How much value does AI-assisted development create if every change still takes hours to explain? Most AI ROI calculators focus on coding speed. Engineering teams also spend time reviewing AI-assisted changes and reconstructing what happened when a reviewer, investigator, or auditor asks for evidence. That work belongs in the business case.
The effort is measurable: how long does it take to identify the human request, agent activity, code change, controls, and approval behind a change? Secuarden's AI development ROI calculator helps security and engineering leaders estimate four sources of recurring effort: audit reconstruction, broad review, incident investigation, and compliance preparation.
What should an AI development ROI calculation include?
Start with the volume of AI-assisted work. Estimate how many developers use AI coding tools, how often they make AI-assisted changes, and what share of those changes already have authoritative evidence. Then put a cost on the time spent filling the gaps.
First, the calculator estimates the volume of AI-assisted work. It rounds the number of active AI developers to a whole person before calculating annual changes:
Active AI developers = round(developers × AI coding adoption)
Annual AI-assisted changes = active AI developers × changes per month × 12
Uncovered share = 1 − share of changes with authoritative evidence
Hourly cost = fully loaded annual developer cost ÷ 1,920
The uncovered share applies separately to every cost category. The current calculator assumes that effort associated with changes lacking authoritative evidence is the addressable portion:
Audit hours = evidence requests/year × hours/request × uncovered share
Review hours = annual changes × review minutes/change ÷ 60 × uncovered share
Investigation hours = investigations/year × hours/investigation × uncovered share
Compliance hours = preparation hours/year × uncovered share
Annual opportunity = (audit hours + review hours + compliance hours) × hourly cost
+ investigation hours × hourly cost × 1.25
Recoverable value = annual opportunity × recoverable-value factor
Net annual benefit = recoverable value − annual platform cost
Value / cost = recoverable value ÷ annual platform cost
Payback months = annual platform cost ÷ recoverable value × 12
The 1,920 working hours, 1.25 investigation multiplier, uncovered-share treatment, and recoverable-value factor are model assumptions, not universal benchmarks. In particular, a compliance task may cover more than AI-assisted changes; edit its hours and the factor to fit your actual process.
Audit reconstruction
How many requests for AI development evidence arrive each year, and how long does it take to reconstruct a request when the record is incomplete?
Review allocation
How many minutes per AI-assisted change are spent on broad checks that could be focused by reliable context about the agent, its instructions, the code affected, and the controls applied? This is potentially avoidable triage time, not the time needed to validate code or make an approval decision.
Incident investigation
When an AI-related change is under investigation, how long does it take to establish what happened and which decisions were made?
Compliance preparation
How much recurring time goes into assembling change and approval evidence for internal or external review?
These are opportunity estimates, not automatic savings. If review time is already counted in an audit or investigation estimate, remove the overlap. Keep security judgment, code testing, and required human approval in the workflow.
A worked example: a 50-developer team
Consider a team with 50 developers, 75% AI coding adoption, 20 AI-assisted changes per active developer each month, and authoritative evidence for 20% of those changes. Assume a fully loaded developer cost of $175,000 per year. The calculator rounds 50 × 75% = 37.5 to 38 active AI developers, then calculates 9,120 annual AI-assisted changes (38 × 20 × 12).
The expected scenario uses 12 evidence requests a year at 10 reconstruction hours each, 12 avoidable review minutes per change, two AI-related investigations at 20 reconstruction hours each, and 120 compliance preparation hours. With an 80% uncovered share, that means 96 audit hours, 1,459.2 review hours, 32 investigation hours, and 96 compliance hours. Review hours, for example, are 9,120 × 12 ÷ 60 × 0.8 = 1,459.2. At $175,000 ÷ 1,920 = about $91.15 per hour, the model estimates about $154,146 in annual gross opportunity. Applying its editable 65% recoverable-value factor gives about $100,195 in recoverable value per year.
At an illustrative annual platform cost of $30,000, that becomes about $70,195 in net annual benefit, 3.3× value to cost, and 3.6 months of modeled payback. Displayed figures are rounded; the calculator uses unrounded values in subsequent calculations. These are outputs of the stated assumptions, not observed Secuarden customer outcomes or a price quote.
Where Secuarden fits
The opportunity depends on the quality of the evidence. A pull request can show the final diff, while questions about the human request, agent actions, policy decisions, and approvals may require a wider record.
Secuarden Change Assurance is designed to connect human intent, coding-agent activity, policy decisions, approvals, and the resulting code change in an inspectable record. A Context BOM is the structured record of that context and its links to files, commits, or pull requests. It helps a reviewer answer why this change happened and how it was governed without piecing together disconnected sources. Code Intelligence examines code risks in the pull-request workflow and provides contextual findings and remediation guidance, helping reviewers focus on material issues. Together, these capabilities can make review and reconstruction more focused where the relevant integrations and controls are configured. The precise evidence available depends on the deployment and supported coding-agent workflows.
That is the Secuarden-specific ROI question: how much time could your team reclaim if the evidence behind each AI-assisted change were available when someone needed it?
Make the estimate defensible
Use a short pilot to replace guesses with observations:
- Sample recent AI-assisted changes and measure the time needed to answer a standard set of questions: who initiated the work, what the agent did, what changed, and who approved it.
- Separate routine review effort from audit requests, investigations, and compliance preparation so the same hours are not counted twice.
- Measure evidence coverage and reconstruction time before and after the pilot, then update the calculator's assumptions.
- Include the actual platform, integration, and operating costs in the investment case.
The calculator deliberately excludes AI coding productivity gains, prevented incidents, regulatory fines, and revenue effects. Those may matter to your organization, but adding uncertain benefits to a time-based estimate can obscure what the evidence actually supports.
Build your own AI development ROI estimate with your team size, change volume, evidence coverage, and costs. The inputs stay in your browser, and you can copy a shareable estimate for review.