Case study · Software
Avalara put AI into production across 8 departments in under six months.
Native helped Avalara move from scattered AI experiments to a repeatable way of deploying them, with forward-deployed engineers in every department, one shared measure of value, and 150+ deployments across the business.
In under six months Avalara had 36 AI initiatives running across 8 departments, with 179 agents and 185+ automation workflows built. Product alone saved 80k hours a year, and sales and partners saved 22k.
About Avalara
Avalara is a global leader in tax compliance software, founded in 2004. It adopted AI early.
The challenge
AI initiatives were fragmented and their value was hard to measure. Governance slowed deployment, platform dependencies stalled projects, and teams duplicated each other's effort.
Adoption depended on individual champions rather than institutional support. What Avalara lacked was a repeatable system for getting AI into production.
What we built
How it works
- 01IntakeEach department brings its AI ideas through one repeatable intake model.
- 02MeasureEvery initiative is scored on the hours it saves per year, which sets the order of work.
- 03BuildA forward-deployed engineer owns delivery, working inside the team whose process is changing.
- 04GovernWorkflows are scanned for security and compliance issues before they reach production.
- 05TrainTeams are trained to run and extend what's built, so the capability stays with Avalara.
Results
In RevOps, first response on sales credit inquiries fell from 12 hours to 1, and full resolution from 24 hours to 2. The governance scanner found that 84% of the 720 workflows it covered had issues, and surfaced them before they reached production.
Avalara now runs AI as an operating system of its own: a repeatable intake model, governance that scales with deployment, and a 12-month roadmap. 40 GTM leaders were trained in person in India, and optional platform training reached a 65% completion rate.
