Success Metrics & ROI

“Are we getting value from this?”

To answer this, we need a balanced scorecard of metrics. Focusing only on speed (“velocity”) is dangerous—it encourages fast application of low-quality code.

We measure impact across 5 dimensions: Productivity, Quality, Speed, Cost, and Innovation.

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The AI Transformation Dashboard

graph TD
    KPI[AI Value Dashboard]
    
    KPI --> Prod[Productivity]
    KPI --> Qual[Quality]
    KPI --> Speed[Velocity]
    KPI --> Sat[Satisfaction]

    subgraph Metrics
        Prod1[Tasks / Sprint]
        Qual1[Bug Detection Rate]
        Speed1[Cycle Time]
        Sat1[Dev Experience Score]
    end

    Prod --> Prod1
    Qual --> Qual1
    Speed --> Speed1
    Sat --> Sat1

    style KPI fill:#e3f2fd,stroke:#1565c0
    style Metrics fill:#fff3e0,stroke:#e65100,stroke-dasharray: 5 5

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Metric Definitions & Targets

Category	Metric	Definition	Target Lift (Yr 1)
Productivity	Acceptance Rate	% of AI-suggested code accepted by devs.	> 30%
	Task Volume	Completed Story Points / Developer / Sprint.	+20-30%
Speed	Cycle Time	Time from “Commit” to “Deploy”.	-40%
	MTTR	Mean Time To Recovery (fixing bugs).	-50%
Quality	Change Failure Rate	% of deployments causing failure.	No Increase
	Test Coverage	% of codebase covered by automated tests.	> 80%
Satisfaction	DevEx Score	”Does AI make your job more enjoyable?”	> 4.0/5.0

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ROI Calculation Model

To justify the license costs (e.g., $19/user/mo), use this simple model:

Cost:

• License: $228/year
• Training/Overhead: $500/year
• Total Cost: ~$728/year per dev.

Benefit:

• Avg Dev Salary: $100,000/year (conservative).
• Efficiency Gain: 10% (very conservative).
• Value Created: $10,000/year/dev.

ROI: ($10,000 - $728) / $728 = 12.7x Return.

Even with minimal assumptions, the ROI is massive. The risk is not “wasting money on licenses”; the risk is wasting the efficiency gain by not having a backlog of work ready to consume the extra capacity.

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Key Takeaways

1. Measure Outcomes, Not Output: Don’t just count “Lines of Code Generated” (that’s often bad). Measure “Features Shipped” or “Bugs Fixed.”
2. Survey Sentiment: Developer happiness is a leading indicator of retention. If AI makes them happy, that alone is worth the cost.
3. Baseline First: You cannot measure improvement if you don’t know your current Cycle Time. Capture baselines immediately.