Why Your Enterprise AI POC Will Die in Production (And How to Prevent It)
Real stories from Barclays-scale deployments on the gap between demos and systems processing 100M transactions daily.
"It worked perfectly in the demo!" - Famous last words before another £2M AI project died three weeks into production. The POC processed 1,000 documents flawlessly. Production hit 1 million on day one and the system melted.
This isn't a cautionary tale. It's Tuesday in enterprise AI.
The POC-to-Production Death Valley
After years of delivering production systems at enterprise scale, the pattern is clear: Beautiful POC, executive buy-in, big budget approved... then reality hits like a freight train.
The Graveyard Statistics:
- 87% of enterprise AI POCs never reach production
- Of those that do, 70% fail within 6 months
- Average waste: £3-5M per failed deployment
- Career casualties: Usually the CTO who championed it
Why POCs Lie to You
1. The Clean Data Delusion
POC: "Here's our curated dataset of 1,000 perfect examples."
Production: 50 million records where 30% have encoding issues, 20% are duplicates, and someone stored JSON in a VARCHAR field.
Real production query from a bank: SELECT * FROM transactions WHERE amount = 'NULL' OR amount = 'null' OR amount = 'Null' OR amount = '' OR amount = ' ' OR amount = '\N' OR amount = 'N/A' OR amount = 'NA' OR amount = '-'
2. The Latency Reality Check
POC: "GPT-4 gives amazing results!" (3-second response time)
Production: Your mainframe batch job has a 200ms SLA. For 10 million transactions. Per hour.
Math check: 3 seconds × 10M transactions = 347 days to process one hour of data
3. The Token Economics Surprise
POC: "Only $50 in API costs for our test!"
Production: That scales to $2.5M per month at production volume.
Real monthly LLM costs at enterprise scale: $500K-$1M+
The Hidden Killers Nobody Talks About
Memory Leaks at Scale
Your beautiful stateless POC becomes stateful in production. Context windows, conversation history, vector caches - they all eat RAM. We've seen systems that work fine for 8 hours then OOM because nobody tested a full trading day.
Fix: Implement aggressive context pruning and external state management from day one.
The Compliance Time Bomb
POC: "Look how well it answers questions!"
Auditor: "Show me the decision tree for trade #4827291."
You: "Well, it's a neural network..."
Auditor: "Production shutdown. Now."
Fix: Build explainability and audit trails before the first line of model code.
Integration Hell
Your POC talks to a nice REST API. Production needs to integrate with:
- SOAP services from 2003
- Mainframe CICS transactions
- FTP file drops (yes, really)
- That proprietary protocol Steve built in 1998
Fix: Test with production systems from week one. Not replicas. The actual systems.
The Production-First Checklist
Here's what we do differently at SOO Group, learned from deploying at Barclays scale:
Week 1: Reality Check
Week 2: Scale Testing
Week 3: Integration Reality
The Brutal Truth About Costs
Let me share real numbers from a recent enterprise deployment:
POC Budget vs Production Reality: POC (1 month): - OpenAI API: $500 - Infrastructure: $1,000 - Total: $1,500 Production (1 month): - OpenAI API: $847,293 - Infrastructure: $124,892 - Monitoring: $23,478 - Backup/DR: $45,231 - Security/Compliance tools: $34,892 - Total: $1,075,786 Scaling factor: 717x
This isn't an outlier. This is typical when you go from processing hundreds of requests to millions.
How to Actually Succeed
1. Start with Production Constraints
Don't build a POC then try to scale it. Build a production system and scale it down for the POC. Every architectural decision should assume 1000x current volume.
2. Hybrid Architecture from Day One
LLMs for complex reasoning, traditional ML for predictable patterns. We typically see 80% cost reduction by routing appropriately. Don't use GPT-4 to classify yes/no.
3. Build the Boring Stuff First
Monitoring, logging, audit trails, error handling, retry logic, circuit breakers. The AI is 20% of the system. The "boring" 80% is what keeps it running at 3 AM.
4. Test with Chaos
Kill servers. Corrupt data. Simulate network partitions. Add 10-second latencies. Your POC should survive everything production will throw at it.
What Success Actually Looks Like
When you build production-first, here's what's possible:
Production-First Results:
- →Systems that scale from POC volume to 10,000x without architecture changes
- →Linear cost scaling instead of exponential explosions
- →99.9%+ uptime from day one, not after 6 months of fixes
- →Audit trails that satisfy regulators on first review
- →Teams that can maintain the system without the original builders
The Bottom Line
Your POC will die in production unless you build it as a production system from day one. There's no "we'll fix it later." Later never comes.
Build like it's going live tomorrow. Because in enterprise, it usually does.
Building an AI POC? Let's make sure it survives.
Talk to engineers who've deployed at enterprise scale. We'll save you from the graveyard.
Schedule a Production Reality Check