Is the product you’re building a good candidate for AI?

Written By Noelle Saldana

Welcome to Part 3 of our comprehensive 6-part series, Beyond Buzzwords: Finding your Purpose for AI , a collaboration by Noelle Saldana with Martina Hodges-Schell, product innovation and transformation expert

Once you are clear in your purpose for AI, a key question to ask at the start of your AI journey is whether the product you’re building is even a good candidate for AI. 

We’ll share a tough truth here: AI can’t fix fundamental problems with your product. It’s not a magic wand that makes everything better. AI is a broad technical landscape, and its implementation being more complex than it may seem at first (more on this later.) This is why it’s so important to take an honest look at where your product is today and identify any areas where it’s lacking before you try to move forward with a new AI project.


To get started, you need to ask yourself:

  1. What added value will AI capabilities bring to your product?

  2. What pain could AI alleviate for your users? 

  3. Does your product roadmap include any of the following product foundations: scaling, addressing technical debt, platform migrations 

  4. How is data instrumented on your product today? What existing data-driven feedback loops do you have for the product? 

  5. Who owns data/AI in your organization?


To state the obvious: if there is no defined value for AI or any ownership of it in your org, this is a nonstarter. If your product has no data instrumentation or is saddled with deep technical debt, your energy is better spent addressing those first. 

Additionally, AI should not be a solution in search of a problem. AI should help accelerate solving pre-existing user pain points/opportunities. You’ll want to invest some time doing discovery to identify the problem or opportunity you’re trying to address, how to best solve this problem, whether your solution is usable and valuable, and whether it’s possible to create a sustainable business model. In the next post, we’ll review how to align these problems with AI solutions, if appropriate.

When it comes to figuring out how to best solve the identified problem, we suggest using the guardrails of creativity, pragmatism, and rigor. 


Products that are too far to any edge will be problematic for different reasons. For example, when you apply rigor and creativity without pragmatism, this is when you end with CEO pet projects that flop (because there was no practical application in the first place). Or, if you apply rigor and pragmatism with no creativity, this leads to products like government websites that people use because they have to, but it’s an unequivocally unpleasant experience.

Products with the most value are in the sweet spot where all three overlap; the user experience is delightful, it serves a purpose that encourages user engagement, and is adequately vetted and responsible.

Finally, we’d be remiss not to point out that creativity is not a linear process. If you want to foster true innovation, you need to be prepared for the messiness that it can involve and the detours teams may need to take before they arrive at a solution. We’ll be sharing more about how to create the ideal conditions for creativity and innovation in the next section. 

This is part 3 in the series. Read on:

Noelle Saldana
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