EJQ4 - Spring 2025 - Journal - Page 32
output was left blank by the AI-assisted solutions for the Environmental
Team to interpret.
text and making nuanced decisions, especially when data gaps exist, human oversight remains essential.
The AI-assisted solution was ultimately successful in signi昀椀cantly reducing the time required to process extensive data sets. Preliminary results
show a reduction of approximately 50 per cent in the time spent reviewing the Ecolog reports. For example, from eight hours to four hours on a
small site, and from 40 hours to 16 hours on a more complex site. This ef昀椀ciency gain allows our sta昀昀 to allocate more time to analysis and interpretation rather than data processing.
Key takeaways and guardrails
Our AI-assisted solution currently focuses on text and it cannot yet interpret drawings, 昀椀gures, spreadsheets, and numbers. To do so would
require integrating other AI models, which we plan on exploring. For example, the solution cannot interpret Figure 1 above, which tells a reader
where and how far a record is relative to the Phase 1 property. There is a
nuanced level of interpretation that experienced environmental practitioners would apply, such as assessing whether an activity is up- or
down-stream of the property, the impact of local geology, presence of
utilities and waterbodies, and the transport nature of contaminants associated with the records. Given the limitations of AI in understanding con-
This was a fruitful exercise on applying AI to a specialized technical application
and we learned several key lessons:
COLLABORATION IS ESSENTIAL: Successful implementation of AI in
specialized technical applications requires close collaboration between AI
experts and subject matter experts. Real humans with real knowledge of both
technical expertise and domain knowledge are critical to developing solutions
that are e昀昀ective and reliable. At minimum, you need one software developer
with AI expertise that understands how to develop and integrate software to
deliver outputs in a format that is consistent and useful for technical users and
one committed technical expert that understands the subject matter.
QUALITY DATA AND ITERATIVE TESTING IS ESSENTIAL: The team needs to
provide high-quality data, review outputs, and provide feedback to support a
rigorous iterative testing process. Subject matter experts should review outputs
and provide feedback on how they intend to use them, which helps 昀椀ne-tune
E N V I RON M E N T J OURN A L QUA RT E RLY RE PORT • S PRI N G 2 0 2 5 • P AGE 3 2
