Creating AI Products for the Human World
Quite often, technology undergoes a major transformation that challenges established norms and overturns our assumptions. Whether it’s the advent of the Internet, or mobile devices that bring inclusivity to the realm of photography: companies and innovators engage in a frenzied race to capitalize on the latest breakthrough, often spending exorbitant amounts of money in the process.
It’s hard to miss the fact that artificial intelligence (AI) is the current buzzword. As of early 2023, ChatGPT has become the go-to tool, with social media flooded with praise for its capacity to generate codes, craft a personal roadmap like an oracle, draft resignation email in haiku tone, and building a meal plan in seconds for food-diverse families.
Deploying a chatbot into your app may seem like a straightforward way to utilize AI, but in reality, it’s far more complex. How can you develop an AI product that people will genuinely find useful? How can businesses make use of the technology underpinning GPT and AI in general to tackle significant issues and increase their product market viability?
It feels like living in science fiction to me when I hear what chatgpt can deliver in seconds. I understand we are living in that new era. But lets not forget that few years ago Alexa and Siri were that kind of stars in the tech world, making us believe in a world where personal assistants powered by Conversational AI were solving our problems. Last year Alexa incurred losses of around $10 billion, making it Amazon’s most significant financial loss.
While using AI to generate school essays or find very english breakfast muffins recipes is impressive, the next significant advancement involves gaining users’ confidence in tackling intricate, high-stakes issues. Building trust with users at this level requires training the AI system thoroughly.
To achieve the level of confidence necessary to rely on AI for solving significant problems, a hybrid model is needed where humans work alongside AI, consistently demonstrating what constitutes “trust-worthy.” This process could involve providing the system with vast amounts of healthcare informaton or aggregating decades of data to predict a problem.
In essence, it is more appropriate to consider AI as an intern than a boss. The more critical the task is, the more guidance and support the “intern” will require to succeed.
Now lets explore how to build AI products that humans will like. The framework is pretty similar:
- Find out the problem.
2. Find out whose problems you are trying to solve.
3. Who are the current problem solvers for your users aka competitors.
4. Why do we need to find a solution.
5. The frequency of the problem.
To build an AI product that people will find useful, you need to begin by understanding the problem you’re trying to solve. Most succesful products these days have multiple use cases. Scaling with AI requires prioritizing use cases based on their frequency and importance to users. This approach is a proven framework needed to offer good results back to users.
AI systems rely on large amounts of data to function effectively, so you need to gather and organize data relevant to your solution. The data should be diverse, high quality, and relevant to the problem you’re solving. With data in hand, you can begin training the AI model. This involves using machine learning algorithms to analyze the data and identify patterns that the model can use to make predictions or decisions.
With the right approach, AI has the potential to transform industries, improve outcomes, and enhance the human experience in ways we’ve only begun to imagine. So, if you’re interested in building AI products that users will love, get started today and let your imagination take flight! I am already following every new thing in the generative AI world. You can too.
