How Spendesk turned 90% adoption into embedded AI workflows with a champions program
After achieving 90% AI adoption, Spendesk found that depth of usage was uneven — most employees were using Dust as a generic chat replacement rather than in embedded workflows, and an initial limited rollout had created a perception that AI was only for certain roles.
A summer hackathon that generated 11 new agents resulted in only 1 surviving after six months, with momentum evaporating almost immediately — demonstrating that burst-format events do not produce sustainable AI habits.
By December 2025, Spendesk achieved 93-94% monthly active users and 80%+ weekly retention, with 40%+ of messages going to purpose-built custom agents rather than generic LLM interactions.
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Frequently asked questions
What did this team achieve with this AI workflow?
By December 2025, Spendesk achieved 93-94% monthly active users and 80%+ weekly retention, with 40%+ of messages going to purpose-built custom agents rather than generic LLM interactions.
What tools did this team use?
Dust, Salesforce.
What results were reported?
monthly active users (December 2025): 93-94%; Weekly retention (key highlights): 83%+; Weekly retention (results section): 80%+; Messages sent to custom agents: 40%+ (source-reported, not independently verified).
What failed first in this deployment?
A summer hackathon that generated 11 new agents resulted in only 1 surviving after six months, with momentum evaporating almost immediately — demonstrating that burst-format events do not produce sustainable AI habits.
How is this back office ops AI workflow structured?
Company-wide platform deployment → Champions program formalized → Department agents built → Governance touchpoints → Custom agents serve employees → 360 Customer View unification.