Legacy systems not fully integrated with modern data platforms
Limited cloud scalability for enterprise AI workloads
Early automation tools in place but not standardized across teams
Gaps in monitoring and maintaining reliable pipelines for experimentation
Inconsistent data definitions across departments.
Limited maturity of governance processes for AI/ML-specific use cases.
Early frameworks for security and compliance, but uneven adoption.
Lack of standardized policies for data accessibility and sharing.
AI awareness exists but is uneven across functions.
Few leaders with deep technical understanding of AI/ML.
Limited cross-functional collaboration on AI initiatives.
Training programs are ad hoc and not tied to enterprise strategy.