Most enterprises don't have a data problem.
They have a decision problem.

A multi-format retailer planned replenishment in Excel. Each planner owned a region, used their own adjustments, and nobody could reconstruct why a number changed between Friday and Monday.

We rebuilt the feed from POS and DC systems into a lakehouse, defined SKU–store demand as one governed metric, and trained hierarchical models with promo, festival and weather regressors. Planner overrides were kept — but logged, attributed and measured against the model.

Forecast error fell from 19.4% to 8.9% MAPE at store–SKU–week. More importantly, planners moved from producing numbers to reviewing exceptions, and the Monday planning meeting dropped from three hours to forty minutes.

Loan files arrived as scanned PDFs, phone photographs and email attachments. Verification took an average of 26 hours of elapsed time, most of it waiting rather than working.

A document pipeline classifies each page, extracts fields with confidence scores, cross-checks them against bureau and internal records, and routes only genuinely ambiguous cases to a human queue. Everything below the confidence threshold escalates — silence is never treated as success.

78% of files now clear without human touch. The audit trail shows every extraction, every confidence score and every override, which turned out to matter more to the risk committee than the speed gain.

Junior doctors spent roughly ninety minutes a day writing discharge summaries from structured records they had already entered. Two earlier vendor attempts were abandoned because the output needed more correction than writing from scratch.

Retrieval strictly limited to that patient's own record, with every generated line traceable to a source field. The assistant drafts; the clinician edits and signs. We deployed inside the hospital's own tenancy with no patient data leaving the boundary, and built an evaluation set of 400 reviewed summaries before go-live.

Median drafting time fell to 21 minutes. Adoption held at 84% after six months — the number we actually care about, since the previous attempts died at week three.

Machine data sat in three historians with inconsistent tag naming. Maintenance ran on a fixed calendar, so healthy machines were stripped down and failing ones were not.

We built a tag dictionary and streaming layer first — twelve of the twenty weeks went into that, which is the part most proposals skip. Failure models came afterwards, per asset class, with alerts landing in the existing CMMS as work orders rather than in a new dashboard.

Unplanned downtime fell 24% across the three plants in the first two quarters, and maintenance moved from calendar-based to condition-based on the top 60 assets by criticality.