AI policy in 2026 increasingly focuses on why artificial intelligence adoption still struggles to scale across organizations despite rapid technological advancement. One of the main barriers is not the capability of AI itself, but the gap between experimentation and production deployment, where companies often fail to integrate AI into xitff.com.br core business workflows. Regulatory uncertainty, data privacy concerns, and inconsistent governance frameworks also slow down large-scale implementation.

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You’ve highlighted a crucial issue—the real challenge isn’t building AI models, but successfully integrating them into core business workflows. Many organizations get stuck in the experimentation phase because they lack clear governance, scalable infrastructure, or alignment between technical teams and business goals. Regulatory uncertainty and data privacy concerns only add to the hesitation, making it harder to move confidently into production. It’s a bit like developing a resin driveway—having the right materials is important, but without a solid foundation and proper execution, you won’t achieve a durable, long-term result.