In 2025, “legal AI” stopped being a novelty and became a real operational lever—when implemented correctly. At the same time, plenty of AI initiatives underdelivered because they were bolted onto legal work without trustworthy data, governance, or workflow fit.
Below is a detailed, factual recap of the legal AI trends that did and didn’t work in 2025, plus what 2026 is likely to look like.
1) What worked in 2025
A. AI embedded in real legal workflows (not standalone chat)
The most successful deployments weren’t “a chatbot for lawyers.” They were AI features embedded inside tools attorneys already use—document management, matter collaboration, contract lifecycle management, and research platforms.
A consistent theme across industry reporting was that adoption increased when AI integrated cleanly with existing systems and fit ethical and operational expectations. (American Bar Association)
Why it worked
- Less behavior change required.
- Fewer “copy/paste” steps (a major source of confidentiality mistakes).
- Easier to standardize prompts, templates, and review checkpoints.
B. Grounded AI for research and drafting support (with authoritative content)
In 2025, firms increasingly favored AI that could tie outputs to authoritative sources and operate under enterprise-grade privacy controls (rather than general-purpose public chat). This is one reason legal research vendors accelerated GenAI copilots and “deep research” style capabilities. (Thomson Reuters)
Where it delivered value
- First-pass research memos and issue spotting
- Summaries of long documents (with citations/links back to sources)
- Drafting support (clauses, emails, client updates) with human review
C. High-volume document review and triage improved materially
One of the clearest “ROI zones” in 2025: bulk review, classification, and triage—especially in investigations, discovery, and diligence. Vendors moved toward larger-scale review features and workflow “plans” designed around repeatable legal tasks. (Thomson Reuters)
What made this work (in practice)
- Constrained tasks (e.g., “find change-of-control clauses,” “flag assignment restrictions”)
- Clear review standards
- Sampling + second-level review for accuracy
D. Governance and ethics policies started to catch up
2025 wasn’t just about capability—it was about permission. Firms that rolled out training, usage policies, and confidentiality controls unlocked broader adoption and reduced risky behavior.
The ABA’s Formal Opinion 512 (2024) became a major framework firms leaned on in 2025—covering competence, confidentiality, client communication, supervision, and billing considerations. (American Bar Association)
Separately, state-by-state guidance continued to develop, reinforcing themes like don’t input confidential data into unsafe tools and verify outputs. (Justia)
2) What didn’t work in 2025 (and why)
A. “ChatGPT will replace associates” initiatives
The biggest failure pattern: treating general-purpose LLMs as if they were reliable legal databases. In real-world settings, that leads to hallucinated citations, inaccurate statements of law, and risky filings—issues that continued to draw scrutiny in 2025. (The Verge)
Core problem
- LLMs predict text; they don’t inherently guarantee legal correctness or citation validity without grounded retrieval and verification.
B. AI tools without data security + confidentiality assurances
Many pilots stalled when vendors couldn’t meet security requirements around:
- Data retention and use
- Access controls
- Auditability
- Client confidentiality obligations
Ethics guidance repeatedly emphasized the lawyer’s duty to understand how a GenAI tool uses and protects information, and to put safeguards in place. (American Bar Association)
C. “One-size-fits-all” prompts and generic outputs
Firms that tried to scale AI via generic prompts often got generic results. What worked better was playbooks:
- approved prompt libraries
- clause standards
- matter-type templates
- review checklists
Without those, quality varied too widely across users and practice groups, and pilots struggled to become reliable operations.
D. Content/training-data shortcuts created legal and business risk
2025 also reinforced that data provenance matters. A major U.S. court decision in Thomson Reuters v. Ross Intelligence highlighted the legal risk of using protected legal editorial content for AI development without permission—an issue with broader implications for AI training and IP strategy. (Reuters)
Bottom line
- “Move fast” approaches to legal data can create expensive downstream exposure.
3) The 2025 reality: adoption rose, but unevenly
Multiple industry sources in 2025 described the same tension:
- Individuals adopt faster than institutions.
- Smaller firms often move sooner than large organizations because decision paths are shorter and ROI is immediate. (AllRize)
Meanwhile, larger firms and enterprise legal departments increasingly demanded proof on:
- governance
- security
- repeatable accuracy
- defensible billing and supervision practices
4) What 2026 may look like for legal AI
A. “Agentic” legal workflows will move from hype to controlled deployment
Expect more tools that don’t just answer questions—but execute multi-step legal tasks (e.g., intake → document set assembly → clause extraction → summary → routed review). Several major vendors are already positioning “agentic” capabilities and workflow plans as the next wave. (Thomson Reuters)
What changes in 2026
- More emphasis on orchestration (workflows), not just generation (text)
- More guardrails: permissions, audit trails, and structured review
B. Regulation and compliance pressure will rise (especially for global teams)
The EU AI Act’s phased implementation puts additional weight on transparency, governance, and risk controls across the AI supply chain—particularly around general-purpose AI models and enforcement timelines. (Digital Strategy)
What to expect
- More vendor questionnaires and contractual AI addenda
- More internal governance: acceptable use, auditability, retention rules, and training
C. Legal AI will be judged on measurable outcomes, not demos
2026 will likely favor products and internal programs that can show:
- time saved per matter type
- reduction in cycle time (e.g., NDA turnaround)
- improved risk spotting consistency
- defensible QA processes
In other words: less “look what it can do,” more “show me your metrics and controls.”
D. Law firms will continue building (or buying) AI capability in-house
A notable 2025 trend was the move toward internal AI build capacity—sometimes through acquisition—to differentiate and meet client demand. (Reuters)
In 2026, expect more:
- captive “legal engineering” teams
- custom workflow automation on top of enterprise AI platforms
- practice-group-specific toolchains
5) Practical takeaways for 2026 planning
If you’re building or deploying legal AI in 2026, the winners will usually have:
- A defined use case (contract review, intake triage, diligence, research summaries)
- Grounding + verification (citations, source links, QA sampling)
- Security posture (retention controls, access controls, vendor terms) (American Bar Association)
- Governance (policies, training, escalation paths) (National Conference of Bar Examiners)
- Workflow integration (where lawyers already work) (American Bar Association)
