By Amit Acco, Partner
On a crisp October morning at the Bella Center in Copenhagen, delegates gathered in Room B1 4 for one of the most anticipated discussions of the IBA Annual Conference, a session on AI in immigration law titled Artificial Intelligence and immigration: promise, peril and practice, presented by the IBA Immigration and Nationality Law Committee. Skillfully steered by Greg Siskind—whose deft moderation, warmth, and pioneering grasp of legal technology kept a complex, fast-moving debate collegial, engaging, and sharply focused—the panel brought together distinct perspectives from across the globe, spanning large-firm leadership in London, specialist corporate practices in Sydney, and in-house technology counsel from Washington. Yet the throughline of the morning centered on a fundamental operational shift: how immigration law practice is transforming from passive prompting into active, agentic execution.
Just two years ago at the IBA gathering in London, the legal community was still debating prompt engineering, generic summaries, and whether chatbots could pass professional exams. In Copenhagen, the conversation moved past theoretical novelties into the reality of daily workflow. Generative AI answers questions, but agentic AI acts. The modern immigration lawyer is no longer merely querying an external chat window; they are deploying software agents that collect records, file documents internally, draft complex correspondence, and triage caseloads. In this new reality, the immigration lawyer’s primary craft is swiftly shifting from the mechanical act of drafting to the higher-order disciplines of review, judgment, and risk management.
The Shift from Assistants to Autonomous Agents in Immigration Law
The distance between the technology of 2025 and late 2026 is best measured not in model benchmark scores, but in what a practicing immigration lawyer can deploy on a Tuesday morning without submitting a ticket to an IT department. A year ago, automating an administrative or drafting workflow required external software developers, protracted budget cycles, and dedicated project managers. Today, with the rapid maturation of coding assistants and lightweight cloud architectures, a practitioner can design, test, and ship custom software in-house.
Within our practice at Kan-Tor & Acco, that transition yielded concrete tools that operate across daily caseloads: Gemini Responder, an Outlook add-in rolled out through Microsoft 365 that distills complex incoming inquiries and drafts contextual responses; an automated bilingual engagement-letter wizard that draws client details directly from firm systems, generates live previews in English and Hebrew, issues finalized client agreements, and archives records in SharePoint; and an inbox triage agent that monitors inbound communication, screens out internal clutter, highlights high-priority external matters requiring senior attorney attention, and prepares draft replies awaiting human approval. In every instance, the architectural principle remains non-negotiable: the agent drafts, but the attorney approves.
Yet this newfound capability brings sharp, unresolved limitations. Large language models remain heavily biased toward English common-law authorities. When confronted with niche jurisdictions, Hebrew-language regulatory circulars, or the unwritten procedural nuances of the Israeli Population and Immigration Authority, general-purpose models exhibit an unearned confidence that quickly veers into hallucination. Public case databases show more than two thousand judicial decisions worldwide that caught and penalized hallucinated legal citations—a figure that represents only the citations exposed on the record. Furthermore, the immigration systems that lawyers interact with daily remain deliberately resistant to programmatic integration. Without standardized application programming interfaces (APIs), and guarded by CAPTCHA barriers and frequent session timeouts, the final mile of government filing remains, and perhaps ought to remain, an exclusively human endeavor.
The $100,000 Allocation: Buy, Build, or Compose?
As managing partners confront accelerating technology cycles, the central strategic question has shifted from whether to adopt agentic AI to how to deploy capital effectively. If a mid-sized immigration law firm were handed a $100,000 budget for the coming year, pouring that capital into speculative software development or legacy vendor upgrades would be a misallocation. The winning formula rests on a clear division of labor: buy the knowledge, build the workflow, and invest in the people.
Under this model, the foundation requires roughly $40,000 invested in enterprise-grade platforms that guarantee strict data privacy, zero model training on firm data, and robust document connectors. Another $25,000 should secure dedicated legal research and immigration-specific intelligence engines whose underlying legal libraries are maintained, curated, and legally citable. An equal $25,000 belongs in workforce enablement: structured training, workflow redesign, and cultivating a part-time legal technologist or AI operator within the firm. The final $10,000 serves as an internal innovation sandbox, empowering curious fee-earners to build targeted automations that solve their own immediate bottlenecks.
The conventional binary between buying commercial off-the-shelf software and building proprietary enterprise code is largely obsolete. The modern strategy is composition: layering general-purpose foundation models over firm-specific knowledge, connecting them to routine productivity suites, and using lightweight custom code as the connective tissue for the final twenty percent of the workflow. Twenty years ago, lawyers learned to master spreadsheets; today, they must learn to orchestrate workflows. A firm does not need a department of computer scientists, but it undeniably needs at least one immigration lawyer who understands how to transform repetitive legal friction into a functioning digital tool.
Algorithmic Gatekeepers: AI in Immigration Law and the Reality of Automated Borders
While private law firms deliberate over workflow automation, immigration authorities worldwide have already deployed machine intelligence at scale. Across the United States, Department of Homeland Security use cases for artificial intelligence expanded by over thirty-five percent in recent months, with agencies deploying automated screening systems like the ELIS Evidence Classifier and contracting advanced analytical platforms to process vast streams of applicant data. In the United Kingdom, Home Office pilots for asylum case summarization have demonstrated notable administrative time savings while simultaneously drawing fierce legal scrutiny over procedural fairness. Across the European Union, while the AI Act categorizes migration and border control systems as high-risk under Annex III, negotiated implementation timelines have deferred those formal compliance duties toward late 2027.
The central procedural dilemma facing immigration law advocates is what has come to be known as the human-in-the-loop loophole. When an agency categorizes an automated vetting tool as non-high-impact simply because a human adjudicator rubber-stamps the final output, transparency vanishes. In high-volume environments where adjudicators face punishing caseload quotas, automation bias sets in: if a machine flags an applicant for suspicion, an overworked officer rarely spends the time required to disprove the algorithm. The machine’s score becomes the de facto adjudication.
This dynamic demands an immediate evolution in advocacy. Counsel must now write every petition, waiver, and support brief for two distinct readers: the machine and the human officer who follows it. Machine reviewers prioritize structural consistency, semantic predictability, and exact data matching across visa petitions, prior filings, corporate registries, and public professional footprints. A single date mismatch or irregular job classification that a sensible human examiner might overlook can easily trigger an automated fraud flag or a Request for Evidence. Furthermore, whenever an administrative refusal displays the rigid, boilerplate hallmarks of an algorithmic assessment, legal counsel must routinely establish an evidentiary record by demanding written disclosure of whether automated triage or predictive scoring influenced the outcome.
The In-House Reckoning and the Death of the Billable Buffer
Corporate clients are experiencing their own internal reckoning with technology. While general counsel surveys reveal widespread frustration that outside law firm adoption has not yet translated into immediate fee reductions, the demand for sophisticated technological governance is accelerating. In corporate requests for proposals, prospective clients increasingly demand detailed disclosures outlining which tools are deployed, how confidentiality is secured, and whether firm policies prohibit the leakage of proprietary corporate data into public training sets.
In this landscape, immigration law occupies a unique structural position. Because immigration law practice has historically operated on fixed, flat-fee billing models rather than billable hours, automated efficiencies translate directly into expanded capacity and protected margins rather than lost billable volume. Yet this pricing insulation is temporary. As corporate procurement teams benchmark how rapidly routine filings can be generated, the billable value of document production will approach zero. Sustainable legal value will concentrate entirely in the human domains of nuanced regulatory interpretation, complex audit defense, cross-border corporate restructuring, and high-stakes advocacy. Within five years, practicing immigration law without integrated AI workflows will seem as unthinkable as attempting to manage global corporate mobility without email.
Guardrails for the Never-Sleeping Trainee in Immigration Law
The deployment of agentic AI demands a fundamentally different ethical posture than the casual use of generative chatbots. An autonomous agent should be managed with the same rigorous supervision one would apply to an eager, tireless junior trainee. It must operate under strict least-privilege system access, remain subject to hard administrative boundaries, maintain an auditable decision log, and feature an instant kill switch. It must never possess the authority to file a document, deliver binding advice to a client, or transmit correspondence outside the firm without senior attorney sign-off.
Recent disciplinary actions across international jurisdictions demonstrate the severe perils of unverified delegation. In Australia, federal courts recently penalized counsel thousands of dollars alongside public reprimands for submitting briefs laced with hallucinated legal citations. In the United Kingdom, tribunals and divisional courts have issued stern reminders that presenting fictitious authorities generated by language models borders on contempt of court. In Israel, where the Supreme Court has levied escalating personal sanctions against practitioners who rely on unverified AI drafting, the Israel Bar National Ethics Committee updated its Opinion 60/24 to draw firm boundaries around autonomous systems, mandating strict source verification, client confidentiality protections, and absolute human oversight over algorithmic actions. Immigration law practice, marked by demanding deadlines, immense filing volumes, and highly vulnerable applicants, represents the legal profession’s canary in the coal mine; shortcuts taken here carry immediate, life-altering human consequences.
Toward 2029: The Rise of Algorithmic Due Process in Immigration Law
Looking ahead to 2029, the landscape of immigration law will likely become an agent-to-agent negotiation. Law firm agents will ingest complex corporate records, cross-examine applicant evidence, assemble certified exhibits, and verify regulatory compliance before submission. On the other side of the portal, government agents will ingest that structured data, run automated background validations, perform instantaneous fraud analysis, and score each petition against internal enforcement metrics before routing the file to an adjudicator.
In that world, the defining competence of an elite immigration lawyer will not be typing speed, template mastery, or manual form assembly. It will be the mastery of algorithmic due process: understanding precisely how administrative models parse human narratives, structuring filings to withstand automated scrutiny, uncovering the black-box logic of automated refusals, and holding government systems strictly accountable to the rule of law. The journey toward that future begins today, not by idly prompting chatbots for surface-level answers, but by rolling up our sleeves, understanding the code, and stepping into the role of the lawyer who builds.
