Getting started with AI for contract analytics
Contract analytics has moved from theoretical possibility to practical necessity faster than most legal departments anticipated. The 2025 State of AI in Legal Report reveals that 40% of legal professionals now trust AI for contract analytics, with in-house teams showing particular confidence at 47%. But “contract analytics” covers a spectrum of activities, not a single capability. Understanding that spectrum matters for implementation.
What contract analytics actually includes
When legal teams discuss analytics, they’re typically referring to several distinct activities:
Data extraction pulls key terms, dates, parties, and values from agreements. Clause identification finds specific language patterns across a portfolio. Risk flagging spots language that deviates from standards or creates potential exposure. Portfolio insights answer questions about contract populations, renewal schedules, or aggregate terms. Trend analysis identifies negotiation patterns or tracks how standard language evolves.
AI can support all of these activities, but the technology requirements, implementation effort, and accuracy levels vary significantly. Teams don’t need to tackle everything simultaneously.
Begin with specific problems
Before evaluating platforms, legal teams should document the contract-related questions they face most frequently. These might include tracking upcoming renewals, identifying agreements that still contain outdated terms, or understanding average negotiation timelines by contract type.
Real business questions should drive implementation choices. A team struggling with obligation tracking needs different capabilities than one addressing inconsistent clause language across regions. The survey finding that 96% of AI users say it helps them achieve business objectives more easily reflects this problem-first approach.
Work with existing contract portfolios
Most contract repositories exist across multiple systems. Some agreements are searchable PDFs, others are scanned images. Templates provide structure, but executed agreements include negotiated variations. Metadata may exist for recent contracts but not for older ones.
This reality affects what AI can deliver. Text extraction from scanned documents requires different technology than analyzing digital files. Clause identification works best with consistent template structures. Risk flagging requires training systems on organization-specific definitions of risk.
The in-house teams showing a 17% year-over-year increase in analytics trust didn’t begin with perfect data. They started with available information and improved incrementally.
A practical starting point
A reasonable implementation path begins with data extraction on a specific, high-volume contract type. Vendor agreements or employment contracts often work well because they’re relatively standardized.
Teams should select 50-100 examples and use AI to extract basic information: parties, effective dates, termination dates, renewal terms, payment terms. Then manually verify every result.
This verification serves two purposes. It measures accuracy, addressing the concern cited by 44% of legal professionals in the survey. It also reveals how the AI interprets ambiguous language, where it struggles with specific agreement types, and what errors it makes consistently.
Once extraction proves reliable for one contract type, teams can expand to clause identification. Testing whether AI can find all confidentiality provisions or indemnification clauses provides the next layer of confidence. After that works, portfolio-level insights become feasible—answering questions like “Which vendor contracts are up for renewal in Q3?” or “How many customer agreements include our updated data protection terms?”
Where human judgment remains essential
The survey found that 35% of legal professionals trust AI to flag risky clauses, but only 22% trust it to replace them. That gap reflects an important distinction. Risk identification involves pattern matching, which AI handles effectively. Risk mitigation requires understanding business context, relationship dynamics, and strategic priorities.
This distinction should shape implementation. AI can surface potential issues for human review. It shouldn’t automatically rewrite contract language without oversight.
The same principle applies to analytics insights. AI might identify that 40% of vendor contracts include unusual termination provisions. It likely can’t determine whether those provisions represent actual business risk or reasonable accommodations for specific vendor relationships. That assessment still requires human judgment.
Address security requirements
Security concerns rank as the top AI adoption barrier at 48%. For organizations in regulated industries or handling sensitive commercial terms, these concerns warrant serious attention.
Before implementing analytics tools, teams need clear answers about data handling: whether vendors use customer contracts to train general AI models, whether data remains siloed from other customers, what encryption and access controls exist, and how vendors handle data retention and deletion.
Satisfactory answers to these questions come before implementation, regardless of a platform’s analytical capabilities.
Establish governance frameworks
The survey shows 58% of organizations allow AI use within published guidelines, while 17% allow it without guidelines. That second group faces unnecessary risk.
Effective governance for contract analytics should address what data can be analyzed, who can access insights, how results get verified, and when human review is required. In-house teams lead in guidelines adoption (67% versus 48% in law firms), which likely contributes to their higher comfort with advanced analytics applications.
Clear parameters don’t slow implementation. They enable teams to expand usage confidently because boundaries are understood. Security considerations fit naturally here—governance should specify which contracts contain data too sensitive for AI processing and what approval processes exist for new use cases.
Measure what matters
The survey reveals that 72% of legal professionals report improved work speed with AI, and 57% report being more strategic. These outcomes justify continued investment.
Teams should track specific metrics: time saved on portfolio reviews, reduction in missed deadlines, faster response to business unit requests, improved visibility into contractual risks. When 76% of AI users report reduced burnout, that partly reflects spending less time on manual data extraction and more time on strategic analysis.
Quantifying this shift demonstrates legal’s evolving contribution to the organization. After verifying that AI can reliably extract data from one contract type, teams should document the time savings and accuracy rates. Those numbers build the business case for expanding to additional contract types or more sophisticated analytics capabilities.
Next steps
For teams ready to implement contract analytics with AI:
Identify the highest-value analytics use case based on business need. Select a clean data set to pilot with rather than attempting to analyze every contract immediately. Establish clear accuracy thresholds and verification processes before distributing insights. Document governance frameworks so team members understand boundaries. Measure and communicate impact in terms the organization values.
The path from curiosity about AI to the 93% satisfaction rate shown in the survey runs through thoughtful implementation. It requires starting with clear objectives, proceeding deliberately, and iterating based on results.
Contract analytics isn’t about replacing legal expertise with automation. It’s about using technology to surface insights that inform better legal strategy. When contract portfolios transform from black boxes into strategic assets, that changes how organizations view legal’s contribution.
The adoption curve suggests the question isn’t whether to use AI for contract analytics. The question is whether to implement it strategically or reactively. Starting now, starting focused, and starting with clear success measures separate the teams that will lead from those that will follow.
Ironclad is not a law firm, and this post does not constitute or contain legal advice. To evaluate the accuracy, sufficiency, or reliability of the ideas and guidance reflected here, or the applicability of these materials to your business, you should consult with a licensed attorney. Use of and access to any of the resources contained within Ironclad’s site do not create an attorney-client relationship between the user and Ironclad.
