Legal document review · Production

Wakam achieves 70% employee adoption with 136 deployed AI agents within two months using Dust

The problem

Critical business knowledge was trapped in silos across Wakam's multi-country organization, scattered across Notion, SharePoint, Slack, Excel, and other databases, making it a significant productivity issue to find the right information at the right time.

First attempt

Wakam's data science team built a custom AI chatbot with RAG capabilities, but the AI market advanced faster than the team could match, maintaining it required constant engineering effort, and it remained primarily used by technical team members rather than achieving broad adoption.

Workflow diagram · grounded in source
1
Employee activates AI agent
trigger
“While human-activated, Harvey handles complex corporate legal workflows that previously required manual coordination across multiple agents”
2
Dual-layer permissions validate access
validation
“Agents only retrieve information from their assigned spaces, and users can only interact with agents if they have access to all the spaces those agents require”
3
Agent retrieves knowledge via RAG
ai_action
“an AI platform that could securely tap into their proprietary knowledge: insurance regulations, partner contracts, operational procedures, and market intelligence. The platform had to integrate seamlessly with existing data sources (Noti…”
4
Harvey handles legal workflows
ai_action
“Harvey (Legal Agent) operates across the corporate legal team's entire digital workspace. With access to Notion, Outlook, web search, SharePoint, and calendar tools, Harvey can read, write, and remember context”
5
MoneyPenny orchestrates productivity actions
ai_action
“MoneyPenny retrieves emails, prepares meetings, synthesizes weekly activity, writes to Notion pages, and summarizes Slack mentions. Rather than employees choosing which agent to use for each task, MoneyPenny orchestrates multiple actions…”
6
Dashboards track adoption and productivity
feedback_loop
“Wakam built internal dashboards to track adoption metrics, they monitored user activity rates by team, identified the most valuable agents, and tracked productivity impact across different use cases”
Reported outcome

Wakam achieved 70% monthly active usage within two months of launching Dust, with 136 AI agents deployed, a 50% reduction in legal contract analysis time, and a dramatic decrease in data analysis time through self-service intelligence.

Reported metrics
Employee adoption rate70%
AI agents deployed136
Legal contract analysis time50%
Data analysis timeDramatic decrease
Show all 8 reported metrics
employee adoption rate70%
AI agents deployed136
legal contract analysis time50%
data analysis timeDramatic decrease
employees actively using AI agentsHundreds
monthly active usage within two months of launch70%
agents built by non-engineering employees96
agents built by AI engineering teamapproximately 40
Reported stack
DustRAGHarveyMoneyPennyNotionSharePointSlackSnowflakeHubSpotEntra IDOutlook
Source
https://blog.dust.tt/the-complete-guide-to-implementing-ai-agents-in-your-enterprise-wakam/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Wakam achieved 70% monthly active usage within two months of launching Dust, with 136 AI agents deployed, a 50% reduction in legal contract analysis time, and a dramatic decrease in data analysis time through self-ser…

What tools did this team use?

Dust, RAG, Harvey, MoneyPenny, Notion, SharePoint, Slack, Snowflake, HubSpot, Entra ID.

What results were reported?

Employee adoption rate: 70%; AI agents deployed: 136; Legal contract analysis time: 50%; Data analysis time: Dramatic decrease (source-reported, not independently verified).

What failed first in this deployment?

Wakam's data science team built a custom AI chatbot with RAG capabilities, but the AI market advanced faster than the team could match, maintaining it required constant engineering effort, and it remained primarily us…

How is this legal document review AI workflow structured?

Employee activates AI agent → Dual-layer permissions validate access → Agent retrieves knowledge via RAG → Harvey handles legal workflows → MoneyPenny orchestrates productivity actions → Dashboards track adoption and productivity.

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