Wakam achieves 70% employee adoption with 136 deployed AI agents within two months using Dust
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Employee activates AI agent
Employees human-activate agents to handle tasks, including complex workflows.
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.
What failed first
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.