How to Transition from Legacy Enterprise to AI-Native in 90 Days

Adapt or Die

Transformation does not take 2 years anymore. You have 10 years of data sitting in a SQL database doing nothing. Let's wake it up.

The 90-Day Timeline

Days 1-30: Foundation

Data hygiene and vectorization. Clean your databases, standardize formats, and embed your most valuable documents. This is not glamorous but it is essential. Everything else depends on data quality.

Key actions:

• Audit all data sources and map what exists
• Clean and standardize the top 20% of data (that drives 80% of value)
• Set up a vector database and begin embedding critical documents
• Establish data quality metrics and monitoring

Days 31-60: Internal Tooling

Internal tooling and "Chat with Data" pilots. Build tools that let your team ask questions of your data in plain language. Start with one department (usually ops or customer support) and prove the value.

Key actions:

• Deploy a RAG system connected to your knowledge base
• Build internal dashboards powered by AI summaries
• Train two "champion" users per department
• Measure time saved and accuracy improvements

Days 61-90: Customer-Facing Features

Customer-facing features. Take what you learned internally and expose it to customers. Personalized recommendations, intelligent search, automated support — whatever creates the most value for your specific product.

Key actions:

• Launch one customer-facing AI feature
• Set up monitoring and evaluation
• Gather user feedback and iterate
• Plan the next 90-day cycle

The Reality

The biggest risk is not moving too fast. It is moving too slowly and letting competitors build the future while you maintain the past.

We act as the injection of speed to get you through this timeline without disrupting current operations.