For core business automations, agentic AIs that "guess" are expensive and unreliable. A superior approach uses tools that convert natural language into deterministic, code-like workflows, which run consistently and use AI only when necessary.

LLMs can fail to follow critical instructions even when explicitly prompted, making them unsuitable for business decisions with 'hard constraints' like environmental regulations or budget limits. For high-stakes problems, mathematical optimization provides a defensible framework that guarantees constraints are never violated.

The current cost of using LLMs for inference is approximately 30 times higher than using a traditional, deterministic API for flight data. This significant cost disadvantage makes it economically unviable for AI-native challengers to replace the existing airline distribution business model.

Unlike a human expert, an LLM's probability estimates and conclusions can be drastically altered by simple rephrasing or irrelevant suggestions. This instability shows they are too easily "pushed around" and lack the coherent world model necessary for trustworthy, high-stakes decision support.

Alex Karp differentiates between hard-coded infrastructure and the 'magical' but limited code generated by LLMs. While LLMs excel at creating probabilistic outputs like dashboards, this 'free code' cannot function as a reliable knowledge store for critical enterprise processes. True enterprise solutions require managed, structured code that understands the world, a feat LLMs alone cannot achieve.

For most enterprise tasks, massive frontier models are overkill—a "bazooka to kill a fly." Smaller, domain-specific models are often more accurate for targeted use cases, significantly cheaper to run, and more secure. They focus on being the "best-in-class employee" for a specific task, not a generalist.

While businesses accept that employees make mistakes, their expectation for software is absolute reliability. This unforgiving standard creates a durable moat for enterprise platforms that provide deterministic outcomes, a key challenge for probabilistic AI models in critical workflows.

LLMs are technically non-deterministic systems designed to guess the next most probable word, not verify facts like a calculator. This inherent design means they will confidently produce incorrect information, making human verification indispensable for high-stakes business decisions.

For critical enterprise functions like financial modeling, 99.9% accuracy from a probabilistic LLM is unacceptable. Platforms like Salesforce's Agent Force 360 solve this by layering deterministic logic and guardrails on top of the AI, ensuring compliance and preventing costly errors where even a 0.1% failure rate is too high.