Six Enterprise AI Adoption Challenges and How Docker's Latest Tools Address Them

AI isn't coming to your software teams. It's already there. Developers are running local models, pulling AI-optimized images, connecting autonomous agents to codebases and cloud APIs, and integrating AI tools into every stage of the development lifecycle. The question for security, platform, and executive leadership isn't whether to allow it. It's whether you govern it or pretend it isn't happening. The risks are well-documented: unpredictable inference costs, unvetted images and tools entering the supply chain, autonomous agents with write access to production systems, and no audit trail across any of it. Without a deliberate architecture, this becomes Shadow AI. Docker's recent AI-focused releases address these challenges directly. Here's how they map to the concerns platform and security teams are navigating right now. The Challenges (and What Addresses Them) 1. "AI inference costs are unpredictable and growing fast." Docker Model Runner + Remocal/MVM + Docker Offload Docker's "Remo