Evidence & Methodology
Lab-benchmarked validation methods for reproducible AI automation results
Executive Summary
What was measured: Time-to-answer reduction, accuracy improvements, and operational efficiency gains across three applied AI projects for IT & Operations.
Baseline vs After: KBMS achieved 85% faster answers (15→2 minutes), RAG chatbot shows improved citation accuracy, ITSM copilot measures time-to-first-draft improvements.
Confidence established: Via time-boxed measurements, expert-curated test sets, SME validation, and continuous monitoring.
Scope & Objectives
Our measurement framework focuses on three core automation domains with specific success criteria:
IT Service Management
Ticket resolution automation, knowledge base integration, and workflow optimization
Evaluation Processes
Automated assessment, scoring consistency, and decision support systems
Lead Qualification
Prospect scoring, qualification criteria, and sales pipeline optimization
Expert-curated test set
Each case study uses carefully curated datasets with expert-validated test sets:
Data Sources
- Historical ticket data (anonymized, CJIS-compliant)
- Expert-curated response templates and workflows
- Real-world edge cases and escalation scenarios
- Industry-standard benchmarks and best practices
Test Set Validation
- Subject matter expert review and approval
- Cross-validation across multiple environments
- Continuous refinement based on real-world feedback
- Version control and reproducibility tracking
Key Performance Metrics
Time to Resolution
Measured from ticket creation to resolution, including all touchpoints and escalations.
Target: 40%+ reduction
Measurement: Automated timestamp tracking
Accuracy & Reliability
Precision and recall of AI-generated responses against expert-curated test set.
Validation: Expert review panels
Frequency: Weekly assessments
Latency & Cost
Response time and system performance under load.
Load Testing: 1000+ concurrent users
Monitoring: Real-time dashboards
Safety & Auditability
Operational safety and comprehensive audit trails.
ROI Target: 300%+ within 6 months
Tracking: Monthly cost analysis
Safety Guardrails
Content Safety
- Automated content filtering and moderation
- Bias detection and mitigation protocols
- Compliance with CJIS and security standards
- Regular security audits and penetration testing
Operational Safety
- Automated rollback mechanisms
- Human-in-the-loop validation for critical decisions
- Performance degradation alerts
- Comprehensive logging and audit trails
Limitations & Considerations
Technical Limitations
- Performance may vary with data quality
- Edge cases require human intervention
- Model updates require retraining and validation
- Integration complexity with legacy systems
Operational Considerations
- Change management and user adoption
- Ongoing maintenance and monitoring costs
- Regulatory compliance requirements
- Scalability constraints in high-volume scenarios
Case-Specific Evidence
KBMS — Deployed impact
Window
- • 15 apps; 2,000+ docs normalized; 6-week rollout
- • Ingestion → structuring → NotebookLM publishing
- • Timestamped lookups, sampling of resolved tickets
- • SME spot checks
Method
- • Timeboxed lookups, sampled tickets, SME spot checks
- • ~30% higher NotebookLM relevance after prompt catalog
- • Internal rubric; reviewer panel
Enterprise RAG chatbot — Internal testing
What's measured
- • Groundedness score
- • Citation accuracy
- • No-answer rate
- • Latency
- • Guardrail compliance
Method
- • Agentic RAG pipeline (retrieval + rerank + synthesis + guardrails)
- • Consumes KBMS corpus (15 apps, 2,000+ docs)
- • Cited, policy-compliant answers with deterministic no-answer fallback
- • Integrated with evaluation harness and nightly Ragas metrics
ITSM copilot — Prototype
Planned KPIs
- • ↑FCR
- • ↓AHT
- • ↓backlog
- • ↑deflection
Controls
- • HITL: Approvals
- • Audit trail: Complete logging
- • Rate limits: Safety controls
- • Scope: Services & connectors over RAG + KBMS
Ready to Validate?
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