EJQ4 - Spring 2025 - Journal - Page 30
Evaluating the AI options
At 昀椀rst glance, using a publicly available generative AI chatbot might seem like an easy
solution. However, several critical factors prevent their use in our specialized context.
Phase 1 ESAs demand an interpretation of text and graphical data, technical precision,
and professional judgment that generic chatbots do not reliably provide yet. Generic
chatbots may generate plausible-sounding but incorrect and often inconsistent outputs, which could lead to errors in assessments and decision-making. Using generic
chatbots often involves transferring sensitive data to external servers, so we also had
legal and data custody concerns that necessitated a more controlled and tailored approach. We need to safeguard proprietary and client-sensitive information from unintentionally becoming part of a public AI model’s training data and ensure compliance
with regional regulations and industry guidelines. This professional obligation underscores the importance of developing AI solutions tailored to speci昀椀c professional requirements. We also needed any output from the solution to complement our sta昀昀’s
expertise and how they work and be in a format that 昀椀ts into our report template.
Resource requirements
Developing such a specialized AI tool involves signi昀椀cant investment, both in terms of
technology and expertise. In our case, we have a team with a deep understanding of AI,
software development (AI Team) and environmental subject matter expertise (Environ-
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 0
