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Chapter 4 : How To Use AI Data Solutions

Based on case studies, where AI data solution is used as in a real-world example of a customer using it, we have put up recommendations.

Please review important aspects and key considerations on How To Use AI Data Solutions

To effectively utilize AI for data solutions, start by clearly defining your objectives and assessing your data needs.

Define Objectives and Assess Data​

  • Identify areas for improvement:
    • Determine specific business problems or inefficiencies where AI can offer solutions, such as customer targeting, inventory management, or fraud detection.
  • Assess data availability and comliance :
    • Evaluate the data srources, tables and accessibility of your data. Ensure the data that will be accessed by AI is accurate and compliant with data privacy regulations.

Prepare Data​

  • Prepare and Transform Data:
    • Select tables to use
    • Define data models to bring relevant data together
    • Build transformation tables for aggregates and summaries

Integrate​

  • Integrate with existing systems:
    • Ensure seamless integration of AI solutions with your existing and legacy systems.

Key Considerations​

Data privacy and security​

Prioritize data privacy and security throughout the entire AI implementation process.

caution

Consider removing or blocking AI access to sensitive data such as:

  • Sensitive data
    • e.g. financial information like bank account details and credit card numbers, health records, and biometric data like fingerprints, etc.
    • Passwords, birthdates, home addresses, and phone numbers.
  • PII (Personally Identifiable Information)
    • e.g. names, phone numbers, addresses, identification details, government-issued ID card details
  • Confidential Business Information:
    • Trade secrets, strategic plans, and internal communication.
  • Data revealing sensitive personal information: Racial or ethnic origin, etc.

Dangers of AI for data solutions: How To Mitigate Risks​

Please refer to below blog post as similar considerations apply for AI Data solutions. We feel readers can draw parallels between data solutions aspects from Ecommerce solutions aspects.

Shopify Blog. "Dangers of AI for Ecommerce: How To Mitigate Risks." Shopify Blog, accessed 28 May 2025, https://www.shopify.com/in/blog/dangers-of-ai.

Examples of AI in Data Solutions​

  • Amazon: Uses AI to analyze customer behavior and recommend products.
  • Coca-Cola: Leverages AI-driven platforms for marketing and sales analysis.
  • Financial institutions: Employ AI for fraud detection and risk management.

Getting the most out of AI​

  • Build and Iterate:
    • Build incremently, deliver on your AI solutions and iterate.
  • AI Tasks categories
    • AI Data Insights
    • AI Transactions
    • AI Workflows
    • AI Integrations
tip

Give context of your table schema and, Leverage GenAI to recommend and get a list of what AI tasks from above categories can be implemented to benefit your business. Then choose from list with human-in-loop review.

  • AI Tasks and solution module bundling
    • It is better to build smaller AI solution modules executing a small set of AI tasks rather than one giant solution.
    • Recommended is: individual AI solutions modules, each based on 5-10 tables and mostly using data modeling, table joins relations, rather than individual table data access.
  • Evaluate AI models to use:
    • Rather than building custom models, consider leveraging free and premium LLMs avaialble that can be readily used.
    • Try different models for your ai task to see which one performs better.