Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.

Effective enterprise AI deployment involves running human and AI workflows in parallel. When the AI fails, it generates a data point for fine-tuning. When the human fails, it becomes a training moment for the employee. This "tandem system" creates a continuous feedback loop for both the model and the workforce.

An AI product's job is never done because user behavior evolves. As users become more comfortable with an AI system, they naturally start pushing its boundaries with more complex queries. This requires product teams to continuously go back and recalibrate the system to meet these new, unanticipated demands.

Initial failure is normal for enterprise AI agents because they are not just plug-and-play models. ROI is achieved by treating AI as an entire system that requires iteration across models, data, workflows, and user experience. Expecting an out-of-the-box solution to work perfectly is a recipe for disappointment.

Many AI projects fail to reach production because of reliability issues. The vision for continual learning is to deploy agents that are 'good enough,' then use RL to correct behavior based on real-world errors, much like training a human. This solves the final-mile reliability problem and could unlock a vast market.

Unlike traditional software, AI products are evolving systems. The role of an AI PM shifts from defining fixed specifications to managing uncertainty, bias, and trust. The focus is on creating feedback loops for continuous improvement and establishing guardrails for model behavior post-launch.

The critical challenge in AI development isn't just improving a model's raw accuracy but building a system that reliably learns from its mistakes. The gap between an 85% accurate prototype and a 99% production-ready system is bridged by an infrastructure that systematically captures and recycles errors into high-quality training data.

To successfully implement AI, approach it like onboarding a new team member, not just plugging in software. It requires initial setup, training on your specific processes, and ongoing feedback to improve its performance. This 'labor mindset' demystifies the technology and sets realistic expectations for achieving high efficacy.

Unlike deterministic SaaS software that works consistently, AI is probabilistic and doesn't work perfectly out of the box. Achieving 'human-grade' performance (e.g., 99.9% reliability) requires continuous tuning and expert guidance, countering the hype that AI is an immediate, hands-off solution.