The most valuable AI systems are built by people with deep knowledge in a specific field (like pest control or law), not by engineers. This expertise is crucial for identifying the right problems and, more importantly, for creating effective evaluations to ensure the agent performs correctly.
Building a functional AI agent is just the starting point. The real work lies in developing a set of evaluations ("evals") to test if the agent consistently behaves as expected. Without quantifying failures and successes against a standard, you're just guessing, not iteratively improving the agent's performance.
Gaining buy-in for AI projects requires different cultural approaches. In North America, building a quick demo to showcase potential ROI is effective. In East Asia, a more disruptive demo can backfire; it's better to align with a stakeholder-driven initiative and secure a formal experimental project budget.
To avoid common pitfalls in AI development, treat building an agent like making a burger. Ensure you have all core components: a model (patty), tools (condiments), knowledge/memory (vegetables), and guardrails (bun). While the specific 'ingredients' can change, omitting any component results in an incomplete or broken agent.
To find tasks ripe for AI automation, simply screen record yourself performing a repetitive, hour-long task. Then, upload the video to a multimodal LLM like Gemini 3 and ask it what parts can be automated and how much time you could save. This provides concrete, actionable suggestions.
Don't get distracted by flashy AI demonstrations. The highest immediate ROI from AI comes from automating mundane, repetitive, and essential business functions. Focus on tasks like custom report generation and handling common customer service inquiries, as these deliver consistent, measurable value.