# Opinion: AI adoption may widen existing engineering blind spots

Digest AI · Enterprise & Industry · published 2026-09-24T06:36:00Z

Canonical: https://digestai.news/story/opinion-ai-adoption-may-widen-existing-engineering-blind-spots

## Summary

A CTO argues that AI tools amplify existing organizational weaknesses rather than creating new ones. Citing the 2025 DORA report, the author notes that while AI adoption correlates with higher delivery throughput, it also leads to increased delivery instability. The author’s own team experienced a 48% increase in throughput over two quarters, followed by a 16% rise in stability issues, illustrating that speed gains do not automatically offset quality risks.

The piece identifies five specific blind spots where traditional ticket-based systems fail to capture AI-driven work: velocity theater, review debt, hidden work, quality drift, and unproven spend. For example, a Stanford case study mentioned in the text found that AI adoption lifted PR count by 14% but caused code quality to drop by 9%, with variance more than tripling. Additionally, a survey of engineering leaders found that 80% of teams spend at least 10% of their time on code review, with roughly one in ten spending more than 40%.

The author contends that engineering leaders must move beyond intuition and anecdotal evidence to prove AI ROI to finance and leadership. By observing actual codebase changes and deployment frequency against historical baselines, teams can distinguish between the "tuition cost" of learning AI and genuine systemic failures. The core argument is that leaders must first understand their current blind spots before investing further in AI tools.

## Key points

- The 2025 DORA report found AI adoption correlates with higher throughput but rising delivery instability.
- A Stanford case study showed AI lifted PR count 14% but dropped code quality 9% and tripled variance.
- A survey found 80% of teams spend at least 10% of time on review, with one in ten spending over 40%.

## Why it matters

Engineering leaders face pressure to prove AI ROI while managing increased instability. Ignoring these blind spots risks amplifying technical debt and quality issues, making it harder to justify continued investment to finance and boards.

## Sources

1. [Your Systems Already Have Blind Spots. AI Just Makes Them Worse.](https://unite.ai/ai-software-engineering-blind-spots) (Unite.AI, 2026-09-24)

## Cite

Digest AI, "Opinion: AI adoption may widen existing engineering blind spots", 24 September 2026, https://digestai.news/story/opinion-ai-adoption-may-widen-existing-engineering-blind-spots

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