AI in immigration and proving the value of AI in global mobility
For governments, the challenge is to manage complex immigration systems while maintaining accuracy, fairness and public confidence. AI offers new ways to address that challenge, and its value depends on how it is used. The strongest applications do not replace immigration expertise; they help make it more effective.
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From administrative workload to intelligent support
One of AI’s most practical applications is reducing the time spent on repetitive administrative tasks. Immigration applications can involve multiple documents, detailed personal histories and extensive supporting evidence. AI can help classify documents, summarise information, identify patterns and direct cases to the appropriate teams.
Canada provides a clear example. Immigration, Refugees and Citizenship Canada (IRCC) uses machine learning and predictive analytics to support application triage and workload distribution. Its AI strategy also describes tools that categorise client enquiries, assist with document processing and help identify anomalies that may warrant further examination.
These capabilities can help officers focus on cases that require closer attention, rather than spending as much time sorting information or completing routine tasks. IRCC makes an important distinction: its AI tools support the process, but do not autonomously refuse immigration applications.
The value is not simply speed. Better-organised information can support more effective case handling, while appropriate human oversight helps ensure that individual circumstances are not lost in the process.
The Netherlands offers another example of AI being applied to specific administrative challenges. The Immigration and Naturalisation Service (IND) uses VisualDOC to recognise and classify documents, including birth and marriage certificates. It also uses an AI chatbot to help visitors find information on its website.
The IND is exploring further applications, including speech transcription and tools that summarise documents and case files. It has also been testing AI assistants to help staff search information, structure documents and draft text. These applications are intended to support employees, not make immigration decisions on their behalf.
This demonstrates how AI can be introduced in targeted ways. Rather than attempting to automate an entire immigration process, organisations can focus on tasks where technology has a defined purpose, and its output can be checked.
Identifying issues before they become bigger problems
AI can also help identify unusual patterns, inconsistencies or information that deserves further investigation. In immigration, this may support document checks and help officers recognise potential concerns earlier in the process.
Canada’s AI strategy describes experiments with tools intended to detect anomalies and possible manipulation in documents such as academic records and bank statements. Such technology can help identify material for further review, but a flagged document is not proof of fraud. Its authenticity and relevance must still be assessed.
The UK provides a different example of AI being explored in a sensitive area of immigration: age assessment. In guidance published in May 2026, the Home Office described facial age estimation technology that uses AI to estimate a person's age from a photograph. The government is exploring its potential use to support initial age assessments, including cases involving people who claim to be children. The technology is not currently used in the UK immigration system.
The distinction is important. An AI-generated estimate could provide an additional source of information, but it cannot establish a person's age with certainty or replace an appropriate assessment by officials. Where the consequences of an error are significant, technology must be evaluated carefully before it is introduced.
Source: UK Home Office — Facial age estimation
Across these examples, the benefit is the ability to bring relevant information to the attention of the right people at the right time.
Different countries, a shared challenge
The UK, Canada and the Netherlands illustrate how AI can support immigration administration through application triage, document classification and information analysis. Germany is also part of this evolving picture, although it is important not to confuse research into AI with evidence of a system already operating in practice.
The European Union Agency for Fundamental Rights launched research in 2026 into AI technologies being tested, piloted or deployed in asylum and immigration procedures across several EU Member States, including Germany and the Netherlands. The research examines how these technologies are used, their implications for fundamental rights and the safeguards needed to manage associated risks. Its findings are scheduled for publication in 2027.
This work reflects a wider challenge for immigration authorities: understanding not just what AI can do, but whether it performs reliably and fairly in real-world conditions.
Source: European Union Agency for Fundamental Rights — AI in asylum and immigration procedures
The value depends on getting it right
Immigration is not a low-stakes administrative exercise. Decisions can affect a person's ability to work, study, live with family or seek protection. AI systems that produce inaccurate information, overlook important details or reflect bias can therefore create serious consequences.
The European Union’s AI Act treats specified AI applications in migration, asylum and border control as high-risk where they fall within the relevant legal categories. The framework establishes requirements intended to address risks involving matters such as data quality, transparency, human oversight and fundamental rights.
For immigration authorities and organisations working with immigration processes, responsible adoption means testing systems, protecting personal information, checking outputs and maintaining clear accountability. AI-generated summaries or guidance should be verified against original documents and current official requirements. A confident answer is not necessarily a correct one.
The same principle applies to decision-making. AI may help organise evidence or highlight an issue, but it should not obscure who is responsible for assessing the facts and reaching a lawful decision.
Source: European Commission — Regulatory framework for artificial intelligence
What this means for global mobility
For businesses managing international workforces, the developments in immigration offer a practical indication of where AI can add value. Global mobility teams must navigate changing immigration requirements, collect supporting documents, monitor deadlines and coordinate information across multiple jurisdictions. These demands create opportunities to use AI for document review, information retrieval, case tracking and the identification of potential compliance issues.
AI may also help teams organise immigration requirements across countries and support workforce planning by making relevant information easier to analyse. However, these are potential business applications, not guaranteed outcomes. Their effectiveness depends on reliable data, up-to-date legal information, secure systems and appropriate human review.
The lesson from government experience is that meaningful progress comes from applying AI to clearly defined problems, rather than adopting technology for its own sake. Used responsibly, it can reduce administrative burdens and help specialists concentrate on complex cases, legal interpretation and individual circumstances.
AI’s value in immigration is ultimately measured not by how much work it automates, but by how effectively it supports better-informed decisions, more efficient processes and responsible outcomes. For global mobility, that provides a strong starting point for exploring how technology can support international workforce management without losing the human expertise that immigration requires.
The next question is how to prove the value of AI in global mobility. Explore through K2's Frontline Thinking Paper for November 2026:
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