AI initiative registry
Every initiative listed in the registry can be consulted here, without an account. Each record shows its progress, its milestones and the history of its decisions. Requests still under triage do not appear: they join the registry once reviewed.
Automation and Decision Support
AI that helps make or automate decisions, often using rules, predictions, or pattern recognition.
What is “Automation and Decision Support”?
This category includes AI systems that analyze structured data, recognize patterns, and support or automate decision-making processes. These systems are often built around predictions, classifications, or recommendations and are used to improve operational efficiency, mitigate risks, or optimize outcomes.
| Subcategory | Description | Examples |
|---|---|---|
| Predictive analytics | Using data to forecast trends or risks | Fish stock modeling, equipment failure prediction |
| Prescriptive analytics | Recommending actions | Conservation policy optimization, vessel routing |
| Robotic Process Automation (RPA) + AI | Automating rule-based digital tasks with AI augmentation | Document classification, triaging emails |
| Optimization | Finding the best outcome under constraints | Resource allocation, scheduling patrols |
- Key indicator
- If the AI’s goal is to make or inform a decision, suggest a course of action, or automate a rule-based task — it likely fits here.
- Focus
- Pattern recognition, statistical modeling, optimization, or rule-based logic, often on structured data (spreadsheets, telemetry, logs).
Filter 1
54 current initiative(s)
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121
Develop a deep-learning model to age capelin otoliths: application for stock assessmentInvestigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determine the age of capelin in the 2J3KL stock in Newfoundland for use in stock assessments. Otolith aging is currently done visually using a microscope, this proposed method would automate that process to save time and resources, while reducing human error and bias
Pilot Ecosystems and Oceans Science Automation and Decision Support
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120
Characterizing areas of convergence in the Saint John HarbourThis project is using a technique called self-organizing maps, which is an unsupervised machine learning algorithm, to identify patterns in areas of convergence near the Saint John Harbour. The preliminary analysis suggests that these patterns are related to different environmental conditions such as river discharge and tidal phase. The work has been conducted in collaboration with scientists from the Maritimes Region and is currently on hold.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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117
PolyDocs: Format and Translate for CSASA modular, bilingually-optimized (EN/FR) application that automates the formatting, quality assurance, and standardization of complex Word documents to meet Government of Canada publishing requirements. The tool orchestrates a highly configurable, low-code assembly line that handles deep document cleanup (resolving fragmented text, localizing punctuation/number conventions, and rebuilding tables of contents), applies localized terminology glossaries, and reduces file sizes for accessibility.
Production Ecosystems and Oceans Science Automation and Decision Support
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116
BinderBot: A Smart Planning BinderEmploys AI-driven extraction to identify and compile the most relevant data from technical documents for the planning binder. Streamlines the planning binder process by automatically assembling targeted, information ready for executive review. Saves valuable administrative time and ensures that binder materials are both comprehensive and user-friendly.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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113
Improving forecasting for high-priority portsResearch into feasibility of improving accuracy and timeliness of short-term forecasts for coastal regions by combining traditional and AI modelling methods.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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108
The development of rapid multiplex qPCR for the detection and quantification of three parasites MSX, SSO, Dermo of oysters (Crassostrea virginica) and MSX single-cell whole genome sequencing using long read sequencing platform’This 2-year project aims to enhance the disease diagnostic services provided to the shellfish aquaculture industry by improving the methods for detecting economically important Oyster parasites, enhance understanding of Multinucleate Sphere Unknown X (MSX) virulence dynamics, and support the creation of new vaccine in investigating future outbreaks and modeling through machine learning.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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99
Improved freshwater runoff modelling in Eastern Canada to drive ocean modelsUse Neural Networks to post-process streamflow simulations from the WRF-Hydro model, improving their agreement with observational data. These neural networks are applied as a downstream calibration step. Builds synergy between traditional calibration methods and modern AI techniques in hydrologic modeling.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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98
Guidance on Optimal Timing for Environmental DNA (GOTeDNA)Provide guidance on optimal eDNA sampling periods and standardized sampling procedures for assessing and monitoring coastal species using eDNA. GOTeDNA, a centralized interactive online tool currently in development, will report/visualize trends in spatio-temporal eDNA distributions.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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83
Daily UseMapping, The AI Assist function in FME has been used to support code development for data processing while integrating and manipulating data in workbenches.
Pilot Programs Sector Automation and Decision Support
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75
Study on the use of Artificial Neural Networks to Predict the Return Timing and Northern Diversion Rate for Migrating Fraser River Sockeye SalmonThe Machine Learning feasibility study compliments the statistical approach by asking whether Artificial Intelligence (AI) methods can be developed to predict salmon behaviour as functions of ocean conditions. classical machine learning models, including linear regression, Ridge regression, and Random Forest
Ideation Ecosystems and Oceans Science Automation and Decision Support
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70
Data Validation Tool (See B6, B22)Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Llama 3.0 Large Language Model
Pilot Ecosystems and Oceans Science Automation and Decision Support
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69
Species distribution modelingSpecies distribution models for various coral/sponge/groundfish Random Forests
Pilot Ecosystems and Oceans Science Automation and Decision Support
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65
Comprehensive marine substrate classification applied to Canada’s Pacific shelfWe built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives and oceanographic predictors using Random Forests. Random Forests
Pilot Ecosystems and Oceans Science Automation and Decision Support
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61
CTD data QC using MLWorking with CDOS office to streamline our CTD QC using ML cnn and others
Pilot Ecosystems and Oceans Science Automation and Decision Support
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59
Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental marginWe introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent environmental conditions (temperature, oxygen, acidity), and define the extremes based on the historical statistics of variability of each of these fields. unsupervised clustering with k-means
Pilot Ecosystems and Oceans Science Automation and Decision Support
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56
Method Recommendation EngineA large language model reviews a scientist’s objective for collecting data in the field and based on the criteria, the model will provide the best procedure to use for their work
Ideation Programs Sector Automation and Decision Support
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54
PII & Sensitivity ScoresDetermines the sensitivity of the content of a document based on a trained model and allows for the redaction of the sensitive material in the document. Presidio
Ideation On hold Programs Sector Automation and Decision Support
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45
MyGCHR – Phoenix analysis toolPro B data would be needed, but feed MyGCHR and Phoenix records, but flag those that are incorrect in the hopes of training the AI to recognize future incorrect entries and to help clean up pay files for employees. P&C is in negotiation currently with PSPC to ‘own’ our backlog of Phoenix cases, so this could be a big help.
Ideation Human Resources and Corporate Services Automation and Decision Support
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41
Cyber Security OperationCyber Security Operation Centre has deployed AI/ML in MS Defender (Endpoint Detection and Response (EDR) system), MS Sentinel (Security Incident and Event Management (SIEM) system), Data Lost Prevention (DLP), and Exchange Online Protection (EOP) for real-time threat detection.
Production Chief Digital Officer Automation and Decision Support
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31
Quality control of oceanographic data - Maritimes RegionThe goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models to predict quality flags for the CTD data products generated by Maritimes region, enabling faster data processing by reducing manual burden. Initial explorations have been conducted to investigate transferability of existing quality control models from Pacific region. Based on investigation results, new models will be trained specific to Maritimes region. Client: Maritimes Region - Ocean Data
Pilot Strategic Policy Automation and Decision Support
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27
Turn on AI Auto labeling on all M365 documents and communications channelsOur E5 M365 licences grant access to AI based tools in Purview to automatically categorized documents and set labels accordingly. Model training would require attention of an Security/IM expert to make perfect. We may want to create specific AI specific labels to avoid conflicts with officials ones.
Ideation Chief Digital Officer Automation and Decision Support
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24
Automated Identification of Vessel Fishing ActivityPiloting use of vessel AIS data with AI to predict probable fishing activity and provide near real time alerts to officers, aiming to boost compliance coverage and response. Action plan for this initiative: 1. Governance: Supervised under compliance analytics team, reporting to C & P management. 2. Guardrails: Apply strict controls (no personal data), validate accuracy in live pilots, monitoring for unintended use.
Pilot Programs Sector Automation and Decision Support
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17
Recruitment and staffing toolsRecruitment and staffing tools (Mark Jarvis) (existing agents created by Microsoft to be explored further)
Ideation Chief Digital Officer Automation and Decision Support
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15
MyGCHR Phoenix analysisMYGCHR Phoenix analysis tool (Mark Jarvis) to flag incorrect records and recognize future incorrect entries to help cleanup pay files
Ideation Chief Digital Officer Automation and Decision Support
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13
PoC in Power Apps, Power Automate, and SharePointPoC in Power Apps, Power Automate, and SharePoint (Mark Jarvis) to have an efficient helpdesk; AI to review questions and predict templates and carry out actions (e.g., reset accounts, forward others to MyPay etc.)
Ideation Automation and Decision Support