Human Centered AI: Ep.007 - Validation Architecture, Not Validation Effort

Validation Architecture, Not Validation Effort | Human Centered AI Ep 007

Deloitte Australia delivered a $440,000 AI-assisted report. The client discovered fake citations, non-existent authors, and books that were never written.

This isn't about criticizing Deloitte - they're tackling what we're all facing. How do you validate AI output without destroying the speed advantage?

The Speed Paradox
AI generates a 100-page report in 3 hours. Human validation takes 2 weeks.
You can't slow back to human speed (defeats the purpose). You can't trust blindly (Deloitte proved that costs $440,000).

So what's the answer?

In This Episode:
→ What actually broke at Deloitte (and why it's a process problem, not a technology problem)
→ Why LLMs are eloquence engines, not truth engines
→ The validation architecture we use for AI-assisted reports
→ How to build checkpoints that preserve speed advantage
→ Why transparency about AI use becomes competitive advantage
→ Managing AI agents vs. managing humans (completely different principles)
→ Four implementation guidelines you can use immediately

Key Insights:
The validation bottleneck is real. If you're reading every word, you're back to human speed with added risk.
Transparency must come first. The AI conversation happens before the project, not after someone finds hallucinations.
Speed without checkpoints is just risk. Build validation milestones throughout creation, not just at the end.

Our Approach:
- Declare sources first (set boundaries or you'll get books that don't exist)
- Cross-validate patterns, not sentences
- Build checkpoints throughout (like data packets - check key milestones, not every byte)
- Human expertise where it matters (evaluate output quality, not proofread words)

Three Questions for Your Practice:
- What's your validation framework that doesn't require reading every word?
- Have you told clients HOW you use AI before they discover it themselves?
- Are you validating during creation or only after?

How you validate matters more than how much you validate.

Deloitte paid $440,000 for this lesson publicly. Learn it here for free.

RESOURCES:
📊 AI Future Signals 2025 Report (with full methodology): https://www.designthinkingjapan.com/#futuresignals