AI Automation for Enterprise Revenue Operations
Automated high-volume workflows while retaining approval controls and measurable quality thresholds.
Executive Summary
Business Challenge
Disconnected systems and manual review created delays, inconsistent decisions, and limited auditability.
Solution
UTPL designed human-in-the-loop OpenAI workflows, integrated CRM and knowledge sources, and implemented evaluation and monitoring.
Implementation Journey
Business Outcomes
Lessons Learned
“The solution moved AI from experimentation into accountable daily operations.”
KPIs & business outcomes
Services Delivered
Industries
Timeline & engagement
- Timeline: 7 months
- Team: 12 specialists
- Engagement: Product and automation pod
- Region: United States
Business value map
Growth; Productivity
Delivery timeline
20-week journey: discover (3w), evaluate (4w), build (8w), enable (5w)
Implementation journey
Prioritise use cases → Build evaluation suite → Pilot → Production rollout → Scale
ROI metrics
4x ROI on targeted revenue workflows within 12 months
Before / after
Before: manual quote-to-cash steps; After: governed AI copilots with human review
Transformation metrics
Pilot-to-production 65%; Cycle time down 40%; Quality eval pass rate 96%
Key success factors
Use-case prioritisation; Evaluation harness; Responsible AI controls; Change management
Reference architecture
RAG pipeline; Model evaluation gates; Audit logging; Human-in-the-loop workflows
Related assessments
Related learning
Related knowledge
FAQ
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