From Idea to Product

Bridging the Gap in the Age of Generative AI

The generative AI landscape is teeming with exciting new ideas. From AI-powered marketing tools to intelligent personal assistants, the possibilities seem endless. But there's a critical gap that often separates an interesting idea from a successful product, and it's a gap that's become even more pronounced in the age of generative AI.

The Disconnect Between Ideas and Products

The power of generative AI to quickly turn concepts into working prototypes can be both a blessing and a curse. It's tempting to get swept up in the excitement of a novel idea and rush to build a "wrapper" around a model's capabilities, assuming the model's inherent functionality will carry the weight of the entire product.

But this approach often leads to products that feel more like features than standalone solutions. They might be technically impressive, but they lack the depth, user experience, and long-term vision necessary to stand the test of time – and the ever-evolving capabilities of the underlying AI models. 

This is where the existential fear creeps in for many founders and builders. What happens when the next GPT or Gemini release can do what your entire product does, but better? It's a valid concern, and it highlights the danger of building products solely on the fleeting capabilities of current AI models.

Is It a Product or a Feature?

This brings us to a crucial question: What's the difference between a feature and a product in the age of generative AI? Here’s how I think about it:

• Feature: A feature is a specific capability or functionality that solves a particular problem. In the context of generative AI, a feature might be the ability to generate marketing copy, summarize a document, or even create a meal plan based on dietary restrictions.

• Product: A product, on the other hand, is a complete, holistic experience that encompasses multiple features, a well-defined user experience, and a clear value proposition that extends beyond the capabilities of the underlying AI model.

The Importance of Product Thinking

If your entire product could be rendered irrelevant by the next iteration of a generative AI model, you don't have a product. You have a feature or a use case, and those are not enough to build a sustainable business.

The key to success lies in product thinking. It's about:

• Differentiation: What makes your product unique and valuable in a world where anyone can access powerful AI capabilities?

• User Experience: What makes your product delightful and intuitive to use?

• Long-Term Vision: Where is your product headed? How will it evolve as AI technology continues to advance?

Navigating the Stages of an Idea

Transforming an idea into a successful product requires careful navigation through distinct stages:

1. Ideation: This phase can include "defining your target audience" and "understanding their needs," but sometimes, the most potent ideas emerge from scouting for opportunities that don't yet exist, from tapping into "informed intuition" as I discussed in my previous post, "Informed Intuition over Deep Rigor". This is often the most exciting phase, where creativity flourishes and possibilities feel endless. But it's crucial to remember that ideas are just the starting point. As the saying goes, "Ideas are a dime a dozen. People who implement them are priceless."

2. Implementation: This is where the rubber meets the road. Moving from idea to implementation is a significant leap, and one that many promising concepts fail to make. It's in this stage that the real hard work begins. You grapple with technical challenges, confront the limitations of current technology, and discover unforeseen complexities. This is also where you learn the most. Quality, reliability, scalability - all of these critical factors come into sharp focus during implementation, and they're vital to determining if your idea is truly feasible beyond the initial excitement of an MVP.

3. Validation: Testing your prototype with real users is crucial, but even more vital is recognizing that just because you built it doesn't guarantee it's a good idea. The validation stage is about confronting reality, gathering feedback, and improving your product or having the courage to abandon ideas that don't resonate with users or face insurmountable technical hurdles. As I learned from my experience with the "Construction Engine" (detailed in my post, "From Dead Ends to Breakthroughs"), sometimes the best course of action is to shut down a project, learn from it, and move on to more promising opportunities (aka, go back to the Ideation phase).

4. Growth: Once you've validated your product, focus on scaling your user base, expanding your feature set, and building a sustainable business.

Short-Term Wins vs. Long-Term Vision

The question of whether to prioritize short-term ROI or focus on a bigger picture can sometimes feel like a constant tension in the world of product development. This tension has been amplified by the arrival of generative AI, and it was a prominent theme at a recent dinner I attended with  leaders from various companies.  

Many CEOs, driven by shareholder expectations and the need to demonstrate tangible results, are laser-focused on integrating generative AI in ways that can achieve quick wins and demonstrable immediate ROI. This often translates into incremental improvements to existing products and processes.  Salesforce, for example, is focused on making enhancements to their platform through their Einstein Generative AI offering, showcasing the immediate value generative AI can bring to businesses through integrating into their existing products.

However, I'm drawn to a different approach—one focused on the medium to long term. I'm captivated by the potential of generative AI to unlock entirely new product categories and redefine our relationship with technology. This perspective resonates deeply with me and with others who are passionate about innovation and pushing boundaries. Adobe's strategy with generative AI exemplifies this approach. They're not just enhancing existing workflows; they're opening up entirely new markets and empowering a broader range of users through the creative potential of generative AI.

As the person sitting across from me at dinner aptly pointed out, his (grand)mother would have never used Photoshop, but now, thanks to generative AI, she could easily create a custom graphic for a personalized card. By introducing Firefly, a whole new product offering, Adobe expanded its total addressable market (TAM), reaching an audience they might have never tapped into before.

The Future of Ideas: Execution is Everything

In the age of generative AI, ideas are still the spark, but execution is the fuel that propels them forward. By embracing product thinking, focusing on user experience, and being adaptable in the face of rapid technological change, we can bridge the gap between idea and product, creating solutions that truly make a difference.  We need to balance the allure of short-term wins with the audacity of long-term vision, always striving to build products that not only meet the needs of today's users but also shape the landscape of tomorrow's world.