Why Legal AI Needs to be Customized for Collaboration
Legal teams need an AI solution that’s built to fit their workflows and scale across teams.
Oct 21, 2025
Harvey Team
Legal work depends on precision, consistency, and effective collaboration, and every matter involves multiple contributors working toward a single, high-stakes outcome. Yet, most AI tools weren’t built with this reality in mind.
Generic AI platforms may generate output quickly, but they don’t understand how legal teams actually operate: the structured workflows, layered review processes, and shared accountability. To deliver real value, legal AI must be customized for collaboration, and designed to fit a team’s unique processes, reflect its expertise, and enable seamless coordination across people and systems.
The Problem With Generic AI in Legal Work
Legal work is inherently collaborative, and every firm or organization has its own processes, templates, tone, and approval hierarchies. When an AI solution doesn’t account for this complexity, it produces outputs that may read well, but don’t hold up in practice.
Most generic AI tools fall short in four ways:
- They lack legal context. Generic models don’t grasp the nuance of statutes, contracts, or procedural language, which leads to outputs that require heavy editing or rewrites.
- They don’t fit existing workflows. Lawyers already juggle multiple systems — from document management and CLMs to matter-tracking tools. When AI sits outside those environments, it adds friction instead of reducing it.
- They isolate (rather than connect) legal teams. Many platforms are designed for individual users and not for collaborative drafting, review, or client sharing. This limits adoption and prevents AI from delivering value at scale.
- They aren’t built with legal-grade security: Legal work depends on strict ethical walls, client confidentiality, and data security. Generic AI platforms often lack the granular permissions and safeguards required to protect sensitive information, risking both trust and compliance breaches.
3 Collaboration Criteria to Look for in a Legal AI Platform
The right AI platform should reflect how your legal team actually operates: your practice areas, your internal processes, your tone, and your standards. It should enable collaboration without adding friction, and scale with you across teams, matters, and regions.
Here are three key attributes to look for:
1. Organization-specific tailoring
It’s important to choose a platform that can be trained on your proprietary materials. This includes internal templates, past matters, preferred tone and style, and procedural playbooks and forms — subject to appropriate confidentiality and data security protocols.
2. Collaboration-ready design
Look for a solution that supports seamless collaboration, both internally across teams and departments, and externally with clients and outside counsel. That includes configurable workflows, jurisdiction-specific outputs, and tools that keep contributors aligned — whether they’re drafting, reviewing, or sharing work product across time zones and devices.
3. Architectural flexibility
Ask whether the platform supports multiple foundational models, such as OpenAI and Claude, offers multi-cloud deployment, and integrates smoothly into your existing tech stack. Long-term scalability requires both technical flexibility and infrastructure resilience.
When evaluating a platform’s ability to integrate with your existing tech and workflows, keep these areas in mind:
- Inside your DMS, like iManage or NetDocuments
- Integrated with your contract lifecycle management or knowledge systems
- Configured to support your review and approval paths
It’s also important to call out that a flexible platform is only part of the equation. Look for a provider that can tailor AI to your specific legal needs, support adoption through training and change management, and keep evolving the platform as your needs grow.
Legal AI That Works the Way Lawyers Do
In the end, the most effective AI solution doesn’t replace legal expertise — it amplifies it. Platforms that understand how lawyers actually work and collaborate will deliver the most sustainable impact. At Cole-Frieman & Mallon, for example, Harvey is integrated into associates’ daily workflows and helps increase knowledge sharing and collaboration across the firm. Associates proactively share best practices to leverage Harvey to its full potential, helping drive collaboration through these key use cases.
If you're in the evaluation process for a legal AI platform, our guide, 7 Key Criteria for Evaluating AI Solutions for Law, outlines the most important factors to consider. Download it for actionable steps, real-world examples, and a practical framework to help your team make informed decisions about legal AI adoption.