While AI holds immense potential for data analytics, it also presents a set of challenges and risks. Here are some and how they can be managed:

1. Data Quality: AI algorithms are only as good as the data they analyze. Poor data quality can lead to inaccurate insights or predictions. This can be managed by implementing rigorous data cleaning and preprocessing practices.
2. Data Privacy: AI often requires large amounts of data, which might include sensitive information. Inadequate handling could lead to privacy breaches. This can be addressed by applying stringent data protection measures, adhering to data protection laws, and anonymizing data when possible.
3. Bias and Discrimination: AI models can perpetuate or even amplify existing biases in the data. This can lead to discriminatory or unfair outcomes. Mitigation strategies include using diverse datasets, auditing AI models for bias, and implementing fairness measures.
4. Lack of Interpretability: AI models, especially complex ones like deep learning, can be "black boxes," making it difficult to interpret their decisions. This can be addressed by using "explainable AI" approaches that make the decision-making process more transparent.
5. Over-reliance on AI: There's a risk that decision-makers may overly rely on AI, ignoring human intuition or expertise. This can be mitigated by maintaining a balance between AI-driven insights and human judgement.
6. Cost of Implementation: Deploying AI for data analytics can be costly and require significant resources. Organizations can start small and scale up, demonstrating value at each step to justify the investment.
7. Skills Gap: There can be a lack of necessary skills within the organization to leverage AI effectively. This can be managed by providing ongoing training and development, or by hiring new talent with the necessary skills.
8. Regulatory Compliance: Depending on the sector, organizations may face regulatory constraints around the use of AI and data. These should be identified and addressed upfront to ensure compliance.

Despite these challenges, the benefits of using AI in data analytics often outweigh the risks, especially when they're proactively managed. Organizations that successfully navigate these challenges stand to gain a competitive advantage through more effective and efficient use of their data.