating AI's accuracy against desired results and the need for marketing and data science teams to work together to achieve this.
Key takeaways:
- AI adoption is increasing across businesses, but trust in AI is crucial for its successful implementation and usage. This trust can be undermined by AI mistakes, as seen in examples from Memorial Sloan Kettering Cancer Center and Zillow.
- Understanding how AI comes to its conclusions and having transparency about how a system works can lead to greater trust and increased usage.
- AI has been successfully harnessed by organizations like Google Ads and Netflix, which use AI to personalize user experiences, leading to increased engagement and business success.
- When linking AI prediction with a business outcome, it's important to evaluate the AI's accuracy against the desired results. This requires collaboration between marketing and data science teams.