Full Answer

AI preparation isn't about selecting models or hiring data scientists. It's about building data infrastructure that makes AI deployment possible. According to WorkOS analysis of successful AI programs, winning organizations spend 50-70% of their AI budget on data readiness before deploying any AI capabilities. The Backwards-Planning Timeline Most businesses approach AI incorrectly: 1. Decide "we need AI" 2. Hire data science team 3. Discover: no quality training data exists 4. Scramble to build data infrastructure 5. Wait 2-3 years for dataset maturity 6. Finally attempt AI deployment 7. Often fail due to poor data quality The correct sequence: 1. Build server-side tracking and data warehouse infrastructure (Year 0) 2. Accumulate clean, consistent customer behavior data (Years 1-2) 3. Establish data governance and quality processes (Years 1-2) 4. Train team on data analysis and SQL querying (Years 1-2) 5. Deploy AI on mature, high-quality datasets (Years 3-4) AI readiness starts 3-5...