When developing internal AI tools, adopt a 'fail fast' mantra. Many use cases fail not because the idea is bad, but because the underlying models aren't yet capable. It's critical to regularly revisit these failed projects, as rapid advancements in AI can quickly make a previously unfeasible idea viable.

Before building an AI agent, product managers must first create an evaluation set and scorecard. This 'eval-driven development' approach is critical for measuring whether training is improving the model and aligning its progress with the product vision. Without it, you cannot objectively demonstrate progress.

The essential skill for AI PMs is deep intuition, which can only be built through hands-on experimentation. This means actively using every new LLM, image, and video model upon release to objectively understand its capabilities, limitations, and trajectory, rather than relying on second-hand analysis.

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.

A successful AI rollout requires a holistic strategy. Start with "People" (training, identifying champions), define new "Processes" (how data is logged), select the right "Platform" (testing tools methodically), and measure success with "Proof" (attaching KPIs to every initiative).

Before investing in robust API connections, test a workflow's value with the simplest possible version, even if it's held together by screenshots and voice commands. If you don't consistently use the 'janky' version for a week, the idea isn't valuable enough to build properly, saving significant time and effort.

To avoid the common 95% failure rate of AI pilots, companies should use a focused, incremental approach. Instead of a broad rollout, map a single workflow, identify its main bottleneck, and run a short, measured experiment with AI on that step only before expanding.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

Don't just plug AI into your current processes, as this often creates more complexity and inefficiency. The correct approach is to discard existing workflows and redesign them from the ground up, based on the new paradigms AI introduces, like skipping a product requirements document entirely.