Concise problem → outcome; light AI & Python where they add leverage. Simulation narratives — illustrative.
35 dashboards with inconsistent data, high maintenance costs, and no ownership model.
→ Standardised data asset layer; less maintenance, more analyst capacity—Python for pipelines; light AI (NL query, drift hints) on governed metrics.
COVID-19 broke all historical forecast assumptions simultaneously.
→ Python-backed driver + ML layer kept 90-day cash visibility when 63% of peers couldn’t forecast 6 months out; gen-AI optional for exec-ready scenario blurbs.
Fragmented ERP inputs caused data inaccuracy cascading through every budget review.
→ $400,000 in hidden costs surfaced; cycles shortened—rules plus Python scoring and a touch of AI to flag suspicious postings; humans sign off.