AI readiness: How to assess and improve

Artificial intelligence (AI) has become a mainstream capability, but the Boston Consulting Group reports that 74% of companies have yet to show tangible value from their AI initiatives due to inconsistent, undocumented, or untrustworthy data. To improve AI readiness, organizations should conduct assessments focusing on data quality, infrastructure scalability, team alignment, and governance. Reliable data management includes ensuring high-quality testing, documentation, automation of manual tasks, and maintaining data lineage. The Analytics Development Lifecycle (ADLC) and tools like dbt provide a framework to support these processes by offering structured, governed transformation layers that facilitate collaboration and accountability. By integrating modern data practices and ensuring consistent workflows, organizations can overcome common barriers to AI success and ultimately capture measurable business value from their AI projects.

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