Why your GenAI pilot succeeds, but your production deployment fails - and what to do about it

If you’ve built a GenAI pilot that works, you’re already ahead of most teams.

The demo answers questions correctly.
Latency feels acceptable.
The model seems “smart enough.”

Then you try to ship it to production - and everything starts to fall apart.

Responses slow down.
Answers become inconsistent or flat-out wrong.
Costs spike.
Security and governance suddenly matter.
And the trust you had during the pilot evaporates.

This isn’t because your LLM suddenly got worse.

It’s because operational GenAI changes the rules of how data is accessed, combined, and governed - and most production architectures weren’t built for that.

Pilots succeed because they cheat (and that’s okay)

Most GenAI pilots are intentionally constrained:

• A small, static dataset
• Pre-curated documents
• One or two well-defined use cases
• Friendly users
• No real SLA
• Minimal security constraints

In other words, pilots operate in a controlled environment.

You can:

• Precompute embeddings
• Cache aggressively
• Manually clean the data
• Ignore edge cases
• Accept partial or stale answers

And that’s fine — pilots are supposed to prove possibility, not viability.

The problem starts when teams assume that scaling a GenAI pilot is just an infrastructure problem.

Production GenAI breaks because the data assumptions change

In production, GenAI systems behave very differently from traditional applications.

1. You no longer control the query shape

In a classic app, you know:

• Which API is called
• Which tables are accessed
• Which joins are executed

With GenAI, the user prompt defines the query.

A single question can implicitly require:

• Multiple domains
• Multiple entities
• Real-time and historical data
• Structured + unstructured sources

Context that wasn’t anticipated at design time

Your data architecture now has to handle unpredictable access patterns - something most data platforms were never designed for.

1. Data freshness becomes non-negotiable

In a pilot, yesterday’s data is often “good enough.”

In production:

• “Why did my order get delayed?”
• “What’s the current status of this customer?”
• “Is this user eligible right now?”

Stale context doesn’t just degrade quality — it destroys trust.

Batch pipelines, nightly syncs, and pre-materialized views simply can’t keep up with conversational, real-time inference.

1. Latency compounds fast

A GenAI request is rarely a single call.

It’s usually:

• Retrieve context
• Enrich with entity data
• Apply business logic
• Inject governance rules
• Call the model
• Post-process the result

Each additional hop adds latency.

Architectures that rely on:

• Fan-out API calls
• Remote joins
• Warehouse queries per request

quickly exceed acceptable response times - especially under load.

What felt “fine” in a pilot becomes unusable at scale.

4. Governance can’t be bolted on later

In pilots, governance is often manual or ignored:

• Everyone sees everything
• No fine-grained access control
• No auditability
• No data residency constraints

In production, that’s a non-starter.

GenAI systems must:

• Enforce row-level and entity-level access
• Mask or exclude sensitive attributes dynamically
• Adapt responses based on who is asking, not just what they’re asking Traditional data governance models assume static queries. GenAI produces dynamic, context-driven queries.

That mismatch is one of the biggest reasons GenAI apps fail security reviews.

The uncomfortable truth

Here it is:

If your GenAI app fails in production, it’s probably exposing architectural debt that already existed.

Teams that succeed don’t “optimize the pilot.”
They change how data is delivered.

Instead of asking:

“Where does this data live?”

They ask:

“What defines this customer, order, or user - right now?”

GenAI works best when context is built around business entities, not tables or files.

The takeaway

GenAI pilots succeed because the world is simple.

Production GenAI fails when systems can’t handle:

• Unpredictable questions
• Real-time context
• Entity-level governance
• Low-latency composition at scale

This isn’t an LLM problem.
It’s not even an AI problem.

It’s a data architecture problem — and GenAI is just the first workload that makes it impossible to ignore.

If you want GenAI to work in production, don’t start by swapping models.

Start by fixing how your systems deliver context, trust, and timeliness - because that’s what GenAI actually runs on.