A recent MIT study reported that only about 5% of GenAI applications are creating real, measurable business value. In my opinion, that’s not a failure of ambition. If anything, most teams are experimenting aggressively. The issue is that the underlying systems we use to deliver software haven’t adapted to what AI actually is.
It has become incredibly easy to build a prototype or demo. A few prompt tweaks, an API call, and you can show something impressive. But turning that prototype into something you can trust in production is a different challenge. That part requires real engineering: reliability, consistency, versioning, monitoring, and guardrails. The problem is that the tools and workflows we’ve relied on for years were never designed to support systems that change their behavior over time.
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