AI Engineering
Research-backed · 2026
AI Engineering
For software teams shipping AI features. See which production-readiness gaps you have and what they cost you each year.
What the calculator measures. For each gap you select, the model estimates three types of cost: engineering capacity lost to manual validation, rework, and maintenance; revenue at risk from increased churn and slower delivery; and roadmap opportunity cost from compounding technical debt. These are added together with diminishing returns applied — gaps share root causes, so the total is weighted rather than a straight sum.
HIGH, MED, LOW ratings show the relative severity of each gap based on how much it typically affects engineering throughput, customer trust, and commercial outcomes across B2B SaaS teams.
Conservative, Base, and Aggressive scenarios shift both the gap weightings and the overall cap. The cap prevents implausible outputs — set at 15–28% of ARR and 28–45% of engineering cost depending on scenario.
Treat all figures as directional diagnostic signals, not financial forecasts.
Formula. For each selected gap, the model estimates two cost buckets. Engineering cost = your team size × average cost × gap weight. Revenue cost = a combination of churn exposure (customers × contract value × churn rate × gap severity) and roadmap opportunity cost (ARR × delivery delay factor). The two buckets are summed with diminishing returns applied — gaps 1–4 carry full weight, gaps 5–8 carry 50%, gaps 9–10 carry 20%.
Where the weights come from. Gap weights are Brillian's calibrated estimates, derived from the research below — not figures cited directly in any single study. The Stripe developer debt research, DORA delivery metrics, and RAND AI failure data anchor the engineering estimates. The revenue risk multipliers are additionally informed by algorithm aversion research (Dietvorst et al., Wharton 2015): one visible AI error causes disproportionate trust collapse and feature abandonment, so hallucination and inconsistency gaps carry higher multipliers. The weights are reasonable estimates, not audited figures.
What this means for the numbers. The output is a directional estimate — the right order of magnitude, not a precise forecast. Two companies with identical inputs can have very different actual costs depending on how their AI features are built and used. The calculation is a starting point for a conversation, not a conclusion.
The studies below informed the gap weightings. Sources 11-13 are new in this version — they strengthen the trust and commercial consequence arguments. Confidence level is noted per source.
This gives you a ballpark. Book a call and we'll walk through which gaps actually apply to your situation and what to prioritise first.
Book a free 30-min analysis