Artificial intelligence is becoming a routine tool inside federal immigration operations, and a newly formalized AI strategy sets clearer guardrails for how it can be used without undermining fairness, privacy, or public trust. The strategy positions AI as a support to staff, not a replacement for human judgment, especially in immigration contexts where outcomes can be deeply personal and high impact. It also aligns departmental governance with the wider AI Strategy for the Federal Public Service 2025-2027, signalling a more consistent, government-wide approach to responsible adoption.
Three-tier framework: where AI is allowed, and where it stops
The strategy organizes AI adoption into three categories based on benefit, risk, and proximity to decision-making. This structure is important for applicants because it clarifies what AI may do in day-to-day processing and what it is not supposed to do.
The three categories are:
- Everyday (low risk): AI supports administrative tasks that are not part of decision-making.
- Program (medium risk): AI informs program operations and decision support in targeted ways.
- Break new barriers (high risk if adopted): AI is used for controlled experimentation, not broad deployment.
Across all categories, the strategy reinforces that AI should not operate autonomously in a way that refuses applications. It also states that tools do not refuse, and do not recommend refusing, any application. The most sensitive part of the Program category is the idea of flagging straightforward, low risk files for expedited officer decision. That wording matters because it implies faster routing may occur, but the final decision remains with an officer.
Examples of what AI may do in each category include:
Everyday tasks
- Triaging applications
- Creating summaries
- Producing documents
- Responding to client enquiries
Program productivity support
- Identifying anomalies
- Matching data
- Making assessments and recommending options
- Flagging straightforward, low risk files for expedited officer decision, with officer verification
Break new barriers experimentation
- Using AI-powered and adjustable predictive analytics to model immigration flows and forecast impacts on the economy
From an immigration consulting perspective, this framework usually rewards applications that are easy to verify and consistent across forms and evidence. When triage and anomaly detection tools are involved, unclear narratives, mismatched dates, and loosely supported claims tend to trigger manual review and longer processing, even when the applicant is eligible.
Operational scale: the numbers behind automation and client service
The strategy provides several statistics that show how long AI and automation have been used and how widely these tools already affect intake and client communications.
Key dates and scale points include:
- The department states it has used AI since 2013, when an advanced analytics centre was created.
- Senior management approved AI-based models in 2017 to generate if-then rules for automation to manage rising temporary resident visa volumes.
- Since 2018, predictive analytics enabled by machine learning has been used to generate insights from past decisions to support triage design and workload distribution.
- Based on internal data, more than 7 million applications have been assessed by this automation approach.
- A client email triage system developed in 2020 is now used in the Client Support Centre and in more than 50 overseas offices, triaging about 4 million emails annually.
- A rules-based chatbot answers approximately 80% of questions it receives using pre-programmed responses without human intervention.
These numbers suggest that the largest near-term impacts are likely to be felt in two places: faster sorting of routine files and heavier reliance on automated intake and enquiry handling. Applicants may notice fewer personalized responses at early stages, but potentially faster movement for straightforward cases that clearly meet checklist-based criteria and are not flagged for risk or complexity.
Privacy, fairness, and integrity: what applicants should plan for
The strategy centers on an AI Charter and five guiding principles: human-centered and accountable, transparent and explainable, fair and equitable, secure and privacy-protecting, and valid and reliable. These principles are not just abstract. They shape what officers can rely on and how systems should be built, tested, and audited. Several safeguards and commitments are especially relevant to applicants and representatives
Human in the loop oversight: AI assists, but officers remain responsible for decisions and verification.
Explainability expectations: The strategy cautions against black box models for application decisions, reflecting administrative law expectations for meaningful explanations and review processes.
Bias and equity controls: The strategy commits to safeguards against bias in data and model design, including testing to identify and correct unintended bias, and references alignment with Anti-Racism Strategy 2.0 2021-2024.
Privacy by design controls:
- Mandatory privacy needs assessments and privacy impact assessments where required
- Handling only the minimum personal information necessary for justified purposes
- Exploring anonymized or synthetic data to reduce privacy risks
- Using controlled environments that enforce domestic data residency for sensitive information
- Clear warnings not to enter protected, classified, personal, or de-identified information into public AI tools
Program integrity and fraud detection:
- The strategy highlights AI experimentation to detect anomalies and possible manipulation of documents such as academic records and bank statements
- It also describes anomaly detection that can flag irregular travel patterns, inconsistent information, document forgery, identity theft or photo morphing, or overstays, with human investigation after a flag
A practical takeaway is that integrity screening is likely to become sharper. Applicants should expect greater sensitivity to inconsistencies across travel history, employment claims, finances, and supporting documents. Clear explanations and well-organized evidence can reduce the chance of being misrouted into higher scrutiny.
Current difficulties include higher application volumes, limited officer time, and stricter integrity screening that can delay files or increase refusal risk when evidence is inconsistent; support can include preparing coherent documentation packages, advising on disclosure and risk issues, and representation for immigration applications through an immigration consultant.
General conclusion: The strategy confirms a cautious expansion of AI focused on administrative efficiency, client service, and integrity support, while rejecting autonomous refusal tools. The strongest applications in this environment are those that are internally consistent, evidence-based, and easy to validate, because automation and anomaly detection tend to accelerate clarity and slow down ambiguity.
