Outcome Engineering Framework

DataQubi ROI Calculator

FSI Operator View: Tune assumptions for frontline claims, AML, underwriting, and ops teams. Model EBITDA impact from Microsoft-native AI implementations in regulated financial services.

βš™οΈ Scenario Configuration

Adjust parameters to match your environment. All values validated against industry benchmarks.

Focus on leakage, fraud detection, and adjudication efficiency.

$50MMid-Market$5B+
5%Industry Avg40%
20dBest Practice90d
2dConservative15d
4%Typical Range15%
10hBenchmark100h
$75FSI Talent$300
$500KTypical$50M
5%Conservative40%
40%Regulated FSI95%
Adoption Curve by Year Realistic ramp in complex environments
Engagement Investment $150,000

Fixed 90-day Outcome Engineering Framework including Microsoft Fabric setup, AI agent development, and outcome tracking infrastructure.

πŸ’‘ CFO Impact Summary

Capital efficiency, EBITDA expansion, and risk-adjusted returns.

πŸ”’
CFO Validation Lock
Baseline, attribution methodology, and ROI pillars are finalized with your CFO during Phase 1 Diagnosis.
Payback Period Capital Efficient
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Months to recover $150K investment
3-Year Net Benefit Post-Platform Costs
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After Azure, licensing, and maintenance
Annual EBITDA Uplift
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Recurring annual impact
EBITDA Margin Impact
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Basis points improvement

Value Pillars

Year 1 Annualized
πŸ’§ Cash Flow
$0
Working capital liberation from DSO reduction and reduced borrowing costs.
Realization: 100% (Hard savings)
⚑ Efficiency
$0
Hours freed from manual processing, reconciliations, and exception handling.
πŸ›‘οΈ Loss Avoidance
$0
Direct reduction in fraud, claims leakage, and write-offs via AI detection.
Outcome Sensitivity Analysis
Worst (-20%) Base Best (+20%)
Ready to model impact. Configure your scenario parameters and click Calculate ROI to generate CFO-ready financial projections including sensitivity analysis and risk-adjusted returns.
3-Year ROI (Advanced)
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Platform Cost (Annual)
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Azure, licensing, maintenance

Disclaimer: Estimates are directional based on conservative industry benchmarks. Actual performance depends on data quality, change management, and regulatory context. All figures are risk-adjusted using your Data Trust Score. Contact DataQubi for a tailored, engagement-ready analysis.