Implementing AI in data analytics is a major undertaking that requires careful planning and consideration. Here are some key factors that companies should take into account:

Key Considerations:

1. Data Quality: Ensuring the quality and integrity of data is crucial as AI models rely heavily on data for accuracy and reliability.
2. Technical Infrastructure: The company needs to have the right infrastructure in place to support AI capabilities, including hardware, software, and networking capabilities.
3. Talent and Skills: Implementing AI requires a team with the right skills. This might include data scientists, data engineers, AI specialists, and business analysts.
4. Ethical Considerations: The use of AI should align with ethical guidelines, especially when it comes to data privacy, security, and bias.
5. Clear Objectives: The company should have clear objectives for what they want to achieve with AI in data analytics. This includes identifying key performance indicators (KPIs).
6. Regulatory Compliance: The company must comply with all relevant regulations related to data handling, AI, and privacy.
7. Integration with Existing Systems: The AI solution should be able to integrate seamlessly with existing systems to prevent data silos and streamline operations.
8. Vendor Selection: If opting for an off-the-shelf solution, the choice of vendor is crucial. Consider factors like reliability, customer support, scalability, and cost.

Implementation Strategy:

1. Needs Assessment: Start by identifying the business needs that AI can help address. This will help you define clear objectives for your AI implementation.
2. Data Audit: Conduct a data audit to assess the quality and quantity of your data. Identify any gaps or issues that need to be addressed.
3. Infrastructure and Team Preparation: Prepare your technical infrastructure and assemble your AI team. You might need to hire new talent or upskill existing staff.
4. Vendor Selection: If you're buying an off-the-shelf solution, research different vendors and select one that fits your needs and budget.
5. Development or Implementation Phase: If you're building a custom solution, this is the development phase. For off-the-shelf solutions, this is the implementation phase.
6. Testing and Validation: Test the AI solution thoroughly to ensure it's working as expected. Validate the results it's providing to ensure accuracy.
7. Deployment: Once tested, deploy the AI solution. This might be done in stages, starting with a pilot phase.
8. Monitoring and Continuous Improvement: Regularly monitor the performance of the AI solution and use the insights gained to improve and optimize it.
9. Training and Change Management: Train staff on how to use the AI solution effectively. Manage the change carefully to prevent resistance and ensure a smooth transition.

Remember, implementing AI is a journey, not a destination. It requires ongoing effort and adjustment as your business needs, and the AI landscape, evolves.