whitepaper

Why ML Pilots Pass Review Then Die in Production.

Inside an 8-month rebuild that turned three failed pilots into a 9:1 ROI model.

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In depth

AI Doesn't Fail at Modeling. It Fails at Feature Pipelines.

Gartner says 85% of AI projects fail.

Most die between training and live serving.

In shortMost die between training and live serving

The model is fine.

The pipeline feeding it is the problem.

In shortThe pipeline feeding it is the problem

Without skew detection, your live model is guessing on bad inputs.

The detail

The 8-Month Rebuild That Made AI Stick.

Fourteen feature pipelines were audited.

Eleven had no SLA. Nine had no monitoring.

In shortNine had no monitoring

The team rebuilt feature reliability, lineage, and training-serving skew checks before re-launching the churn model.

The Result: $2.1M in retained revenue from one model, with two more rebuilds already queued.

In shortwith two more rebuilds already queued

Deep dive

AI Readiness Is Infrastructure, Not Models.

Teams that fix the pipeline first ship models that survive past launch.

AI-ready infrastructure compounds.

Each new model rides the same reliable rails.

Logiciel's AI Readiness Audit grades your feature pipelines, lineage, and validation against production-grade standards in two weeks.

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By the numbers

The figures that make it a board-level conversation.

9:1

ROI

$340K

Investment

Pipelines Standardized

Inside the report

What you'll take away.

Feature Pipeline Reliability

Standardized null handling, SLAs, and quality monitoring per pipeline.

Lineage And Impact

Source-to-feature lineage with schema-change alerts to model owners.

Production Validation

Automated training-vs-serving distribution checks that flag drift early.

Questions

Frequently asked.

Who should read this whitepaper?

What is training-serving skew, exactly?

Why did the team blame the models?

What were the three failed pilots?

How did the rebuild get funded?

Is dbt lineage enough for ML feature pipelines?

How is feature monitoring different from pipeline monitoring?

What does ROI look like beyond the first model?

How long does an AI-readiness rebuild take?

How do I know if my team has this problem?

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Next step

Put this into practice.

Talk through how this applies to your roadmap with our engineering leads - a working session, not a sales pitch.

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