How does Autonomous Commerce improve over time?

Improvement over time

Autonomous Commerce improves over time as AI agents absorb every human exception resolution as a training signal. Autonomy rates rise by 10 to 25 percentage points in the first 6 months. Continuous improvement compounds because exception resolution becomes the next batch of automated cases. Mature deployments reach 95 percent autonomy at the 12 to 18 month mark.

Continuous improvement in depth

By Go Autonomous · Last updated: April 28, 2026

Key terms

Feedback loop: Cycle from exception resolution back into model training.
Fine-tuning: Adapting the model to customer-specific data.
Drift: Gradual change in input distribution over time.
Autonomy lift: Increase in autonomy rate after a model update.
Continuous learning: Online updates as new data arrives.

Proof points

99 percent first-time-right rate on autonomous orders.
Danfoss processes orders in under 1 minute across 26 countries.
30B+ B2B transactions executed across the Go Autonomous customer base.
43 percent capacity released across order processing teams.

Frequently asked questions

How long does deployment take?

First production flow ships in 6 to 12 weeks. Coverage scales to 80 percent autonomy within 6 to 9 months on disciplined deployments. New countries and channels add in days, not months.

How is the program measured day to day?

Three numbers carry the program: autonomy rate (share executed without human touch), first-time-right rate (share correct on the first pass), and cost per order. Cycle time and exception volume sit underneath.

Who owns it inside the organization?

Operations and IT co-own. The business case sits with the CFO, the architecture with the CIO, and the day-to-day outcomes with customer service and sales. The AI engineering is vendor responsibility, not a customer build.