AI infrastructure decisions should start from the business workflow. National AI compute programs and cloud-provider credits can help, but they do not replace a clear project, trusted data, risk controls, and a measurement plan.
Compute is part of the plan, not the starting point
AI infrastructure decisions should follow the workflow. A small customer-support knowledge base, a manufacturing quality workflow, a document review process, and a multi-agent operations system have different needs for data access, latency, privacy, human review, and cost control.
- Workflow fit: what work should AI help with first?
- Data boundary: what documents, systems, or records can be used safely?
- Tool choice: does the project need a workspace assistant, RAG layer, automation, cloud AI service, or custom build?
- Governance: what needs human approval, logging, policy, or quality review?
- Cost control: what usage, hosting, storage, and support costs will continue after launch?
Cloud and funding paths to consider
Depending on the project, a business may need to compare public cloud services, Canadian data-residency requirements, vendor tools, internal systems, available credits, financing, DMAP/TDP planning, BDC LIFT readiness, SR&ED evidence, or a smaller self-funded pilot.
The practical question is not which program sounds largest. The practical question is which path matches the project stage, evidence, budget, and adoption risk.
Where Digid fits
Digid helps Canadian SMEs assess the workflow, choose an AI pattern, review funding and cloud paths, define governance, and build a first implementation that can be measured by the business.
Funding, credits, financing, and program outcomes are never guaranteed. Third-party providers and program owners control eligibility, pricing, terms, approvals, and availability.