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From a CIO perspective, this isn’t a disagreement about whether AI can code; that debate is over, said Gogia. Enterprises should be using AI “aggressively” to compress cycle times while keeping humans accountable for outcomes. To this end, he is seeing more bounded pilots, internal tooling, gated autonomy, and strong emphasis on auditability and security.
It is also critical to correct the “mental model” for the next two to three years, Gogia noted. The dominant shift will not be to fully autonomous coding, but to AI-driven acceleration of processes across the enterprise. “Value will come from redesigning workflows, not from removing people,” he said. “The organizations that succeed will treat AI as a force multiplier inside a disciplined delivery system, not as a replacement for that system.”
Ultimately, repeatable results will reveal whether AI systems can handle complex, multi-repository, long-lived software that doesn’t require constant human rescue, Gogia said. “Until then, the responsible enterprise stance is neither dismissal nor blind belief, it is preparation.”
