Legal document review · Production

ALPLA transforms legal operations with Icertis Contract Intelligence and generative AI, achieving 60% reduction in external legal spend

The problem

ALPLA's small legal team struggled to manage contracts across a global operation spanning dozens of countries, with decentralized, manually-managed agreements in local languages that left risk unmonitored and legal requests outpacing capacity, making even basic NDA reviews time-consuming and inconsistent.

Workflow diagram · grounded in source
1
Non-legal user submits contract
Trigger
Non-legal users across ALPLA bring contracts to the platform to instantly assess contract health.
▾ source quote
“enabling non-legal users across ALPLA to instantly assess contract health”
2
RiskAI evaluates and classifies risk
Ai action
RiskAI evaluates risk across standard contract types and automatically classifies each as red, yellow, or green using a traffic-light model.
▾ source quote
“a generative AI application that evaluates risk across standard contract types, starting with NDAs. Using a traffic-light model (red, yellow, green), the application automatically classifies risk in contracts”
3
Field teams route by risk signal
Routing
Field teams use the red, yellow, or green classification to determine which contracts require legal review.
▾ source quote
“They just need to understand red, yellow, or green to know which contracts need review”
4
Legal team reviews flagged contracts
Human review
The legal team can focus on higher-value work.
▾ source quote
“the legal team can focus on higher-value work”
5
24/7 AI Copilot provides legal counsel
Ai action
A scalable AI assistant, trained by ALPLA's legal department, is operational 24/7 to provide enterprise-wide legal support.
▾ source quote
“A scalable AI assistant, trained by ALPLA's legal department and operational 24/7”
Reported outcome

ALPLA achieved a 60% reduction in external legal spend, with 99% of licensed users exposed to the GenAI Copilot and 79% active usage as of February 2025, while legal risk awareness became embedded in daily business operations without increasing headcount.

Reported metrics
External legal spend reduction60%
licensed users exposed to GenAI Copilot99%
active AI Copilot usage by licensed users79%
Workforce equivalent need addressedthe equivalent of 25 additional employees
Reported stack
Icertis Contract IntelligenceRiskAIGenAI Copilot
◆ Does this fit your context?

Compare to your context

Tell us your scale, team, and constraints. We'll show what changes at your size, what fails at your scale, and whether this case is a fit, needs adaptation, or won't scale to you. Free demo, no signup.

Compare to your context →
~30 seconds · free
Source
https://www.icertis.com/customers/customer-stories/alpla-transforms-legal-operations-and-risk-management-with-icertis-generative-ai/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

ALPLA achieved a 60% reduction in external legal spend, with 99% of licensed users exposed to the GenAI Copilot and 79% active usage as of February 2025, while legal risk awareness became embedded in daily business op…

What tools did this team use?

Icertis Contract Intelligence, RiskAI, GenAI Copilot.

What results were reported?

External legal spend reduction: 60%; licensed users exposed to GenAI Copilot: 99%; active AI Copilot usage by licensed users: 79%; Workforce equivalent need addressed: the equivalent of 25 additional employees (source-reported, not independently verified).

How is this legal document review AI workflow structured?

Non-legal user submits contract → RiskAI evaluates and classifies risk → Field teams route by risk signal → Legal team reviews flagged contracts → 24/7 AI Copilot provides legal counsel.

Related legal document review cases

WHAT TO DO WITH THIS

Now compare it to your context

This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.

Browse the Legal document review cases →