AI for Planning Applications

The vendor required to provide artificial intelligence (AI) for planning applications can enhance the streamlining of workflows, automate routine tasks, improve regulatory compliance, enable data-informed decision-making, and enhance opportunities to support long-term planning and public engagement processes, all of which contribute to a more responsive and transparent planning process.
- By integrating AI into these core functions, the city intends to create a more efficient, responsive, and future-ready planning system including but not limited to:
1. Application review and pre-screening: 
•    Streamlining drawing and document analysis and compliance checks, automating pre-screening tasks to reduce processing times while ensuring accuracy and policy adherence. 
•    Helps applicants understand the planning process and provides instant compliance feedback on responses. 
2. Data-driven decision-making: 
•    Using analytics for scenario modelling and GIS integration to empower data-driven insights, enabling planners to anticipate impacts and evaluate development options effectively. 
3. Workflow optimization: 
•    Automating routine tasks such as data entry and offering real-time tracking to enhance process efficiency, reduce circulation distribution times, and provide applicant transparency. 
4. Improved stakeholder collaboration: 
•    With the right tools and data, AI and automation can enhance spatial analysis and visualization opportunities to better inform the public, facilitate long-term urban planning by analyzing growth patterns and forecasting needs. 
•    IT can also enhance compliance and risk management and support sustainable urban growth. 
5. Improved service delivery: 
•    Use of AI technology to create smarter, faster, cost-effective and high-quality services. 
•    This can increase value-added work, provide opportunities to improve services based on data and trends collected, provide better modelling analysis, and improve financial management. 
6. Enhanced transparency: 
•    The improved processes will strengthen transparency by enabling applicants to track the status of their responses, identify potential areas of improvement, and gain clarity on requirements. 
•    This fosters a more open and predictable planning experience.
- The city is particularly interested in gaining insight and information on solutions that address the following functional areas:
1. Application intake and validation
•    Intelligent document recognition and data extraction from submitted materials
•    Automated completeness checks and validation against submission requirements to reduce manual review and improve first-time submission quality
2. Policy and regulation compliance
•    Natural language processing (NLP) tools to apply zoning bylaws and official plan policies
•    AI-driven rule engines to assist with the assessment of applications for compliance with municipal and provincial regulations such as the act and the code.
3. Decision support and analysis
•    Analytics for application outcomes, approval timelines, and identify potential risks
•    Machine learning models to analyze historical data, detect trends, and support proactive planning decisions
4. Visualization and public engagement
•    AI-enhanced modeling and scenario planning tools to support public consultation and stakeholder engagement
•    Chatbots or virtual assistants to provide applicants and the public with real-time guidance and information
5. Workflow optimization
•    AI based task routing and workload balancing to improve internal coordination and reduce circulation times
•    Integration with existing planning systems, permitting platforms, and GIS tools to ensure seamless data flow and operational efficiency.
- Critical use cases
•    Automated application document review and validation - AI tools automatically analyze submitted documents (e.g., site plans, reports, drawings, forms) to verify completeness, identify missing elements, and ensure alignment with submission requirements. 
•    Automated zoning and official plan policy compliance checks - natural language processing (NLP) engines define information from the zoning bylaws, official plans, and building codes to assess whether applications meet regulatory requirements. 
•    Automated building code review - AI systems analyze building plans to check for compliance with the code. 
•    Analytics for application outcomes - machine learning models that analyze historical data, approval timelines, identify high-risk applications, and forecast potential issues. 
•    Intelligent workflow routing - AI classifies building applications by complexity and routes them to appropriate reviewers or departments based on expertise and workload. 
•    Conversational AI for applicant support - AI-powered chatbots provide real-time assistance to applicants, answering questions, guiding them through submission steps, and offering status updates. 
•    AI enhanced public engagement tools - AI integrates with interactive visualizations (e.g., 3d models, GIS maps scenario simulations) of proposed developments to support public consultation and feedback. 
•    Data driven insights for strategic planning - AI analyzes trends in application types, geographic distribution, and approval rates to inform policy development and long-term planning. 
•    AI-assisted growth forecasting - AI models generate localized population and employment forecasts by analyzing development activity, economic and demographic trends, and incorporating official plan land use policies and zoning by-law considerations to support long-term planning over a 30-year horizon.

- Questions/Inquires Deadline: April 28, 2026