‘Canada has the foundation to lead in artificial intelligence, with world-class research, strong public institutions, and a globally competitive technology sector. The challenge now is execution, scaling AI across government in a coordinated and secure way,’ writes Kelly Hutchinson. / ISTOCK PHOTO

Artificial intelligence is quickly moving from promise to practice in governments around the world. In Alberta, that shift is already underway, with AI delivering measurable results across public services. Alberta’s Minister of Technology and Innovation Nate Glubish is bringing the province’s approach to Ottawa through a hands-on AI hackathon focused on real-world government challenges, signalling a broader shift. If Canada is to grow and compete, the next phase must be execution — deploying AI at scale, embedding it into core services, and delivering measurable outcomes for citizens, sooner rather than later.

Early use cases show AI can reduce costs, improve service delivery, and enable faster, more informed decision-making. In the UK, for example, AI tools are already being used to analyze tens of thousands of public submissions in a fraction of the time, saving an estimated 75,000 days of manual work each year. Yet while provinces and global governments are moving quickly, federal adoption here at home remains fragmented across departments, systems, and mandates.

Canada does not lack AI expertise, but rather a co-ordinated, enterprise-wide approach to scaling it. This creates an opportunity for the Digital Transformation Office to align priorities and accelerate adoption, consistent with Treasury Board Secretariat guidance on centralized capacity, shared tools, and coordinated governance. Delivering on this opportunity will depend on integrating AI into government machinery. Now foundational to operations, AI can enhance productivity by augmenting public servants and streamlining workflows. Real impact, however, will come only when AI is embedded across systems rather than deployed as isolated pilots, supported by an integrated ecosystem of data, governance, infrastructure, and workforce capabilities.

Canadian-founded CGI is playing a critical role in enabling this shift. Through its work with governments in Canada and globally, CGI is helping move beyond experimentation toward enterprise-scale, human-centered AI adoption grounded in real operational outcomes. Their experience demonstrates that successful AI adoption depends not just on deploying new technologies, but on aligning systems, processes, and people to deliver measurable improvements in service delivery and productivity.

The federal government’s AI strategy reinforces the need for a more centralized approach. Recent guidance highlights the value of a common hub to support implementation, share knowledge, and provide access to reusable tools and technical capacity. The Digital Transformation Office outlined in Budget 2025 is well positioned to operationalize this model, ensuring departments are supported by coordinated expertise rather than working in isolation.

At the core of this transformation is a more fundamental challenge. Data. Scaling AI requires a strong data foundation, yet federal data remains fragmented across departments and legacy systems. Without integration and standardization, governments cannot generate the insights needed for enterprise decision-making. This is particularly evident in real property management, where large volumes of data exist but are not consistently connected, despite roughly half already residing within a common platform.

Companies such as Kanata-based Horizant are helping address this gap by enabling integrated, real-time visibility into assets, utilization, and operational performance. By standardizing and connecting data across systems, governments can make more informed decisions on portfolio optimization, workplace planning, and cost reduction. This type of enterprise data visibility is essential not only for operational efficiency but also for enabling AI systems to function effectively at scale. Without a coordinated data strategy, AI risks reinforcing fragmentation rather than solving it.

At the same time, the rapid adoption of AI is reshaping the threat landscape. As governments adopt AI, threat actors are using the same technologies to increase the speed and sophistication of cyberattacks, making cybersecurity central to economic resilience, public safety, and national security. AI introduces new risks, including automated attacks, synthetic identities, and vulnerabilities across digital supply chains. This makes it essential to embed security into every stage of the AI lifecycle, from design to deployment and ongoing monitoring.

Organizations like Palo Alto Networks are at the forefront of this shift, advancing AI-driven cybersecurity capabilities that enable governments to detect, prevent, and respond to threats in real time. Their work reflects a broader move toward integrated platforms that provide visibility across increasingly complex digital environments and help secure AI systems as they scale. This approach underscores the need for a security-first model that integrates AI innovation with strong, built-in protection measures. In practice, Canada must be prepared to fight AI-enabled threats with equally advanced AI-enabled defences.

Canada has the foundation to lead in artificial intelligence, with world-class research, strong public institutions, and a globally competitive technology sector. The challenge now is execution, scaling AI across government in a coordinated and secure way. This will require moving beyond pilots to enterprise deployment, integrated data, and security by design, supported by strong central leadership, including a key role for the Office of Digital Transformation in driving alignment and standards. With continued public-private collaboration and a commitment to evolving its AI strategy, Canada can deliver meaningful results. The question is no longer whether governments should adopt AI, but whether they can do so quickly, responsibly, and at scale for Canadians.