Quick Facts

Bronson teamed up with the National Research Council of Canada (NRC) to identify, score, and sequence AI opportunities across Canada’s construction standards programs, Codes Canada, the National Master Specification (NMS), and the Canadian Construction Materials Centre (CCMC).

• A custom prioritization framework was built around agreed classification rules and scoring criteria. Each idea was measured against five dimensions: business value, feasibility, scalability, data readiness, and strategic fit.
• A full review of NRC’s technology landscape covered everything from older platforms to Azure cloud services, plus data held in SQL, NoSQL, and XML formats.
• Three deliverables landed at the end: an AI Use Case Management Playbook, a prioritized Backlog Register, and an Architecture Review Summary with a phased roadmap toward AI-ready infrastructure
• Input came from across the organization. Leaders, researchers, technical staff, and operations managers all contributed through interviews and group sessions, helping shape the context and goals for AI adoption.

Project Description

NRC brought in Bronson to lead the scoping, evaluation, and ranking of AI use cases under its Partnership Pathways initiative: a program aimed at bringing artificial intelligence into Canada’s national construction standards.

NRC sits at the heart of Canada’s built environment. The standards it produces shape how buildings are designed, how fire safety is enforced, how plumbing is regulated, how energy performance is measured, and how structural integrity is verified. Codes Canada, the National Master Specification (NMS), and the Canadian Construction Materials Centre (CCMC) carry that work forward, providing the regulatory and technical backbone that the construction sector depends on.

Three forces are reshaping that landscape. Technical content keeps growing. Regulations keep evolving. And demand for digital, accessible standards keeps rising. NRC saw AI as part of the answer — but moving forward responsibly meant first asking two questions: where would AI deliver the most value, and was the existing tech foundation ready to carry it?

The engagement Bronson ran was structured and stakeholder-driven. By the end, NRC had a ranked AI roadmap, a grounded view of its technical readiness, and a clear path into the next phases of feasibility work and implementation planning.

Business Challenge

Plenty of AI ideas were already in circulation across NRC’s teams. What was missing was a consistent way to compare them, rank them, and decide which ones to pursue first. There was also a parallel question: could the current digital and data foundation actually support AI workloads, or did groundwork need to come first?

Five issues stood out:

• A growing list of AI proposals was scattered across programs and domains, with no shared yardstick for measuring business value or feasibility.
• The technology landscape was complex — older platforms, third-party content systems, Azure cloud resources, and a mix of SQL, NoSQL, and XML data sources — and visibility into how it could support AI workloads was limited.
• Analytics and visualization tools such as Power BI needed to be considered alongside AI from the outset, so that operational reporting and business intelligence weren’t left out of the equation.
• Data readiness was uneven across systems. Availability, accessibility, labelling, metadata management, and governance practices varied from one platform to the next.
• Without a structured review of existing internal tools and assets, different teams could easily duplicate each other’s work on parallel platforms.

A more disciplined approach was needed — one grounded in evidence, structured for clarity, and capable of confirming that the technical foundation could carry the highest-value use cases.

Our Solution and Outcome

The engagement ran across two parallel tracks: AI Use Case Scoping and Prioritization, and Target Architecture and Data Readiness Review. Stakeholder participation was wide-ranging, drawing in Codes Canada, NMS, and CCMC alongside NRC’s IT, digital transformation, and AI architecture teams. Structured discovery, collaborative workshops, and technical analysis combined to produce concrete outputs.

Workstream 1: AI Use Case Scoping and Prioritization

• Discovery interviews opened the workstream. Conversations with business leaders, technical specialists, research staff, and operations managers across all three programs surfaced context, ambitions, and pain points.
• Every existing and proposed AI use case was catalogued, then classified against agreed criteria covering operational efficiency, research enablement, regulatory compliance, and innovation potential.
• A prioritization framework was co-developed with NRC stakeholders. Weighted scoring was applied across business value, feasibility, scalability, data readiness, and strategic alignment, and the framework was tested before any rankings were locked in.
• Workshops put the framework into practice. Use cases were ranked openly with everyone in the room, ensuring transparency, stakeholder buy-in, and clear alignment with NRC’s broader business and digitalization strategy.
• The output was consolidated into a structured backlog spanning short-, medium-, and long-term horizons. Quick wins, dependencies, enablers, and risks were flagged for each priority use case.
• One to two top-ranked use cases were recommended for an immediate deep-dive feasibility study and follow-on planning.

Workstream 2: Target Architecture and Data Readiness Review

• A full inventory of NRC’s digital, analytics, and AI architecture was mapped out, covering legacy platforms, commercial solutions, Azure cloud environments, and the relevant SQL, NoSQL, and XML data sources.
• Current-state architecture practices were reviewed across cloud and on-premises infrastructure, data pipelines, orchestration, APIs, security, compliance, and governance controls.
• Data readiness was tested against the prioritized use cases and analytics needs, with focus on availability, accessibility, labelling, and governance.
• A separate sweep examined existing internal tools and assets to identify reuse opportunities and minimize duplication across teams and platforms.
• The current architecture was benchmarked against an AI-ready reference model. Gaps surfaced in compute scalability, MLOps, observability, and reusability.
• Findings and recommendations were validated with subject matter experts and architecture leads, then presented to NRC leadership to secure alignment on priorities and sequencing

Key Deliverables