Key Takeaways
Legal teams are increasing their investment in AI, but many are still working through how to measure impact, scale adoption, and choose the right tools for the right workflows. In this webinar, legal and AI practitioners discussed what it takes to move from experimentation to measurable value.
1. Legal AI success depends on use-case discipline, not just tool selection
The biggest gap for legal teams is not access to AI tools or budget. It is the discipline required to identify the right use cases, configure tools around real legal workflows, and support adoption with the right services layer. Teams that scale AI successfully tend to start with specific, high-impact workflows rather than searching for one tool that can solve every legal department challenge.
2. Contract review is one of the clearest areas for measurable AI value
Contract review, redlining, and repeatable document analysis emerged as strong use cases for legal AI because they involve enough volume to measure time savings and process improvements. One example discussed during the webinar involved using AI-assisted contract review to help an existing team manage more work without backfilling every open role in the traditional way.
3. Playbooks and configuration are critical to moving beyond pilots
AI tools rarely create value simply by being deployed. Legal teams need playbooks, workflows, prompts, and adoption support that reflect how their attorneys actually work. Without that configuration, even promising pilots can stall because users do not trust the output, understand the workflow, or see how the tool fits into daily practice.
4. Measuring AI ROI requires both hard and soft metrics
Legal departments are tracking AI value through metrics such as time saved, contract cycle time, outside counsel deflection, and the ability to handle more work internally. But the panel also emphasized less tangible benefits, including consistency of work product, improved risk spotting, employee satisfaction, and higher-value use of attorney time.
5. General-purpose AI and legal-specific AI both have a role
Tools like Microsoft Copilot can be useful for brainstorming, meeting preparation, business-context analysis, and general advisory work. Purpose-built legal AI tools may be better suited for specialized workflows such as contract redlining, legal research, due diligence, or document analysis where repeatability, legal context, structured playbooks, and confidence in source material matter.
6. The “one tool to rule them all” mindset is fading
Legal teams are increasingly recognizing that AI adoption will involve a mix of tools: enterprise-wide general-purpose AI, legal-specific platforms, niche workflow tools, and potentially custom automations or agentic workflows. The key is to avoid tool sprawl by ensuring each tool has a clear use case, measurable value, and integration into existing workflows.
7. AI is changing the relationship between in-house teams and outside counsel
As in-house teams build AI capabilities, they may handle more repeatable work internally and rely on outside counsel more for judgment, validation, and strategic advice. This could shift law firm engagement away from high-volume research or review tasks and toward higher-value legal analysis, quality control, and risk-based counseling.
8. AI literacy is now a core legal department capability
A successful AI rollout requires more than access to software. Legal teams need baseline AI literacy, realistic expectations, structured training, and champions across practice areas. AI should be treated as a business transformation effort, not simply another technology deployment.
9. The future of legal AI is proactive, data-driven risk management
The panel pointed toward a future where AI helps legal teams move from reactive support to proactive business counseling. By combining legal judgment with AI-powered insights, in-house teams may be better positioned to identify risks earlier, strengthen compliance programs, and deliver more strategic value to the business.
Agenda
Why AI at scale needs more than IT sponsorship
- 80% of the most advanced teams place ownership with legal operations
- How to incorporate a structured approach to scaling up
The tool selection trap
- 69% of teams are running broad tools like ChatGPT in general-purpose mode
- The balance between flexible all-purpose tools and targeted use cases
- What "legal grade" configuration requires
Measured ROI vs estimated ROI
- 83% can't or don't measure ROI on AI investments
- Key indicators to measure from day one
- Proving budget value to the business
The outside counsel reset
- 95% of teams expect law firms to use AI
- New expectations for pricing due to AI-driven productivity gains
- ALSPs are preferred 2X more than firms as the in-house AI partner
30 Jun, 2026 |
