AI-Ready in Ambition, Not in Data

The manufacturer had scattered AI experiments with no sequencing logic — automating low-value tasks while high-impact bottlenecks went untouched. Spend was rising without measurable EBITA impact, the pattern McKinsey found in 80%+ of adopters. Leadership couldn’t see which initiatives to fund, kill, or sequence first.

The bank had 20+ uncoordinated AI pilots across divisions, none with shared governance, attribution, or ROI discipline. Financial services carries the highest AI failure rate in market at 82%. With the board demanding demonstrated ROI and regulators circling autonomous decisioning, the program risked mass abandonment and a multi-million-dollar write-off.

Leadership had set a mandate to become a data-driven business — but no way to act on it. Reporting was manual, consuming senior analyst capacity on undifferentiated work. Launches lagged competitors by months. And there was no prioritized, costed roadmap, so every AI proposal stalled at the funding gate because no one could defend the business case to the board.

The network wanted AI across diagnostics and intake, but its data sat in fragmented, non-interoperable systems, the exact condition that causes 60% of AI projects to be abandoned.