Federal impact assessment agency officials will use artificial intelligence to prioritize regional data analysis, allowing more time for Indigenous and public consultations.
The Impact Assessment Agency of Canada outlines a transition to ChatGPT, custom internal code, and automated report analysis in its departmental plan.
The Impact Assessment Agency of Canada (IAAC) is rolling out generative artificial intelligence tools, including ChatGPT Enterprise and Microsoft Copilot, in a bid to eliminate administrative backlogs and curb the cost of commercial software subscriptions.
According to the agency’s 2026–27 departmental plan, the integration will be managed by its newly established “Impact Lab.” The specialized internal unit is tasked with testing and deploying AI-assisted workflows that automate routine documentation, transcribe meetings, and analyze voluminous stakeholder feedback.
A primary financial objective of the strategy is reducing the agency’s digital footprint and technology overhead. By using AI to build custom, license-free code internally, the IAAC plans to decrease its reliance on commercial low-code and Software-as-a-Service (SaaS) platforms. Federal officials project that these technical efficiencies will lower licensing costs, minimize employee overtime, and reduce the agency’s dependence on temporary staffing contracts.
The automated tools will also handle routine tasks within the administration of public funding programs and budget management. Progress on these fronts will be measured through tracked reductions in processing times and direct operational cost savings.
Beyond internal accounting, the technological pivot is expected to alter how the agency executes its core regulatory mandate. Regional and strategic assessments require the collection of massive data sets spanning environmental, economic, social, and health factors. The agency intends to use AI to scan these data landscapes and detect emerging patterns, shifting the focus of human staff to high-value analytical work and long-term planning.
The plan notes that automating manual information gathering will directly benefit relationship-building with external partners. Specifically, the agency expects field teams to spend less time summarizing meeting notes for internal briefings and more time working directly with Indigenous groups involved in assessment processes.
To encourage public engagement, the IAAC will pilot AI solutions designed to translate complex technical findings into plain-language summaries. By identifying core themes and tailoring public materials to diverse audiences, the agency aims to remove traditional informational barriers to regulatory participation.
To address transparency concerns surrounding algorithmic governance, the IAAC is introducing a human-centred AI framework. The policy mandates data readiness controls, structured employee training in both official languages, and the use of standardized language in public records to explicitly disclose whenever AI tools have been used to draft or edit federal documents.
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