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.
source quote
source quote
source quote
source quote
source quote
source quote
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.
Show all 11 reported metrics
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.
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.
Related back office ops cases
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.