Part 3/13:
Despite this rapid growth, traditional ML remains vital for precise, niche use cases where accuracy and reliability are critical. As organizations experiment with gen AI, they encounter challenges—particularly in transitioning from prototypes or pilots to scalable, production-level solutions. Concerns about quality, trustworthiness, cost, and governance are central to this transition.
Major Challenges in Deploying Enterprise AI
Enterprises face several hurdles when deploying gen AI at scale:
- Response Quality and Hallucinations: Ensuring responses are accurate and grounded in enterprise data is crucial. Hallucinations—where generative models produce plausible but false information—pose significant risks, especially in sensitive domains.