AI should make data better by flagging drift and anomalies before they affect decision-making. Better data should make AI more reliable with governed definitions, traceable lineage, and faster iteration.
In theory, this creates a compounding loop where both sides improve together and returns accelerate.
In practice, most mid-market teams run the loop in reverse. Models fail on poor data, but can’t tell why. Data quality improves, but doesn’t reach the models that need it.
Engineering teams operating at capacity end up spending valuable hours chasing incidents that monitoring should detect on its own.
The loop that should compound returns instead compounds costs; most of it is invisible until executives ask why AI projects are slowing down despite modern data infrastructure. The gap is operational; five specific places where the handoff breaks and costs compound.
In this edition, I will walk you through those gaps and share what you can do about them.
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1. You’re discovering data issues through AI failures (not preventing them)
What’s happening
Data quality issues usually reveal themselves only when dashboards misfire, fraud models overreact, or analysts chase broken joins through the pipeline.
Typical MTTR runs in days; someone notices, escalates, engineering traces pipelines, fixes, backfills, and revalidates.
Meanwhile, every connected system keeps running on bad data.
What you can do
Use AI checks to spot unusual patterns (such as a field that stopped updating or a sudden drop in record volume) and automatically flag them before they reach models.
Pause only the affected table or partition while keeping the rest of the warehouse live with a “data under review” tag. It keeps trust without freezing reports.
Define an SLO for Mean Time to Recover (MTTR); how long it takes from detection to clean data flowing again. Review that metric alongside model performance so the data and AI teams share the same target.
Case in point
Cummins used automated, AI-powered classification to tag more than a million files with sensitivity and usage labels. Governance scaled without proportional headcount, and the AI teams finally trusted that datasets marked “safe for ML” actually met policy and quality bars. The result was fewer surprise incidents at model time and far fewer fire drills.
2. Your numbers don’t agree and nobody owns the result
What’s happening?
“Revenue” is three different things. BI often tracks gross bookings at order time, excluding promotions. Finance reports net revenue recognized after delivery with refunds and rebates applied. Data science trains models on the trailing 30-day gross, sometimes including pending refunds.
The same split shows up in “active customer,” “churn,” “CAC,” “ARPU,” and “conversion.”
Decisions conflict, models learn the wrong target, and every board readout turns into reconciliation instead of action.
What you can change
- Publish contracts for your top ten metrics: the calculation, what is included or excluded, join keys, currency rules, and time windows.
- Back them with end-to-end lineage from source to transform to semantic layer to BI and model features. Gate changes in CI, so a metric cannot ship without its contract and lineage updated.
- Track governance coverage and MTE, the mean time to explain a metric change, and aim for hours rather than weeks.
- A stand-up one-loop dashboard that blends data SLOs, such as freshness, drift alerts, and policy-to-warehouse lead time, with AI outcomes, such as decision yield, p95 decision latency, deflection, and rollback rate.
- Assign named owners and review it on a fixed cadence. Open a post-mortem automatically when thresholds are breached.
Proof it works
Rabobank unified definitions and lineage across business units. Data access fell from days to minutes, and teams could trace a credit risk score from the dashboard back to source fields and rules. Audits were answered in minutes, reconciliation cycles shrank, and change approvals moved faster.
3. Changes go live all at once (no progressive validation)
What’s happening
Analytics and model changes are shipped like a product launch to everyone at the same time.
A single release often bundles multiple edits: a SQL transformation, a metric rule tweak, a feature-pipeline change, and a new prompt or model version.
Most BI stacks lack an actual shadow environment, and many teams optimize for throughput, so there is no side-by-side view and no delta tolerance set in advance.
The result is visible in KPIs jumping without explanation, models behaving differently on the same inputs, and credibility taking the hit.
What you can do
- Simulate first. Run the change on historical data and compare the results with the current version. Keep a short diff report so teams can spot unexpected shifts early.
- Shadow test. Run the new logic or model quietly alongside production to see where results diverge and fix mismatches before users see them.
- Roll out gradually. Start with a small audience, widen only when checks pass. Set clear gates, such as “revenue change ≤ 0.5% unless reviewed,” and keep a rollback switch ready.
Case in point
Majid Al Futtaim retail company cut the time required for customer feedback analysis from 7 days to about 3 hours, saving roughly $1 million a year. Teams shadow-tested changes first, so store managers no longer saw surprise number swings. Confidence rose because every release earned trust before it reached everyone.
What holds back most modernization efforts is the engineering time lost fixing data incidents that AI and automation could prevent. When data pipelines self-check and models flag drift before it spreads, every recovered hour goes back into building features rather than cleaning them up.
To see how AI-driven data modernization actually makes that shift possible, join our upcoming webinar: Data Warehouse Modernization with AI.
