Automotive Supplier AI Readiness Sprint
12-month roadmap in 3 weeks
Client Context
A Tier-2 automotive supplier with legacy ERP systems and limited digital maturity. The management team sees competitors adopting AI but has no internal expertise to evaluate feasibility.
The Challenge
The company wants to know if AI-driven demand forecasting and quality inspection are feasible with their existing data infrastructure. They need answers quickly — their largest customer is beginning to require AI-driven quality documentation.
Our Approach
Our 3-week sprint includes data profiling across ERP and quality management systems, identifies critical data gaps, recommends a phased data lake implementation, and maps a 12-month journey from data remediation to production AI.
Timeline: 3 weeks
The Results
- 12-month phased implementation roadmap delivered within 3 weeks
- Critical data gaps identified with a concrete remediation plan
- Data lake architecture designed for the specific ERP landscape
- Quick-win identified: quality inspection PoC feasible within 8 weeks
- Business case ready for first-phase investment approval
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