5 Tips For Creating AI Product [Newsletter 4]

It's incredible to see how much the technology space has changed just in one year after the chatGPT was released to the public. Not only does it feel like magic, but it also magically increases the value of your startup in the eyes of investors and increases your chances of securing funding…at least for now.

We all need to jump on the AI train sooner or later, so here are 5 tips for building products with AI from this year's Websummit 👇

1. Create an AI strategy for your business

Develop both short-term and long-term strategies. Short-term strategies focus on quick wins by implementing general AI models for more straightforward tasks like text summarization, recommendations, grammar corrections, etc. Long-term strategies involve training models with your proprietary data to help you gain a competitive edge.

2. Build painkillers

AI isn't a magical solution to improve your product. The same is true with any other feature you add to your product; make sure it solves real-life problems and creates customer value. As with any exciting technology, the first use cases we will come up with are most likely vitamins. Nice to haves, but won't stick. Aim to find a unique painkiller use case that genuinely stands out.

3. Garbage in → garbage out

Recognize that base AI models can be biased due to the data sets they were trained on. For example, if an AI consistently refers to doctors as "he," it reflects the inaccuracy of the training data. To avoid bias, train your AI on synthetic data sets that include diverse genders, age groups, races, and ethnicities for more balanced results.

4. Gain user trust

Many people are skeptical about AI, and even enthusiasts find it challenging to trust AI results fully. To address this, consider implementing AI as a copilot rather than fully automating processes. Allow end users to review and edit AI-generated results, building trust over time. For example, GitHub's Copilot has a 40% acceptance rate for suggested code.

5. Keep costs in mind

AI can get really expensive; implementing every single idea without considering if customers would actually want to pay extra for this might hurt you more than benefit. Always make sure that the added value justifies the cost.

In the end, AI is just a technology. Every product will eventually utilize it in some way or another. Our focus should be on creating a better product that our users love, one that adds value for the customer by reducing costs, increasing productivity, and so on.

Free AI courses

👉 Generative AI Fundamentals by Google
👉 AI product management by Pendo
👉 AI fundamentals on Udacity (in collaboration with Microsoft)
👉 Transform your business with Microsoft AI
👉 AI Foundations for Everyone Specialization by IBM

I'll see you next week my workaholic friends 💜