72% of AI projects fail to reach production. The reason is almost never the AI — it is data quality, architecture gaps, or an unclear use case. This assessment tells you exactly where you stand.
72%
AI projects fail to reach production
$180k
avg cost of a failed AI integration
6 wks
avg Heimatverse AI delivery
Data Readiness
Up to 25 points
Do you have labelled, domain-specific data for the task you want AI to perform?
Is your data accessible via an API or structured database (not locked in spreadsheets/PDFs)?
How much relevant data do you have for training or retrieval?
Architecture Readiness
Up to 25 points
Is your existing system modular enough to add an AI component without a full rebuild?
Do you have API access to your core business logic (not just the UI)?
Can you isolate the AI component for independent testing and monitoring?
Team Readiness
Up to 20 points
Do you have someone on your team who can prompt engineer, iterate, and evaluate AI output quality?
Is your engineering team comfortable with non-deterministic outputs and probabilistic systems?
Use Case Clarity
Up to 20 points
Can you describe the specific decision or action the AI needs to take in one clear sentence?
Do you have a concrete way to measure whether the AI is performing well (beyond "it feels smarter")?
Budget & Timeline Realism
Up to 10 points
What is your realistic budget for the initial AI integration?
Already know you want to build?
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