Diagnostic Tool
The Post-Hype Diagnostic
Is Your AI Initiative Grounded in Reality?
A 42-point diagnostic from Engineering Reliable AI Agents & Workflows
The Problem
95% of enterprise AI pilots fail to deliver business value. Not because the technology doesn't work—but because organizations skip the foundational work.
What goes wrong:
- • Stakeholders expect transformation in weeks (it takes 6-12 months)
- • No one budgets for human oversight teams
- • "Success" is defined as "better experience" instead of measurable outcomes
- • Data quality issues surface after $200K is spent
- • Feedback loops don't exist or take weeks to close
What this diagnostic does: Surface the gaps in problem definition, technical readiness, and organizational alignment before you spend budget. Ten minutes now saves six months of explaining failure later.
How It Works
Go/No-Go Protocol
Five prerequisite questions. All must be "Yes" to proceed. One "No" = stop and fix that gap first.
The Scorecard
14 criteria scored 0-3 across three areas. Total possible: 42 points.
Your Zone
Your score places you in one of four zones. Each zone has specific next-step recommendations.
The Assessment Areas
Part 1: Problem-Solution Fit
Does this problem actually need AI?
Evaluates whether your use case benefits from probabilistic AI—or whether you're using a sledgehammer on a thumbtack.
- • Uncertainty tolerance and failure definitions
- • Human performance baselines
- • Error tolerance during the learning curve
Key Question: Probabilistic Tolerance: Does this use case benefit from a system that "guesses," or does it require 100% precision?
If you need 100% accuracy—financial calculations, regulatory reporting, safety-critical decisions—you probably need traditional software, not AI.
Part 2: Technical Reality
Can your systems and data support this?
"We have data" isn't enough. You need the right data, properly labeled, with documented edge cases.
- • Data quality and labeling standards
- • Integration complexity and feedback loop speed
- • Data sovereignty and compliance requirements
Key Question: Edge Case Documentation: Have you identified the "weird" edge cases that will confuse the model?
Your AI's failure modes are predictable. Teams with a "Golden Dataset" of tricky edge cases before development have far higher success rates.
Part 3: Organizational Readiness
Is your organization set up for AI success?
This is where most initiatives actually fail.
- • Stakeholder expectations and solution selection rigor
- • Shadow AI awareness and failure budget
- • Human oversight resources and escalation paths
Key Question: Stakeholder Expectations: Do stakeholders understand this is a 6-12 month journey, not an overnight transformation?
Executives expecting "transformation" in 8 weeks will kill your project—even if the technology works. Misaligned expectations create impossible pressure.
What Your Score Tells You
Your total places you in one of four zones:
Zone 1: Critical gaps
Do not proceed.
Zone 2: High risk
Address lowest-scoring areas first.
Zone 3: Solid foundation
Phased deployment recommended.
Zone 4: Ready
Ready for systematic execution.
The complete diagnostic includes zone thresholds, detailed definitions, and specific recommendations for each outcome.
Who Should Use This
Evaluating technical and organizational foundations
Deciding whether to greenlight AI features or redirect resources
De-risking budget allocation before major investment
Assessing integration complexity and data readiness
Planning human-in-the-loop processes
Validating whether an AI budget request is grounded in reality
Run this as a team.
Schedule 90 minutes with your technical lead, a business stakeholder, and someone who does the work you're automating. Disagreements on scores reveal misalignments that will derail you later.
Frequently Asked Questions
What is an AI project risk assessment?
Why do most enterprise AI initiatives fail?
How do I know if my organization is ready?
What's the difference between AI hype and reality?
Should I assess before or after selecting a vendor?
Download the Complete Diagnostic
Get the full 42-point diagnostic with Go/No-Go questions, scoring criteria, zone thresholds, and recommendations.
What you get:
- ✓ 5 Go/No-Go prerequisite questions
- ✓ All 14 scored criteria with detailed guidance
- ✓ Zone thresholds and definitions
- ✓ Recommendations for each outcome
- ✓ Printable worksheet format
- ✓ Notes template for team sessions
Related Diagnostics
From the Book
This diagnostic is one of seven tools in Engineering Reliable AI Agents & Workflows. The book adds case studies, the complete "Post-Hype Audit" with security criteria, and frameworks for closing the gaps this diagnostic reveals.
Learn more about the book →