Context
Enterprise knowledge platform used by manufacturing organizations (automotive components, industrial materials) to capture, structure, and operationalize distributed knowledge across global teams. The system functions broadly as a decision-support layer — aggregating insights, standardizing evaluation, and enabling continuous operational improvement across complex environments.
Challenge & Complexity
Challenge
- Existing workflows captured ideas but did not assist in improving decision quality or execution outcomes
- Unclear where AI would provide real value vs. introduce noise or risk
- Need for controlled, secure integration aligned with enterprise data and privacy constraints
- Highly heterogeneous users (submitters, evaluators, implementers, managers) with different needs at each stage
System Complexity
- Multi-role system with distinct workflows: submission, evaluation, implementation, governance
- High-volume, multilingual inputs across global teams
- Requirement for consistency, traceability, and auditability in decision processes
- Integration constraints: multi-tenant architecture, strict data separation, API-based LLM access
System Design & Intervention
01
Defined where AI should operate within the system — not as a feature layer, but as embedded support across decision workflows
02
Designed a unified, context-aware assistant model adapting behavior based on user role and workflow stage (e.g., submitter vs. evaluator)
03
Structured interaction flows (problem → solution → impact) to improve input quality and standardize downstream evaluation
04
Translated business needs into system-level AI behaviors: clarity rewriting, feasibility signals, similarity detection, action planning
05
Aligned AI behavior with technical architecture (Azure OpenAI integration, no data persistence, optional RAG layer) to meet enterprise security requirements
06
Established phased rollout strategy (foundation → workflow expansion → intelligence layer) to enable controlled adoption and continuous validation
07
Produced end-to-end system models, interaction flows, and functional specifications to guide development and pilot implementation
Solution
A context-aware AI layer embedded across the platform, supporting users at each stage of the workflow — from structured idea submission to evaluation, implementation planning, and system-level insights. The system evolves from assisted input to decision augmentation and organizational intelligence.
Outcome
Shifted the platform from static idea capture to an AI-augmented knowledge and decision system — improving input quality, enabling more consistent evaluation, and laying the foundation for scalable, data-driven innovation across global operations. Supports cost reduction and operational efficiency gains in manufacturing environments.
