Organizational AI is the next big shift in legal tech
The loudest AI headlines focus on deepfakes, viral chatbots, and worst-case scenarios. The real progress is happening inside firms and institutions, where AI is being wired into systems, workflows, and data. Legal tech shows this clearly: the firms that win will build organizational solutions, not just bolt on features.
Harvey and Clio show where the market is heading
Legal tech is moving fast. Harvey and Clio aren't dropping novelty tools; they're rebuilding core workflows around AI.
Harvey has grown into a major platform with backing from OpenAI and a new Toronto hub for engineering and client support. Its model is simple: put generative AI inside the work lawyers already do-litigation, contracts, and discovery-while integrating with systems firms rely on, like LexisNexis, iManage, and NetDocuments. The focus is on assisting higher-value work and giving elite teams a unified, AI-enabled workspace.
Clio, long trusted by small and mid-sized firms, is making a deeper AI push with its acquisition of vLex and its billion-document library. As CEO Jack Newton noted, general-purpose models trained on the open web aren't enough for legal practice. By fusing practice management with vLex's Vincent assistant and real legal data, Clio is aiming at reliability, not hype.
The shared thread: partnership and integration. Harvey isn't trying to replace firm processes; it's building infrastructure that strengthens them. Clio is tying research-grade data directly to daily operations. That's where practical value shows up for lawyers and clients.
Enterprise AI is where the real progress is
Consumer tools get attention, but enterprise platforms are doing the heavy lifting. Cohere is a clear example. It serves businesses and governments with deployment models that keep sensitive information controlled-virtual private clouds or on-premise setups-so client data stays inside the organization.
General counsel Kosta Starostin has been clear about the goal: let institutions adopt advanced models without giving up privacy or national sovereignty. That posture has made Cohere a fit for risk-sensitive organizations that need AI to meet legal and regulatory standards.
The pattern holds across the industry: AI companies are partnering with incumbents and plugging into existing systems-Harvey with legal research and DMS providers, Clio with vLex, and Cohere with public-sector and enterprise infrastructure. Innovation lands when it meets established workflows and governance.
What this means for law firms and legal departments
Your advantage won't come from playing with demos. It will come from building an operating system for legal work-documents, data, approvals, knowledge, and risk controls-where AI sits inside the pipes.
- Pick problems that matter: contract review/triage, discovery acceleration, deposition prep, research synthesis, and matter reporting.
- Insist on secure deployment: VPC or on-prem, clear data isolation, and audit logs. No commingling of your data with a public model's training set.
- Integrate, don't fragment: require tight connections to your DMS, KM, billing, and practice management. Context is the difference between noise and useful output.
- Ground on reliable sources: prioritize tools tied to authoritative legal databases over generic web-trained models.
- Measure results: cycle time per task, accuracy vs. precedent, reduction in rework, and realized write-offs recovered.
Practical vendor questions to ask
- Data security: Can you deploy in a private cloud or on-prem? Is customer data excluded from model training by default?
- Provenance: What sources feed the model for legal tasks? How is freshness handled? Can you show citations and confidence signals?
- Integration: Do you support iManage/NetDocuments, LexisNexis/vLex, and our practice management system out of the box?
- Controls: Can we set role-based access, matter-level permissions, redaction, and jurisdictional limits?
- Governance: What audit trails exist? How do you handle incident response, model updates, and versioning?
- Quality: What benchmarks or blind tests have you run against real legal workflows? Will you run a pilot on our data?
Where to start (90-day plan)
- Week 1-2: Identify two use cases with measurable pain (e.g., third-party paper review, discovery summaries). Define success metrics.
- Week 3-6: Pilot with a small, expert user group. Integrate the DMS and research tools. Capture accuracy and time-to-completion.
- Week 7-10: Add guardrails (templates, clause libraries, redline policies). Set approval paths and audit logging.
- Week 11-12: Expand to a second matter type. Build a short playbook and training for new users.
The quiet shift
The firms that pull ahead will combine domain expertise with strong data foundations and trusted integrations. AI won't replace lawyers; it will let good teams take on bigger matters, move faster with less rework, and prove value with evidence.
The next wave won't be decided on social media. It will be decided in procurement reviews, security audits, and rollout plans that connect models to real legal work.
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