Why AI Initiatives Stall at the Data Boundary
Most AI projects don't fail because of the model. But for one specific class of use case, anything needing current transactional state from your systems of record, legacy batch architecture is a hard constraint no model change can solve. This whitepaper shows you how to tell the difference, and what to do when it's the one you have.
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Figures are reproduced with their original qualifying conditions. Full references are included in the paper.
What's inside
What this whitepaper covers
A technical guide for data, platform and AI leaders: how to diagnose whether legacy workloads are what's blocking your AI use case — and the engineering interventions, trade-offs and measurements that follow if they are.
What the failure evidence actually says
The three most-quoted AI failure statistics, read against their primary sources — including the qualifying conditions that are usually dropped, and what they do and don't support.
The latency budget
Every AI-supported decision has a deadline. A worked decomposition of a nightly extract path shows why the variance and dependencies matter more than the headline number.
Point-in-time incorrectness
Why batch extracts that overwrite current state destroy the history needed for correct training data — and why the resulting failure looks like a modelling problem, not an infrastructure one.
Training-serving skew from mixed freshness
How hybrid estates produce features with different staleness profiles offline and online, why that degrades production models, and the control that makes it measurable.
A differential diagnosis
Six questions that establish whether this constraint is actually binding your use case — including the answers that should send you somewhere else entirely.
Interventions, trade-offs & a 90-day sequence
Log-based change capture, decision-time API access, critical-path batch re-engineering and freshness contracts — with the costs each carries and when not to do them.
Before you download
Is this the problem you have?
This paper makes a deliberately narrow argument. It's worth knowing whether it applies to you.
Read this if
- An AI use case needs data fresher than your batch cycle delivers
- A model performs well offline and worse in production, with no code change
- You can't reconstruct a feature's value as of an arbitrary past timestamp
- Core data sits on mainframe or other batch-oriented systems of record
This isn't your constraint if
- Your decision deadlines are comfortably met by the current cycle
- No business owner is accountable for the decision the model supports
- The blocker is adoption, framing or scope rather than data availability
- You're looking for a general case for modernization — this paper doesn't make one
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