Artificial Intelligence (AI) is transforming product management at an unprecedented pace. The rise of generative AI and AI-powered development tools has opened up new possibilities, altering the way product managers (PMs) define, build, and refine products. As AI capabilities grow, so do the best practices for AI product management. In this post, I’ll share key insights and strategies that every AI PM should embrace to stay ahead.
The Power of Concrete Examples in AI Product Definition
One of the most effective ways to accelerate AI product development is by using concrete examples to define product requirements. AI models thrive on data, and similarly, AI product teams need specific examples to clarify their vision.
Consider a PM who proposes, “Let’s build a chatbot to answer banking inquiries that relate to user accounts.” This statement is too vague. Does the chatbot handle only balance inquiries? What about wire transfers or interest rates? The ambiguity can lead to misalignment within the team.
Instead, if the PM provides 10 to 50 specific conversation examples, the scope becomes clear. Just as machine learning models require training data, AI products require detailed specifications. These concrete examples function as a Product Requirements Document (PRD) in AI development, setting clear expectations and reducing misunderstandings.
Similarly, let’s say a company wants a vision system to detect pedestrians outside a store. Simply stating this goal isn’t enough. Engineers need to know:
- Should the system work at night?
- What camera angles are acceptable?
- How far should it detect pedestrians—10 meters, 50 meters, or 100 meters?
Providing annotated images with desired outcomes makes the requirement more tangible. Engineers can then evaluate feasibility and build accordingly. Initially, data collection might involve a PM taking and tagging pictures manually. Later, real-world data from a deployed system will refine the model.
AI PMs must think like AI trainers, providing the right examples to guide product development effectively.
Testing Technical Feasibility Through Prompting
Assessing the technical feasibility of AI applications is a crucial early step. Traditionally, PMs relied on engineers to build prototypes, which took time and resources. However, Large Language Models (LLMs) now allow PMs to test feasibility through prompting—even with minimal coding knowledge.
For example, imagine a PM exploring an AI-powered email routing tool that directs customer inquiries to the correct department (e.g., customer service, sales, tech support). Instead of waiting for engineers to develop a prototype, the PM can:
- Use an LLM to prompt for classification – Feeding the AI sample emails and asking it to categorize them.
- Evaluate accuracy – If the AI correctly assigns emails to departments, the project is promising.
- Refine prompts – Tweaking instructions to improve performance before handing it over to engineers.
If the AI struggles, the PM can falsify the idea early—saving valuable time and effort. Sometimes, feasibility testing requires light coding, such as implementing Retrieval-Augmented Generation (RAG) for better context awareness. Fortunately, AI-powered coding assistants, like ChatGPT and GitHub Copilot, make writing small scripts accessible even to non-engineers.
Rapid Prototyping Without Engineers
User feedback is essential in shaping AI products. Thankfully, the barriers to rapid prototyping are lower than ever, enabling PMs to build and test prototypes without professional developers.
Many no-code and low-code platforms help PMs create functional prototypes quickly:
- Replit – A browser-based coding environment.
- Vercel’s V0 – A powerful tool for front-end development.
- Bolt – Ideal for testing AI-driven automation workflows.
- Anthropic’s Artifacts – A tool designed to facilitate AI development.
Although these tools reduce technical barriers, basic coding knowledge still enhances efficiency. Non-technical PMs can experiment with prototypes, gather feedback, and iterate faster than ever before. Interestingly, even experienced developers use these tools to speed up the development cycle.
The Growing Demand for AI Product Managers
The rapid expansion of AI applications has created a surge in demand for AI-savvy product managers. While AI product management existed before the rise of generative AI, today’s PMs must adapt to new methodologies that reflect AI’s increasing accessibility and impact.
Key skills that modern AI PMs need:
- AI Literacy – Understanding core AI concepts and their applications.
- Data-Driven Thinking – Knowing how to define AI features using examples and structured data.
- Technical Experimentation – Using prompting and basic coding to assess feasibility.
- Rapid Prototyping – Leveraging no-code/low-code tools to test ideas quickly.
- Collaboration with AI Teams – Communicating effectively with engineers and data scientists.
As AI continues to evolve, so will the best practices for AI product management. The PMs who learn how to scope, validate, and iterate AI products efficiently will be the ones driving the next wave of AI-powered innovation.
The Future of AI Product Management
AI is revolutionizing the way we build products, making speed, iteration, and experimentation more critical than ever. By leveraging concrete examples, prompting for feasibility, and rapidly prototyping without engineers, AI PMs can accelerate product development and bring AI-driven solutions to market faster.
As AI continues to reshape industries, mastering these best practices will set AI product managers apart. Whether you’re an experienced PM or just entering the field, embracing these strategies will position you for success in the AI-driven future.
Stay tuned for more insights as AI product management evolves! 🚀
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