AI in Product Development?

I like product ideation brainstorming—done right and focused, it opens my mind to think much more analytically about an idea, its development, and its trajectory. But on the other hand, I often had brainstorming sessions, and they were just a waste of time. And to be honest, can you count how often a session went sideways, got stuck in the same old thought patterns, and the loudest voices in the room dominate the conversation? 

I did a test yesterday with GPT-4o, and it blew the lid off my creative potential. I had tried the same exercise with the earlier models, and it was a colossal waste of time and energy.

Adding AI To The Product Team, worth?

Short, after the test, yes, it's definitely worth. Why?

We as startup founders, product managers or developer, our job isn't just about executing on a roadmap, we have to build the roadmap and come up with the right product idea at the right time - in the first place. That means staying ahead of the curve, spotting opportunities where others see blank space. Here's how I discovered that the use of AI becomes my "unfair advantage":

• Idea Generations: AI models are trained on massive amounts of data, so they can generate novel ideas that you might never have thought of on your own. The newest one from OpenAI is a multimodal model, which means it can use different sources as combined input, like images, text and video, at the same time.

• Data-Backed Inspiration: Use it as a market research tool, even emulate customer feedback, or competitor analysis to spark ideas rooted in reality, not just pie-in-the-sky thinking.

• Rapid Prototyping: Quickly draft product descriptions, taglines, or even mockups for early testing and validation. Does the idea solve the problem users and the market have? Does it actually help to make things better or it is just a technology ride?

• Team Collaboration: In brainstorming sessions AI spark new ideas, create discussion points, and even enhance ideation across the whole team. We ran an A/B test on a new product sales strategy: We don't sell technology, what if we concentrate on customer reviews and use our technology to help?

Practical Tactics (How I see AI as CPO)

Prompting a fresh instance of any AI tools and hope for the best is not working, trust me. You need to train and explain how you think, what your current market and customer / user base tells you, how the market moves and what capabilities you and your team have. Here's some kind of thought process matrix:

• The Problem: Give your AI model a clear prompt that outlines the problem you're trying to solve. Don't just ask for "product ideas." Get specific about your target audience, their pain points, and the desired outcome.

• The Idea: Ask the AI to generate a list of potential solutions, feature ideas, or even new product categories. Don't be afraid to experiment with different prompts and constraints to get a wider range of results. Use direct prompts to initiate market research and sources.

• The Prototype: Use the AI to draft product descriptions, marketing copy, or even basic wireframes. This will help you visualize and communicate your ideas more effectively.

• The Devil's Advocate: Challenge the pros and cons of a particular idea and ask it to critically analyze it. This can help you spot potential pitfalls and refine your approach. That can be done in a role play, see my example for Apache Wayang.

How I Prompt GenAI

I start with a role play, I'm the one who challenges an idea, and the AI tries to convince the team with data driven market insights. The software in question is a federated learning stack, Apache Wayang, and we want to define one major product or use-case which decouples the project from a pure technology driven approach. I used this prompt: 

Lets's do a role play, I challenge a product idea and you are the "AI-Powered Advocate", kind of. Let's go:  

Companies struggle to manage data silos and collaborate across organizations, it's a mess and might slow AI/ML development down, in the best case. The idea now is to add a AI feature store concept to Wayang, which can transform disparate data sources into a collaborative feature store, enabling secure collaborative model training without data ever leaving its source.

Output:

As you see, we have numbers, sources and a role play which points to multiple decision matrix hooks. We used this output to iterate over potential scenarios for Wayang and how we build a feature store for the project. Saying this - the experiment was a success from my point of view, and we'll make use of our AI bot more regularly. 

TL;DR

GenAI is a powerful tool, but it's not a substitute for human creativity and critical thinking. Use it to supplement your own ideas, not replace them entirely. Also, be aware of potential biases in the AI's output and always vet the results carefully. I also tend to integrating AI into my XOps framework, the collaborative nature of XOps ensures that these AI-generated ideas are thoroughly vetted, refined, and seamlessly integrated into the product development process.

My Take

Leveraging AI for product ideation isn't just a "nice to have." It's a competitive advantage. If you want to build innovative, customer-centric products that solve real problems, genAI should be in your toolkit. Don't be afraid to experiment and embrace this new frontier of innovation.