Business Applications Innovation Summit 2026

About this talk

The economics of software development have shifted. AI has eased the old constraint of developer capacity, letting teams produce more code, faster, than manual processes ever allowed. But that acceleration exposed a new one: confidence. More code means more states, more integrations, and more regressions to validate, and testing capacity hasn't kept pace. The result is a hidden AI tax that accumulates underneath every fast release: more inconsistency, more untraceable dependencies, and more code nobody fully understands, let alone tested.

This session reframes that tax as AI debt, a faster-compounding cousin of technical debt that builds quietly while output metrics look healthy. It offers a pragmatic look at what it takes to bring agents into every stage of the SDLC, making quality a built-in discipline, not a downstream bottleneck.

Key takeaways:
- Quantifying AI debt before it compounds into missed releases and production risk.
- Governing self-healing and self-generating systems without slowing development.
- Evolving QA expertise from maintaining scripts to defining intent, risk, governance, and acceptable outcomes.