Impact assessment is facing new pressures, more data, tighter timelines, and growing regulatory scrutiny. As demands increase, so does the need for clearer, more defensible decisions. In this Q&A, Michael Taylor, Vice President of Ecology, EIA and Heritage for BC/Yukon at WSP in Canada, shares how expert-led, AI-enabled approaches can help organizations navigate complexity, strengthen confidence, and move from analysis to action. 

Q1. What pressures are reshaping impact assessments right now? 

Michael Taylor:  Globally, there is growing pressure to accelerate major projects and reduce the time it takes to move from assessment to delivery. In many jurisdictions, including Canada, that has triggered discussion around streamlining processes and removing unnecessary barriers. 

The real challenge, however, is figuring out how to do that without compromising environmental and social protections. Strong guardrails still matter, and right now, regulators and project teams are working through how to balance those protections with the desire to move faster. 

For proponents and regulators alike, this can create pressure to deliver assessments that are not only quicker, but also credible and defensible, while still meeting structured requirements around engagement, data, and review timelines. That balance hasn’t been fully resolved yet, which is where new approaches and tools are starting to come into focus.

Q2. Where do impact assessment processes tend to break down today? 

Michael Taylor: Lengthy timelines are a core reason impact assessment processes struggle today. When assessments extend over many years, projects may inevitably change—but the systems used to manage and assess impacts often don’t keep pace. 

As a result, teams may need to revisit the same information, update multiple disconnected documents, and realign decisions every time a project shifts. This creates duplication, repeated effort, and growing uncertainty about what information is current or has already been reviewed. 

Over time, that compounding rework can make it harder to keep information, expectations, and trust aligned across proponents, regulators, and stakeholders—turning impact assessment into an exercise in managing delays and inconsistencies, rather than focusing on clearly understanding and addressing impacts. 

Q3. Why can turning environmental data into clear decisions be so difficult?  

Michael Taylor: The volume and diversity of data are the biggest challenges. Information comes from spatial datasets, reports, grey literature, field observations; it’s scattered across sources that weren’t designed to work together. 

Collecting, screening, and synthesizing that information takes time, and it can slow down progress when teams are under pressure to deliver clear outcomes quickly. The real difficulty isn’t just finding data; it’s understanding what matters and how it applies to a specific project. 

Q4.  In impact assessment, where does AI add real value, and where is caution needed?  

Michael Taylor: The strongest value is at the front end: collecting and synthesizing data more efficiently. AI can help identify relevant information, summarize large volumes of material, and organize it into structured, usable categories in a matter of minutes rather than weeks. 

At the same time, caution is essential. AI shouldn’t be making judgments on its own. Trust in the data, clear guardrails, and structured human review are critical. Without that, there’s a real risk of misinterpretation or over-reliance on automated outputs.

Q5. How can AI improve consistency and defensibility without replacing expert judgment? 

Michael Taylor: AI helps by making it easier to introduce structure and consistency into impact assessment processes. When assessments follow a common framework across projects, it becomes easier to manage change over time—preserving decision history, assumptions, and rationale even as project details, requirements, or conditions evolve.

That structure can reduce the need to redo work when projects shift, help teams understand what has already been assessed, and make it clearer how and why decisions were made. As a result, assessments can be easier to update, easier to explain, and more defensible. 

At the same time, every project is different. Context still matters—site‑specific conditions, community considerations, and client risk tolerance can’t be captured by data alone. That’s where expert judgment remains essential. AI can surface patterns and efficiencies, but people are still needed to identify gaps, apply context, and make informed decisions. 

Q6. What does a more modern, decision‑ready impact assessment workflow look like in practice?