Is your in-house legal department ready for AI?
Corporate legal teams are rushing to add AI tools as demands grow and budgets face scrutiny. The opportunity is real, but the risk is wasting money on software that sits unused. Preparation beats speed.
Key insights
- Implementation, training, and integration need more focus. Many teams are not ready to scale AI responsibly.
- Efficiency and cost pressure are driving adoption. Most in-house teams feel under-resourced and see tech as a lever to do more with less.
- Budgets are holding steady or growing. 59% report increasing use of tech tools, and 88% have stable or expanding tech budgets.
The race is on
Legal departments are being pushed to take on more work while controlling spend. That's fueling faster adoption of tech and AI, with a rising share planning fast, large-scale deployments. The share of departments going big has increased from 3% to 12% year over year.
Speed without structure, however, leads to shelfware. Buying more tools won't fix bottlenecks if teams can't implement, integrate, and train.
The gap: adoption vs. effective use
Core tools like legal research, spend management, and eDiscovery are widely deployed but often underused. The same pattern is emerging with AI. Contract AI is more than twice as likely to be underutilized than used effectively. Generative AI is splitting between effective usage and underuse.
That's a signal. The issue isn't a lack of software - it's readiness. The good news: many departments haven't fully rolled out GenAI yet and plan to procure in the next 24 months. There's time to set the foundation.
A practical plan for legal leaders
- Run a tech audit. Map every tool, who uses it, how often, and for what. Cut or consolidate low-use tools. Double down on what supports priority workflows (e.g., matter intake, contract review, discovery, compliance).
- Ensure implementation capacity. Assign owners, project plans, and timelines. Budget for configuration, data prep, change management, and support - not just license fees.
- Treat AI differently. Set policies for data sources, privacy, privilege, model output review, retention, and vendor oversight. Borrow proven guidance like the NIST AI Risk Management Framework and the ICO's AI and data protection guidance.
- Integrate with core systems. Connect AI and automation to your DMS, CLM, matter management, SSO, and knowledge sources. If it doesn't fit the daily workflow, usage will stall.
- Provide targeted training and enablement. Deliver role-based sessions, quick reference guides, office hours, and internal champions. If you need structured options by role, explore AI courses by job.
- Start with high-impact, low-risk use cases. Examples: intake triage, clause extraction and comparison, spend analysis, research summarization, and first-draft generation for routine documents.
- Measure outcomes. Track cycle time, outside counsel spend per matter, time-to-first-draft, review hours saved, and accuracy/defect rates. Tie results to business goals to sustain buy-in.
What good looks like in year one
- Clear policies for responsible AI use, approved data sources, and review standards.
- Two to three production use cases with owners, SLAs, and measurable outcomes.
- Integrated workflows - no copy/paste loops between tools.
- Adoption tracked monthly with usage dashboards and feedback loops.
- Ongoing enablement: refreshed training, playbooks, and show-and-tell sessions.
Budgets are rising - results must follow
With stable or growing tech spend, the mandate is simple: translate licenses into measurable productivity. Avoid big-bang rollouts. Pilot, integrate, train, measure, then scale. Repeat.
Bottom line
Buying AI is easy. Getting value from it requires preparation, process, and steady execution. If you put integration, training, and governance first, your department will see real efficiency gains - and your AI spend won't turn into shelfware.
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