Canada’s Public Service Data & AI Challenge: Longlisted Ideas


  • Eight innovative projects are longlisted in Canada’s Public Service Data/AI Challenge, focusing on data-driven solutions for federal government services.
  • The challenge includes proposals like an AI-enhanced language training tool and a digital twin for smarter infrastructure management to improve decision-making.
  • A generative AI tool aims to streamline application processing for Indigenous children under Jordan’s Principle, reducing workload and processing times.
  • Financial forecasting and risk assessment tools are also proposed to enhance government spending efficiency and project management.
  • Last year’s winners showcased the effective use of data to solve real-world issues, demonstrating the challenge’s impact on public service improvement.

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Agentic AI: The Future of Public Service Efficiency


  • Emerging Technology: Agentic AI is becoming a transformative system capable of independent reasoning, planning, remembering, and acting without ongoing human oversight, enhancing efficiency and service quality in software systems.

  • Organizational Readiness: At GovInsider’s Festival of Innovation 2025, no audience members felt prepared for agentic AI adoption, despite widespread recognition of its potential benefits, as highlighted by survey results.

  • Human-AI Collaboration: In the public sector, agentic AI can work alongside human roles, such as facility managers, to streamline processes and provide time-efficient solutions by autonomously diagnosing and addressing issues.

  • Strategic Frameworks: A strategic assessment of an organization’s goals and capabilities is crucial for effective AI deployment, emphasizing the need for integration, quality data availability, and alignment with business objectives.

  • Workforce and Regulatory Preparation: Leaders stress the importance of fostering a culture of readiness and regulatory frameworks to ensure successful agentic AI adoption, highlighting the need for clear value understanding to avoid ineffective implementations.

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