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Why 90% of AI Consultants Never Deploy to Production

The uncomfortable truth about POC builders vs production engineers in enterprise AI.

12 min readSOO Group Engineering

"We've built an amazing POC! The client loves it! 98% accuracy!"

Six months later: Project cancelled. Zero production deployment. Consultant moved to next POC.

Sound familiar? Here's why this pattern repeats endlessly.

The POC Industrial Complex

After working with dozens of consultancies and seeing the aftermath of their "successful" POCs, the pattern is clear. The consulting model is fundamentally broken for production AI.

The Ugly Numbers:

  • 90% of AI POCs never reach production
  • Average consultant has deployed 0 production systems
  • $50M+ wasted annually per Fortune 500 on dead POCs
  • 18-month average from POC to "let's try something else"

The Consultant Playbook (And Why It Fails)

1. The Demo-Driven Development

Consultants optimize for the wow factor, not production reality.

POC Version:

  • Cherry-picked data that shows 98% accuracy
  • Runs on consultant's laptop
  • Manual data preparation hidden
  • No error handling ("we'll add that later")

Production Reality:

  • Real data drops accuracy to 67%
  • Needs 24/7 uptime across regions
  • Data pipeline more complex than the AI
  • Error handling is 80% of the code

2. The Handoff Hell

"Here's our POC. Good luck productionizing it!"

What gets delivered:

  • Jupyter notebooks with hardcoded paths
  • No documentation beyond PowerPoints
  • Dependencies: "It worked on my machine"
  • Security: "What security?"
  • Scalability: "Just add more servers"

3. The Incentive Misalignment

Consultants get paid for POCs, not production success.

Consultant Success Metrics:
āœ“ Client impressed in demo
āœ“ Next POC lined up
āœ“ Case study published
āœ— System running in production
āœ— Actual business value delivered
āœ— Long-term maintenance plan

The Skills They Don't Have

What Consultants Know

  • Latest ML papers
  • Jupyter notebooks
  • Python libraries
  • PowerPoint
  • Demo narratives

What Production Needs

  • System architecture
  • DevOps/MLOps
  • Security hardening
  • Performance optimization
  • 24/7 operations

The gap is massive. And it's why their POCs die.

Real Examples of Consultant Disasters

Pattern 1: The Unscalable Demo

Common failure mode we see repeatedly:

  • POC handles 10 concurrent users beautifully
  • Production needs 1,000 concurrent users
  • Architecture fundamentally can't scale
  • Consultant response: "Just add more servers"
  • Reality: Complete rebuild required

Typical outcome: 6-12 month delay, 3x budget overrun

Pattern 2: The Cherry-Picked Data

Another classic consultant move:

  • POC uses carefully curated "golden" dataset
  • Production data is messy, incomplete, inconsistent
  • Model accuracy plummets in real world
  • No data quality pipeline built
  • No plan for handling edge cases

Typical outcome: System unusable, team loses faith in AI

How to Spot POC Builders vs Production Engineers

Questions That Reveal Everything:

1. "Show me your monitoring dashboard from your last deployment"

POC Builder: "We focused on the model accuracy..."
Production Engineer: *Opens Grafana* "Here's our p99 latency over the last quarter"

2. "How do you handle model drift?"

POC Builder: "We recommend retraining quarterly"
Production Engineer: "Automated drift detection triggers retraining pipeline, here's the architecture..."

3. "What happened at 3 AM when your last system failed?"

POC Builder: "Our POCs don't run at night"
Production Engineer: "PagerDuty alerted, auto-failover kicked in, root cause was..."

4. "Walk me through your security controls"

POC Builder: "We use HTTPS"
Production Engineer: "RBAC, encryption at rest, key rotation, penetration test results..."

The Production-First Approach

Here's how we do it differently at SOO Group:

Start with Production Constraints

  • Day 1: Set up monitoring and logging
  • Day 2: Security scan and compliance check
  • Day 3: Load testing framework
  • Day 4: Now we start the AI part

Build for Operations

  • Every feature includes operational metrics
  • Error handling before happy path
  • Deployment automation from the start
  • Documentation written for ops team, not data scientists

Success = Running in Production

  • No POC is complete until it handles real traffic
  • Incremental rollout with rollback capability
  • SLAs defined and monitored
  • Handoff includes runbooks and training

What to Demand from Your AI Partner

Non-Negotiable Requirements:

  1. Production experience: Show me systems you've deployed that are still running
  2. Ops involvement: Your ops team meets their engineers day 1
  3. Incremental delivery: Working system every 2 weeks, not a big bang
  4. Performance guarantees: SLAs in the contract
  5. Knowledge transfer: Your team can maintain it alone
  6. Production metrics: Success measured by uptime, not accuracy

The Real Cost of Failed POCs

It's Not Just Money:

Opportunity Cost

While you're playing with POCs, competitors deploy real systems

Team Morale

Engineers tired of building demos instead of products

Executive Trust

Each failed POC makes the next approval harder

Technical Debt

POC code that somehow becomes "temporary production"

How We're Different

We're engineers who've deployed systems processing millions of transactions. We've been on-call at 3 AM. We've explained failures to regulators.

āœ“ Every POC runs in production within 30 days

āœ“ We stay until it's stable (not until the contract ends)

āœ“ Success = your team running it without us

āœ“ We've been burned by consultants too. We know better.

The Bottom Line

Stop funding POC theater. Either build for production from day one, or don't build at all. The era of six-figure PowerPoints needs to end.

Real AI value comes from systems that run 24/7, not demos that run once.

Tired of POCs that go nowhere?

Let's build something that actually ships. Production guaranteed.

Talk to Production Engineers
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