Will AI Replace Legal Jobs in Singapore? Here’s What to Do in 2025

Singapore's courts, firms and law schools are already feeling the push and pull of generative AI in 2025 - from Harvey AI in the Small Claims Tribunals to GPT‑Legal slashing document summary time “from two days to approximately 10 minutes” - so the question is no longer if AI will matter, but how legal jobs will change.

Senior leaders, including Chief Justice Sundaresh Menon, have urged practical upskilling and SAL is rolling out training to help lawyers use GenAI responsibly; see coverage: GovInsider - Four ways AI is shaking up Singapore's legal practice and Singapore Academy of Law - How legal tech is transforming practice in Singapore.

For juniors and firms the path is clear: learn to weaponise AI safely, reclaim higher‑value judgment work, and treat legal data and governance as strategic assets - otherwise routine tasks will be automated and career stepping stones will evaporate.

“AI won't replace junior lawyers, but junior lawyers who use AI might replace those who don't.”

The current state of AI in Singapore's legal sector in 2025 can best be described as pragmatic experimentation: courts, firms and arbitration hubs are piloting targeted tools (legal research assistants, translation and speech‑to‑text engines, and case‑summarisation aids) while embedding clear governance so innovation doesn't outpace trust.

Pilot projects range from the Harvey AI collaboration in the Small Claims Tribunals to LawNet and bespoke summarisation trials that help judges and litigants wrestle with mountains of WhatsApp messages, emails, photos and long affidavits by turning them into usable chronologies and summaries; these practical moves, and the emphasis on verification and human oversight, are well documented in regional reporting and the Singapore Judiciary's own speeches and guidance (How courts are adopting AI in the Asia‑Pacific region - Thomson Reuters, Justice Aidan Xu speech - Legal and regulatory issues with artificial intelligence (IT Law Series 2025)).

The policy mix - Model Gen‑AI frameworks, PDPA safeguards, and court Guides - signals a clear message: use AI to boost efficiency and access to justice, but keep humans firmly in the loop.

“AI is in the end a tool. Tools can maim and kill, just as much as they can help us in our work, enabling us to do more, better and faster.”

The top risks for legal work in Singapore are practical and immediate: generative AI's propensity to “hallucinate” facts and cite non‑existent cases (a problem that has already led to court rebukes overseas), the risk of deepfakes and fabricated evidence undermining trust in filings, erosion of foundational craft as juniors rely on AI for research and drafting, and data‑privacy and confidentiality dangers when sensitive materials are fed into public models; the Singapore Judiciary has flagged many of these concerns in its IT Law Series work and pilots such as the Harvey collaboration in the Small Claims Tribunals (see Justice Aidan Xu's IT Law Series speech) while global reporting documents real sanctions sparked by AI‑generated fiction in court papers (see Reuters' coverage of AI “hallucinations”).

Add opacity and bias in black‑box models and the practical pain point of self‑represented litigants being misled by overconfident chatbots, and the result is clear: systems, governance and lawyer AI‑literacy must be strengthened fast - otherwise routine but essential checkpoints (fact‑checking citations, verifying evidence) will be eroded; judges already report spending time peering at images for tell‑tale signs of fakery, a reminder that human scrutiny remains the last line of defence.

“AI is in the end a tool. Tools can maim and kill, just as much as they can help us in our work, enabling us to do more, better and faster.”

Judiciary and institutions in Singapore have shifted from cautious observation to active stewardship, with Chief Justice Sundaresh Menon's TechLaw.Fest 2025 keynote laying out a practical framework for using technology to extend the court's reach, manage “complexification” and protect access to justice - think automation for routine workflows, AI summarisation for long affidavits, and “extended court” e‑services that help users navigate the system (Chief Justice Sundaresh Menon keynote at TechLaw.Fest 2025 on using technology to extend court reach).

In parallel, the Judiciary is piloting targeted tools - most notably a Harvey AI platform for the Small Claims Tribunals designed to help self‑represented persons organise evidence, summarise opposing submissions and prepare filings in a jurisdiction that sees more than 10,000 SCT claims a year with a S$30,000 limit - while layering governance, human oversight and training so efficiency gains don't erode core legal scrutiny (Harvey AI pilot for Singapore Small Claims Tribunals assisting self-represented litigants).

The institutional message is clear: use AI to boost access and productivity, but embed verification, explainability and judicial diplomacy so humans remain the final safeguard.

“Courts across the world face similar challenges, including the proliferation of disinformation and misinformation in public discourse (also known as the phenomenon of “truth decay”); the transformative impact of technological advancements, particularly in generative artificial intelligence; and the global access to justice deficit.”

Law firms should treat AI adoption as a people-first transformation: preserve the “ground floor” where juniors draft first and learn from red‑ink feedback, build deliberate upskilling so associates can spot AI errors, and create clear, firm‑wide policies that govern when and how generative tools are used.

Practical steps include mandatory prompt‑and‑verify training and sandboxed pilots tied to the Model AI Governance Framework, contractual safeguards for external models, and knowledge‑management plans that turn a firm's archives into high‑quality, proprietary datasets rather than loose prompts.

Firms must also rethink business models - moving from pure billable hours to fixed or value fees plus technology surcharges - and create pathways that redeploy juniors into client‑facing, judgment‑heavy work as AI handles routine tasks.

These are not abstract ideas: commentators urge firms to consciously preserve training opportunities (see Angeline Poon's warning in the Law Gazette) and Justice Valerie Thean advised firms to re‑examine training, governance and business practices in her Litigation Conference speech to keep lawyers accountable and courts protected.

Small, disciplined changes - sandboxed pilots, compulsory vetting checklists, and supervisor review of first drafts - will preserve professional judgment even as AI raises productivity.

“If we took Harvey away from our staff, there'd be a riot.” - Fortune (on junior lawyers' embrace of AI)

Juniors, trainees and law students in Singapore should treat 2025 as a practical apprenticeship in two tracks: legal craft and AI literacy. Keep the “ground floor” work - write your own first drafts, research memos and clause notes before consulting a model so you learn structure and judgment, then compare outputs line‑by‑line to spot hallucinations or missing nuance; this simple exercise reveals where human judgment still matters.

Use the 4R Decision Framework (Repetition, Risk, Regulation, Reviewability) to decide when to delegate routine, repeatable tasks to tools and when to insist on human oversight - see the Law Gazette's clear guide to the 4R framework for concrete tests and red flags.

Learn the local governance tools and standards too: explore Singapore's AI Verify testing toolkit and the Model AI Governance Framework so you can speak confidently about explainability, data residency and PDPA limits when firms sandbox new systems.

Practise verifying citations, flagging bias, and never uploading confidential client material to public LLMs; ask supervisors for feedback on both your drafts and your AI‑checks, and treat prompt engineering as an audit skill, not a substitute for legal reasoning.

The advantage for juniors who master this mix is simple: faster productivity plus irreplaceable judgment that machines cannot replicate.

“Draft first, then compare.”

Safe, high‑impact AI use cases for Singapore legal teams are concrete and immediately actionable: deploy targeted legal‑research assistants and document‑summarisation tools to turn lengthy affidavits and message dumps into concise chronologies, use speech‑to‑text for faster hearing transcripts, and build tribunal chatbots to guide self‑represented litigants - all practical pilots noted by Thomson Reuters' review of court experiments (Thomson Reuters review of courts adopting AI in the Asia Pacific).

Protect client data by developing models inside IMDA's Privacy Enhancing Technologies (PETs) Sandbox or by training on synthetic datasets so useful AI outputs don't leak personal data (IMDA Privacy Enhancing Technologies (PETs) Sandbox).

Pair these tools with PDPA‑aligned governance, DPIAs and validation checks recommended in Singapore's compliance guidance to keep human oversight central and measurable (PDPA and AI compliance guidance for data privacy in Singapore).

The payoff is vivid: judges and juniors move from drowning in documents to scanning a clear, verified timeline - provided firms invest in sandboxed testing, citation verification and ongoing model assurance.

“…The ability of LLMs (large language models) to be able to help us sift through evidence and synthesise it and give us a composite document summarising the evidence is potentially a huge game changer,” he mused.

AI is reshaping the market and productivity calculus for Singapore's legal sector: targeted tools such as the Harvey AI legal assistant are already trimming document and research time, which can compress billable hours and push firms toward value‑based fees and tech surcharges, while labour rules mean efficiency gains won't automatically translate into longer workweeks - the Employment Act caps a standard week at 44 hours and overtime is paid at 1.5×, so firms must redesign staffing and workflows rather than demand unpaid extra time (Harvey AI legal assistant for legal professionals in Singapore, Singapore Employment Act working hours guide (Zimyo)).

To capture productivity without eroding training, firms should run sandboxed pilots and internal validation using tools like the AI Verify Toolkit and sandboxing for legal AI pilots in Singapore, reallocate juniors to judgment‑heavy client work, and codify flex‑work and overtime policies in light of new fairness rules - because non‑compliance can carry penalties that make shortcuts painfully expensive (up to SGD 250,000 in fines under new fairness provisions).

The net effect: faster delivery and lower cost per task, but only firms that pair tech with governance and deliberate training will convert those gains into sustainable jobs and career ladders.

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Building AI governance, ethics and training in Singapore is now a practical, well‑resourced task rather than a theoretical one: adopt the Model AI Governance Framework for Generative AI as the backbone, run sandboxed pilots in IMDA's GenAI Sandboxes and validate models with the AI Verify toolkit and Foundation, and use ISAGO and Project Moonshot for repeatable testing and red‑teaming before any firm‑wide rollout - the value of this approach was underscored by IMDA's multicultural red‑teaming challenge that drew 350 participants to probe LLM bias across languages.

For public‑sector and firm projects, follow the Responsible AI Playbook's lifecycle advice (design, test, monitor) and embed clear SOPs for roles, incident reporting and provenance so decisions remain explainable and contestable; practical training pathways already exist via TeSA/TechSkills Accelerator and the GenAI Playbook to reskill staff into compliance, prompt‑audit and AI‑assurance roles.

Legal teams should pair these governance tools with job‑redesign guidance (so juniors still learn craft while models handle repetition), regular third‑party audits and scenario‑based red‑team exercises - a disciplined mix of testing, training and mapped accountability that turns governance documents into everyday courtroom‑ready practice rather than paperwork.

Read IMDA's guide to the Model AI Governance Framework and the Responsible AI Playbook for concrete templates and starter kits.

Start small but sprint: in the first 30 days assemble an AI Strategy Team, map high‑value pain points and pick one pilot (eg. matter management with the Legal Technology Platform's “Copilot for SG Law Firms”) so teams can test real workflows rather than theory; MinLaw's Copilot integration shows how routine admin (status updates, deadlines, prioritisation) can be automated to free lawyers for judgment work (MinLaw Copilot for Singapore law firms - Legal Technology Platform).

By day 60 run a controlled pilot across a small set of matters: measure time saved (note GPT‑Legal cut document‑summary time “from two days to approximately 10 minutes”), lock down data controls, and train supervisors to “prompt‑and‑verify” so juniors still learn the craft (GovInsider analysis: Four ways AI is shaking up Singapore's legal practice).

By day 90 codify learnings into an AI policy, workflow redesign and change plan for full rollout - apply the Law Gazette's staged roadmap (vision, pilots, full implementation, monitoring), define KPIs, and embed mandatory training so efficiency gains don't hollow out on‑the‑job learning (Law Gazette guide: What's Your Law Firm's AI Strategy?).

This 30/60/90 rhythm turns experimentation into repeatable value while protecting client confidentiality and preserving the apprenticeship that builds litigators and dealmakers.

“With ‘Copilot for SG Law Firms', they can now apply Generative AI directly to the cases and matters they have on hand.”

Conclusion: Singapore's path through AI disruption is practical, not panicked - and there are concrete steps to take now: respond to MinLaw's public consultation on the proposed Guide for Using Generative AI in the Legal Sector (open 1–30 Sept 2025) to help set the rules on ethics, confidentiality and disclosure; lean on IMDA's playbooks (the Model AI Governance Framework, AI Verify and GenAI sandboxes) to run safe, testable pilots; and treat upskilling as non‑negotiable - for example, consider cohort training like Nucamp's AI Essentials for Work (15 weeks, practical prompt‑and‑verify skills) so juniors learn to spot hallucinations and preserve craft.

The courts, firms and regulators are aligning around three themes - governance, verification and human oversight - so make submissions to shape the Guide, run sandboxed experiments using IMDA toolkits, and lock in structured training so productivity gains don't hollow out career learning: think measured pilots, mandatory verification checklists, and clear client disclosures to keep trust intact.

“AI is in the end a tool. Tools can maim and kill, just as much as they can help us in our work, enabling us to do more, better and faster.”