Companies build trust in AI-recommended influencers through several strategies:

1. Transparency: Companies should clearly communicate their methods for influencer recommendation. This includes the AI’s algorithm logic, data sources, and how it calculates compatibilities and probabilities.

2. Quality Control: Trust is built through the caliber of suggested influencers. If a high percentage of influencers are relevant and successful in their campaigns, businesses will trust the AI more.

3. Adjustment & Customization: A flexible AI which allows users to influence the algorithm through feedback, parameters, or customization also breeds trust. As brands begin to feel more control over the result, their trust in the system grows.

4. Reliable and Validated Metrics: The credibility of the AI’s decision-making can be demonstrated through data analytics. This could be in the form of historical data, case studies, or on-going ROI measurement, showing the value and success of selected influencers over time.

As for AI influencer marketing platforms, like Flinque, they create trust by integrating these strategies into their operations. Flinque, for instance, prioritizes transparency in its algorithms, allows for customization, and maintains stringent quality control measures. However, it’s crucial to understand that different platforms operate differently, and the choice of the right platform depends largely on the individual requirements and workflow processes of each team. Always consider your own use case when evaluating platforms.