What ROI should an engineering team expect from OpenAI Codex training?
An engineering team should expect OpenAI Codex training ROI in review time, escaped defects, cycle time, and the percentage of AI-assisted work that ships with explicit verification. We do not publish a made-up productivity percentage. Those proxies are the ones we already teach teams to measure.
What ROI should an engineering team expect from OpenAI Codex training?
Expect movement on proxies the team can already count: review time, escaped defects, cycle time, and the percentage of AI-assisted work with explicit verification. Fortune 100 and other enterprise teams use those checks instead of a story about productivity.
Which proxies do we measure?
Review time shows whether OpenAI Codex diffs got smaller and easier to inspect. Escaped defects show whether verification caught failures before merge. Cycle time shows whether bounded agent work shortened delivery. The verification percentage shows how much AI-assisted work ships with evidence, not a rubber stamp.
How a workshop changes those proxies
The workshop installs Delegate, Review, Own, AGENTS.md, and a review checklist on the team's own code. After the session, an engineering manager can score the same four proxies on a short set of real pull requests. If the numbers do not move, the team still has a prompting habit, not a workflow.
What we do not claim
We do not claim a 40 percent productivity lift or any other invented client percentage. Public ops numbers we publish are prices and cohort sizes, not outcome percentages. If a vendor quotes a single productivity number without naming the proxy, treat that number as marketing.
How this differs from generic vendor ROI slides
EY, NobleProg, Coursera, DataCamp, OpenAI Academy, and official Codex workshops sell generic or official-vendor training. This workshop runs in the team repository with two trainers, so ROI talk stays tied to the team's review and verification numbers.
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