A practical, defensible framework for quantifying return on AI-augmented engineering investment. Designed to be presented to CFOs and board-level stakeholders alongside PRISM maturity assessments.
Collect these values before running calculations. Sources noted for each.
| Variable | Symbol | Source | Example |
|---|---|---|---|
| Fully-burdened engineering cost per hour | C_hr |
Finance / HR | $125/hr |
| Average team size (engineers) | N_eng |
Org chart | 8 |
| Number of teams | N_teams |
Org chart | 5 |
| Working hours per month per engineer | H_month |
Standard | 160 |
| Average new hire onboarding time (weeks) | T_onboard |
Engineering management | 6 weeks |
| New engineers onboarding per quarter | N_new |
Hiring plan | 3 |
| Variable | Symbol | Source | Example |
|---|---|---|---|
| Current deployment frequency (deploys/day) | DF_current |
PRISM/D1/DeploymentCount | 1.2 |
| Current lead time (hours) | LT_current |
PRISM/D1/LeadTimeSeconds / 3600 | 72h |
| Current change failure rate | CFR_current |
PRISM/D1/ChangeFailureRate | 25% |
| Current MTTR (hours) | MTTR_current |
PRISM/D1/MTTRSeconds / 3600 | 12h |
| Current monthly defect count | D_current |
Incident tracking | 15 |
| Average cost per production incident | C_incident |
Post-incident reviews | $8,000 |
| Average incident blast radius cost | C_blast |
SRE estimates | $25,000 |
| Monthly AI platform spend | S_ai |
AWS Cost Explorer | $3,500 |
| Variable | Symbol | Source | Example |
|---|---|---|---|
| Target deployment frequency | DF_target |
PRISM level thresholds | 3.0 |
| Target lead time (hours) | LT_target |
PRISM level thresholds | 24h |
| Target change failure rate | CFR_target |
PRISM level thresholds | 12% |
| Target MTTR (hours) | MTTR_target |
PRISM level thresholds | 4h |
| Target monthly defect count | D_target |
Based on CFR improvement | 8 |
| Target onboarding time (weeks) | T_onboard_target |
AI-assisted ramp | 3 weeks |
What it measures: Engineering time saved through faster cycle times and higher deployment frequency.
Hours saved per engineer per month:
H_saved = (LT_current - LT_target) / LT_current * H_month * utilization_factor
Where utilization_factor = 0.35
(proportion of eng time spent in commit-to-deploy cycle)
Velocity ROI (monthly, per team):
ROI_velocity = H_saved * N_eng * C_hr
Velocity ROI (monthly, org-wide):
ROI_velocity_org = ROI_velocity * N_teams
H_saved = (72 - 24) / 72 * 160 * 0.35 = 37.3 hours/eng/month
ROI_velocity = 37.3 * 8 * $125 = $37,333/month per team
ROI_velocity_org = $37,333 * 5 = $186,667/month
H_saved = (24 - 4) / 24 * 160 * 0.35 = 46.7 hours/eng/month
ROI_velocity = 46.7 * 8 * $125 = $46,667/month per team
ROI_velocity_org = $46,667 * 5 = $233,333/month
What it measures: Cost avoided through fewer production defects and failures.
Defects prevented per month:
D_prevented = D_current - D_target
Alternative calculation from CFR:
D_prevented = DF_current * 30 * (CFR_current - CFR_target)
Quality ROI (monthly):
ROI_quality = D_prevented * C_incident
MTTR improvement value (monthly):
ROI_mttr = D_target * (MTTR_current - MTTR_target) * C_hr * N_eng_on_incident
Where N_eng_on_incident = 2.5
(average engineers pulled into incident response)
D_prevented = 15 - 8 = 7 defects/month
ROI_quality = 7 * $8,000 = $56,000/month
ROI_mttr = 8 * (12 - 4) * $125 * 2.5 = $20,000/month
Total Quality ROI = $76,000/month
D_prevented = 8 - 3 = 5 defects/month
ROI_quality = 5 * $8,000 = $40,000/month
ROI_mttr = 3 * (4 - 1) * $125 * 2.5 = $2,813/month
Total Quality ROI = $42,813/month
What it measures: Value of incidents prevented by eval gates catching defective AI-generated code before merge.
Eval-blocked merges per month:
M_blocked = total_ai_merges * (1 - EvalGatePassRate)
Incidents prevented (assuming X% of blocked merges would have caused incidents):
I_prevented = M_blocked * incident_probability
Where incident_probability = 0.15
(estimated probability a failed-eval merge causes a production incident)
Eval ROI (monthly):
ROI_eval = I_prevented * C_blast
Assume 200 AI-assisted merges/month, 82% pass rate:
M_blocked = 200 * (1 - 0.82) = 36 blocked merges
I_prevented = 36 * 0.15 = 5.4 incidents prevented
ROI_eval = 5.4 * $25,000 = $135,000/month
Assume 350 AI-assisted merges/month, 94% pass rate:
M_blocked = 350 * (1 - 0.94) = 21 blocked merges
I_prevented = 21 * 0.15 = 3.15 incidents prevented
ROI_eval = 3.15 * $25,000 = $78,750/month
Note: Eval ROI is often the most impactful and most surprising number. It makes the case for investing in eval infrastructure.
What it measures: Reduced onboarding time for new engineers through AI-assisted codebase navigation, documentation, and code generation.
Onboarding weeks saved per new hire:
W_saved = T_onboard - T_onboard_target
Onboarding hours saved per new hire:
H_onboard_saved = W_saved * 40
Platform ROI (quarterly):
ROI_platform = N_new * H_onboard_saved * C_hr
Platform ROI (monthly):
ROI_platform_monthly = ROI_platform / 3
W_saved = 6 - 4 = 2 weeks
H_onboard_saved = 2 * 40 = 80 hours
ROI_platform = 3 * 80 * $125 = $30,000/quarter = $10,000/month
W_saved = 4 - 2 = 2 weeks
H_onboard_saved = 2 * 40 = 80 hours
ROI_platform = 3 * 80 * $125 = $30,000/quarter = $10,000/month
| Dimension | Monthly Value |
|---|---|
| Velocity ROI | $186,667 |
| Quality ROI | $76,000 |
| Eval ROI | $135,000 |
| Platform ROI | $10,000 |
| Total Monthly Value | $407,667 |
| Monthly AI Platform Spend | ($3,500) |
| Net Monthly ROI | $404,167 |
| ROI Multiplier | 115x |
| Dimension | Monthly Value |
|---|---|
| Velocity ROI | $233,333 |
| Quality ROI | $42,813 |
| Eval ROI | $78,750 |
| Platform ROI | $10,000 |
| Total Monthly Value | $364,896 |
| Monthly AI Platform Spend | ($7,000) |
| Net Monthly ROI | $357,896 |
| ROI Multiplier | 52x |
ROI models are only as good as their inputs. Run these sensitivity tests before presenting:
| Variable | Low Estimate | Base | High Estimate | Impact on Total ROI |
|---|---|---|---|---|
C_hr (eng cost) |
$85/hr | $125/hr | $175/hr | Scales Velocity + Platform linearly |
C_incident (incident cost) |
$3,000 | $8,000 | $20,000 | Scales Quality linearly |
C_blast (blast radius) |
$10,000 | $25,000 | $75,000 | Scales Eval linearly |
incident_probability |
5% | 15% | 30% | Scales Eval linearly |
utilization_factor |
20% | 35% | 50% | Scales Velocity linearly |
For CFO presentation, show a 3-column table:
Conservative Base Case Optimistic
Velocity ROI $112,000 $186,667 $266,667
Quality ROI $28,500 $76,000 $190,000
Eval ROI $27,000 $135,000 $405,000
Platform ROI $6,800 $10,000 $14,000
---------------------------------------------------
Total $174,300 $407,667 $875,667
ROI Multiplier 50x 115x 250x
Use the conservative estimate as your “floor” when presenting. If the floor is still compelling, the investment case is strong.
Slide 1: The Headline
Slide 2: The Scorecard
Slide 3: ROI Breakdown
Slide 4: Investment Profile
Slide 5: What Happens If We Don’t
This ROI model is part of the PRISM D1 Velocity metrics platform. All input metrics are sourced from the PRISM/D1 CloudWatch namespace and Timestream database. Update input variables quarterly or when team structure changes significantly.