From digital twins to AI financial forecasting: staff ideas longlisted in Canada’s Public Service Data and AI Challenge
Eight projects have been longlisted in Canada’s latest Public Service Data/AI Challenge, which gathers data-related ideas from the federal government workforce and develops the best ones towards implementation.
The longlisted ideas include a digital twin for smarter infrastructure management, a financial forecasting tool for government spending, and an AI-driven solution to simplify data-sharing across national trade corridors.
The Challenge, organised by Global Government Forum in partnership with Statistics Canada and Natural Resources Canada (NRCan) and now in its third edition, identifies promising ideas for data-based reforms and services and helps move them into implementation.
The longlist was selected from a record 151 entries to the Challenge and these projects will now be taken forward by interdisciplinary, cross-departmental teams of public servants. They will present their work to the judging panel at the semi-final in summer, ahead of the final in December.
The panel comprises top data leaders from across government, including Elise Legendre, chief data Officer, Agriculture and Agri-Food Canada; Gabrielle FitzGerald, executive director, chief data and risk officer, Canada Food Inspection Agency; Erica Ren, chief data officer, Immigration, Refugees and Citizenship Canada; and Wesley Yung, director, International Cooperation and Methodology Innovation Center, Statistics Canada.
Read more: Delivery driver: how the Canadian Data/AI Challenge makes data dreams come true
The proposals were put forward by public servants working in a wide variety of roles and incorporate technologies such as generative AI, AI analytics, chatbots, natural language processing and metadata solutions for use cases in areas including staff training and job classification, children’s services, and risk management in government projects.
“This is a fantastic way to develop your own skills and career while improving the lives of Canadians,” said Stephen Burt (left), Canada’s chief data officer and one of the programme’s three champions, speaking at the Challenge launch late last year. “Those putting forward the best ideas will get to carry them all the way through into delivery.”
The longlisted projects in this year’s Public Service Data/AI Challenge, which is supported by knowledge partners IBM, Dell and Nvidia, are:
- Language competency evaluation and training tool: Using AI to enhance second-language training for public service employees, this tool provides real-time assessment, personalised feedback and adaptive learning. It generates custom practice questions and explanations to reduce training costs and improve success rates.
- Digital twins for smarter infrastructure management: A digital twin of the Samuel De Champlain Bridge and highway corridor will be used to monitor structural health, traffic flow and environmental impact using real-time data. By integrating AI-powered analytics with geospatial data, this virtual model seeks to enhance decision-making, enable predictive maintenance and improve emergency response planning, with potential for wider federal use.
- Application processing tool to support Indigenous children: A generative AI solution to streamline the intake and triaging of applications submitted under Jordan’s Principle, which ensures First Nations children receive essential services. The AI tool will extract and categorise key information from diverse document formats, reducing processing times and manual workload.
- ‘Tell-us-once’ approach for national trade corridors: This initiative introduces an AI-driven solution to simplify data-sharing across national trade corridors, reducing redundant submissions and improving interoperability. Standardised data formats and real-time validation aim to enhance efficiency, lower administrative burdens and strengthen Canada’s trade competitiveness.
- Financial forecasting for government spending: Building on successful AI-driven forecasting models used at Housing, Infrastructure and Communities Canada, this tool seeks to transform financial planning across federal departments. By automating and improving grants and contributions forecasting, the model is designed to enhance accuracy, reduce processing time by 66%, and minimise budget lapses – potentially saving billions in taxpayer dollars.
- Enhanced job classification evaluation in the federal public service: The federal government’s classification system assesses job descriptions to determine salary levels, but the manual process is time intensive. This project proposes testing an AI chatbot trained on thousands of job descriptions and classification standards to support advisors in analysing and summarising job evaluations.
- Metadata and meta-tagging for scientific data management: This initiative leverages AI to automate metadata creation and enhance meta-tagging, streamlining scientific data management and improving discoverability. It aims to foster collaboration, reduce duplication and maximise the value of government-funded research.
- Risk and requirements advisor for government projects: By analysing historical project data, AI will provide proactive risk assessments and mitigation strategies, while also improving requirements gathering through NLP-driven extraction and automated quality checks. The goal is to reduce costly project delays, improve decision-making, and ensure more effective resource allocation.
Bob Conlin, managing director, federal government, IBM Canada, said: “At IBM Canada, we’re thrilled to see the longlist of the Government of Canada’s Public Service Data/AI Challenge, which reflects a wonderful spectrum of practical use cases designed to boost Canadian productivity and empower responsible data utilisation. We’re eager to collaborate with the Government of Canada, Global Government Forum, and other esteemed partners to bring these innovative ideas to life. Together, we’re committed to fostering a data-driven and AI-powered public sector that serves Canadians with excellence and integrity.”
Find out more at the Public Service Data Challenge website.
Real-world impact
After a research and development phase, the longlisted teams will pitch their ideas to the judging panel. Those shortlisted then embark on further development work before meeting the judging panel again at the final.
Last year’s Public Service Data Challenge was won by a team working to combine data held in two separate NRCan datasets to understand how money spent under the Green Homes programme has improved domestic energy efficiency – dramatically improving the information available to public funders and homeowners on how best to cut energy bills and carbon emissions.
Burt said that the project demonstrated “the incredible potential of data to solve real-world problems”.
Meanwhile, Canadians are already benefiting from the winning idea from the first Data Challenge in 2023. The AI-powered AgPal chatbot was born of an idea submitted by policy analyst Jay Conte. The project was supported through to implementation and became the Government of Canada’s first public-facing generative AI tool.
Read more: Data Challenge serves as an engine of innovation for Canada’s public services
About Sarah Wray
Sarah has over 15 years’ experience as a journalist with a specialism in the public sector and topics such as digitalisation and climate action. Sarah was formerly the editor of Cities Today and Smart Cities World, as well as a specialist video-based publication in the aerospace sector. She has also written for publications including Smart Cities Dive, Mobile Europe, Mobile World Live and Computer Weekly.
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