How PMs Can Build AI-First Products from the Ground Up (2025 Guide)

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Hey there, product people! 👋

Welcome back to Rashdan’s Huddle. If you’re new here, this is where we unpack the nitty-gritty of product management and explore how AI is transforming the way we build. Today’s edition is one I’m particularly excited about because it gets to the heart of where our industry is headed in 2025 and beyond: AI-First Products.

Let’s be real: AI isn’t just a shiny add-on anymore. It’s rapidly becoming the foundation of successful products. As PMs, we need to shift from “How can we add AI?” to “How can we build with AI at the core from day one?” In this guide, I’ll break down exactly what that means and how you can lead the charge.

Here’s what we’ll cover:

• What it really means to build AI-First products

• Key principles to bake AI into your product strategy from the ground up

• Practical tools to help you get started

What Does It Mean to Build AI-First Products?

Let’s clear up a common misconception: an AI-First product isn’t just a product that uses AI, it’s a product that couldn’t exist without it.

Think about Google Photos. Its magic: automatically organizing your memories, recognizing faces, objects, and places is powered by AI every step of the way. That AI isn’t a bonus feature; it’s the beating heart of the app.

In AI-First products, AI is not a layer you sprinkle on after development. It’s foundational to how the product works, learns, and improves.

Core Factors for Building AI-First Products

1. Data Is the Foundation

If AI is the engine, data is the fuel. Without the right data, your AI product is dead on arrival.

This means you must invest early in strong data infrastructure. Clean, well-labeled, and relevant data sets are critical. I always recommend evaluating your data pipelines from the start. Tools like Segment and Amplitude can help you collect high-quality, granular user data that trains your models to deliver meaningful results.

Take Spotify as a case study: its recommendation engine thrives because it’s constantly fed a goldmine of listening data. The more users interact, the sharper its AI becomes. Without that constant data flow? No Discover Weekly.

Pro tip: Don’t just collect data. Design systems to make it actionable. 

2. Build Cross-Functional Teams

AI-First products are team sports. It’s not enough for PMs and engineers to work in silos. You need tight collaboration between:

• Data scientists

• Engineers

• UX/UI designers

• AI specialists

• Marketing & ops teams

The key is alignment. Everyone should share a vision of how AI enhances the user experience.

Tesla’s Autopilot is a perfect example. It’s not just clever engineering; it’s a coordinated effort between AI experts, designers, and engineers who together ensure the system is safe, intuitive, and effective.

My rule: Set up regular syncs to keep cross-functional teams aligned, and always make space for feedback from every discipline.

3. Solve Real Problems, Not AI Hype

This one’s big. I’ve seen too many teams get swept up in “AI-washing”, using AI just because it’s trendy.

But here’s the truth: AI is a means to an end. Your first job as a PM is to deeply understand your users’ pain points. Then, and only then, figure out how AI can solve them better than traditional approaches.

For example: If onboarding is a sticking point, think about an AI-powered onboarding coach that adapts in real time to user needs. That's the real value.

Grammarly nails this: Its AI helps users write better, spot errors, and fine-tune tone in real time. It’s AI deployed with precision to solve a clear, meaningful problem.

4. Build for Continuous Learning

The magic of AI is its ability to learn and improve over time. But that doesn’t happen automatically, you need to design for it.

Feedback loops are essential. Set up A/B tests, monitor performance metrics, and keep iterating. Tools like Google Analytics, Mixpanel, and even in-app surveys are invaluable for gathering the data that will keep your AI sharp.

Amazon’s Alexa is a standout example. Every interaction feeds into a loop that makes Alexa smarter: whether it’s learning new accents, understanding context better, or anticipating user needs.

Tools to Get You Started

Here are some of my go-to tools to build AI-First products:

• Google Cloud AI: A suite of pre-trained models and tools for custom AI development.

• Azure Machine Learning: Great for scaling machine learning workflows.

• TensorFlow: My favorite open-source library for building sophisticated ML models.

• Amplitude: For tracking user behavior and closing the data feedback loop.

Tip: Don’t get overwhelmed by the tech stack. Focus on your product vision first, and let the tech follow. 

In the next 5 to 10 years, AI-first will be the default approach for new products. As PMs, it’s on us to lean in now: understand AI’s capabilities, build smart infrastructure, and stay relentlessly user-focused.

This is a huge opportunity to not just keep up with change but to lead it.

Until next time, keep building awesome products!  

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