Trust is needed to activate Canada’s AI ambition

Canada’s National Artificial Intelligence Strategy: AI for All, sets out an ambitious vision to position the country as a global leader in Artificial Intelligence (AI). It recognizes that while Canada has world-class research and strong sectoral advantages, the primary barrier to realizing AI’s full value is not innovation, but the ability to translate ambition into sustained, enterprise-scale adoption.

As we wrote in a previous article, organizations are experimenting with AI, but relatively few have embedded it into core operations in a way that delivers measurable outcomes. AI for All explicitly addresses this gap through a set of key actions focused on safety, workforce enablement, sector activation, infrastructure, and ecosystem development.

While these actions are comprehensive, they are also fragmented. Without an integrated operating model, organizations risk pursuing disconnected initiatives that fail to scale. The central insight is clear: Canada does not have an AI innovation problem; it has an AI activation problem.

The activation challenge: bridging strategy and execution

For most organizations, AI is no longer a discretionary innovation agenda—it is becoming a core capability required to remain competitive, compliant, and relevant within the Canadian economy. The strategy creates both an opportunity and an expectation: organizations that can translate policy direction into operational capability will be positioned to lead, while those that cannot risk falling behind as adoption accelerates across industries.

Rather than treating AI as a series of isolated pilots, organizations should focus on end-to-end adoption at scale. This requires clear ownership across the AI lifecycle and tighter integration between business, technology, risk, and compliance functions.

AI for All has the potential to fundamentally reshape the country’s economic trajectory—accelerating productivity, unlocking new sources of growth, and strengthening sovereignty across critical sectors. But, as articulated in the strategy’s first pillar: “AI will only deliver on its promise if Canadians trust it.”

Building trust across the lifecycle  

Trust is not just a constraint on AI adoption—it is the prerequisite for scale.

Organizations must shift to secure-by-design AI, where cybersecurity and privacy are embedded from infrastructure through to model operation. Adding security features after implementation is expensive, inefficient, and insecure.

By embedding governance, controls, and assurance across the AI lifecycle, organizations can unlock three critical outcomes:

• Accelerated adoption, by reducing uncertainty and enabling confident decision-making
• Improved productivity, through accelerated adoption
• Sustainable performance, through continuous monitoring and optimization

To achieve these outcomes, Deloitte advocates for a four-step lifecycle-based model aligned to key actions.

1. Design

Organizations must begin at the level of system design, moving beyond abstract AI strategies toward actionable roadmaps that link investment decisions to measurable business and societal impact.

At the earliest stages of adoption, the Scientific Research and Experimental Development (SR&ED) tax incentives could be a lever to encourage risk taking in AI research and product development. However, fundamental challenges around AI in SR&ED claims include:

• Algorithmic development and AI-assisted coding, particularly by non-engineering contributors (i.e., vibe coders), may be ineligible for SR&ED tax credits.
• SR&ED projects where the business outcome lands in social sciences (e.g., economics or assessing human behaviors) may fall into fields of science that the program may not consider eligible.
• Token, storage, and compute costs might be claimed as overhead, leasing, or capital expenditures under the SR&ED program, but these are still unproven filing positions with no clear guidance from government.

This backdrop may present headwinds to early adopters and act as disincentives in taking the risks needed to begin adoption. Additional clarity from government around how taxpayers can leverage existing incentivization levers in the context of their AI adoption programs would be welcome.

2. Implement

AI for All emphasizes the need to accelerate adoption across sectors, recognizing that value is realized only when AI is embedded into reimagined, real-world processes. Here, the focus shifts to execution: deploy AI solutions into core operations, integrate them with existing systems, and move from experimentation to production.

Government can spur adoption as an anchor buyer. It can also spur adoption through incentive policies that encourage private sector adoption. However, so far, the implementation of these policies has been somewhat uneven and encouraged risk-averse stances:

• AI Compute Access Fund Program: Encourages enterprises with subsidies to scale commercial AI solutions. However, this program has primarily driven subsidies for foreign compute stacks, and only one business has been funded so far.
• ScaleAI: Encourages large enterprises to adopt AI technologies using a consortium model that manages execution risk by design. However, this approach generally produces solutions that are already well proven with much lower execution risks.
• Regional Artificial Intelligence Initiative: Focused on commercializing AI solutions or adopting AI solutions in priority sectors. Because the program is loan-based, projects generally need to be de-risked and commercial.

The business community would welcome more urgency across these programs. At present, the cycle of closed intake applications is followed by long review and negotiation cycles. In the meantime, projects may have changed dramatically.

3. Govern

AI for All's emphasis on standards, certification, and safety reflects a broader recognition that trust must be engineered into AI systems from the outset. Governments can accelerate the adoption of robust AI governance by establishing clear expectations for board oversight, promoting governance standards and disclosure practices, and supporting AI literacy programs for directors and executives.

Consistent with the Deloitte Trustworthy AI framework, our approach focuses on establishing end-to-end governance frameworks that address model risk, bias, explainability, and regulatory compliance. This includes independent validation of AI models, continuous monitoring of performance, and the creation of auditable evidence trails, as outlined in Deloitte’s Strategic AI governance roadmap.

4. Scale

AI for All highlights the importance of cross-sector infrastructure, global alliances, and capital access to scale AI. But achieving this requires more than technology—it requires ecosystems.

These ecosystems include partnerships with hyperscalers, platform providers, and public institutions, as well as alignment with funding mechanisms and policy frameworks. In turn, additional stakeholders add new complexities related to shared infrastructure and data. Trust must extend across these ecosystems.  

How can Deloitte help?

Connect with our leaders today to begin meeting the activation challenge across each of the pillars of Canada’s national AI strategy.