AI Readiness Self-Assessment

for Groundwater & Environmental Consulting Organizations
Shared by Reset & fill in your own answers
The people who oversee your professional license — NSPE, ASCE, NCEES — have already weighed in on AI in engineering practice. The question is whether your organization has caught up.

Answer each statement based on what's true for your organization today. No right answers — the value is in the picture these questions paint together.
Risk Management

AI Usage Awareness

Our organization knows which AI tools staff are currently using for work tasks.
Personal AI accounts used for client work is a known and managed risk.

Policy & Governance

Our organization has a written AI usage policy.
There is a designated person or team responsible for AI governance.

Data Hygiene & Security

Staff understand which types of data are safe (and unsafe) to share with AI tools.
There is a process for reviewing AI-assisted deliverables before they reach clients or regulators.

Vendor & Tool Management

AI tool terms of service and data retention policies are reviewed before adoption.
We maintain a list of approved AI tools with defined use cases and limitations.
Capability Development — a preview of where the course takes you

AI Literacy & Judgment

Staff have been trained on the difference between deterministic and probabilistic AI.
There is clear guidance on when AI assists vs. when licensed professional judgment must lead.

Technical Depth

Our organization uses AI beyond basic chat — structured prompting, knowledge-base integration, or agentic workflows are in use or being evaluated.

Integration & Impact

AI is embedded in key workflows and we track its impact on project quality, efficiency, or outcomes.
Your responses help us improve the course and software, and may be used to contact you for followup discussion. We never sell or share your data.
Enter your email and click View Results to see your profile.
12 questions · radar chart · personalized recommendations
Score: / 24
DimensionScoreLevel

What your score means

Discuss with your team

1. Where in your work could AI create the most value — and what's stopping you from trying it?
2. What have you tried with AI that didn't deliver the results you were hoping for?