How to Build AI Products for More Users: A 2024 Guide

Summary

An AI-native product uses AI for a main feature from the start. Begin with a problem customers want to solve. Prepare data, choose tools, test the model in the product, and track its cost, speed, and results. Plan for the computing systems the product needs as it grows. Consider privacy, security, and user feedback too.

Introduction

Artificial intelligence (AI) is part of many products. An AI-native product builds a main feature around AI from the start. For more tools, see our AI tools guide for entrepreneurs.

These examples reflect how companies described their products in May 2024. Check product documents for current features.

What is a product built around AI?

A traditional product may add AI later. An AI-native product uses AI for a key task from the start. It may use machine learning (ML). ML helps computers find patterns in data. It may also use natural language processing (NLP). NLP helps computers work with human language.

Main parts of a product built around AI

AI at the center: AI supports one or more main tasks. It shapes how the product works and what customers can do.
Model Updates: Some models do not keep learning after launch. Teams can retrain them with new data, test the results, and release an update. See AWS guidance on retraining models.
Growth: Plan systems that can handle more users and requests as the product grows.
How people use it: AI can adjust results for each user. Test changes with users to see whether they help.

How to build an AI product for more users

Build and test the product in stages.

1. Choose a user problem and set a goal

Start with a problem the product can solve. Talk to users and set a goal you can measure.

Identify User Needs: Ask target users which tasks feel slow, hard, or costly.
Set a Goal: Choose a measure, such as minutes saved per task or completed sign-ups.

2. Collect and check information

Collect data that represents the task. Check that it is accurate, complete, and up to date.

Data Sources: Use sources such as product events, transactions, or customer feedback. Collect only the data you need.
Clean the Data: Remove errors and duplicate records. Check the data before training a model.

3. Choose a model and tools

Choose a model and software that fit the task and data.

Machine Learning Approaches: Supervised learning uses examples with known answers. Unsupervised learning finds patterns without known answers. Reinforcement learning improves by trying actions and using feedback. Pick the method that matches your task.
Software Tools: Libraries are collections of code that help teams build or use models. TensorFlow, PyTorch, and scikit-learn are examples. Check their documentation before choosing one.
Shared work area: Use a shared place to run tests and record results.

4. Build and train a model

Use your data and tools to build and train a model.

Choose and adjust a model: Pick a model for the task. Change its settings, then check whether its results meet your needs.
Train the model: Use prepared data to train it. Larger models or datasets may need more computing power. Online services can help if your computers are not powerful enough.
Check model results: Compare its results with your goal. If it misses the target, make a change and test again.

5. Connect the model to the product

Connect the model to the product and test the full workflow.

Connect with an API: An API lets the product send a request to a model and use its reply.
Make results easy to use: Make the model’s output easy to understand and act on.
Test and fix problems: Try real tasks in the product. Fix failures and test again.

6. Release the product and check how it works

Release the product for customers. Plan for the number of requests you expect.

Release the product: Make it available to customers. Online services can handle the work if they fit your needs.
Check and update after launch: Watch speed, cost, errors, and user feedback. Update the model when tests show a problem or a clear improvement.

Plan for more users and requests

Scaling means serving more customers without losing quality or reliability.

1. Choose systems that can grow

Plan for higher demand. Choose systems that can expand as needed.

Online computing services: These services let teams rent computing power online. AWS, Google Cloud, and Azure are examples.
Build in parts: Teams can update parts without changing the whole product. This can make later changes easier.

2. Check speed, cost, and accuracy

As use grows, check speed, cost, and accuracy.

Improve the model: Track speed, cost, and accuracy. Change settings or choose another model if results miss your target.
Manage computer resources: Track memory. Share requests across computers if one cannot handle them.

3. Ask users what works for them

Ask users where the product helps or causes trouble. Use their feedback to guide changes.

Personalization: Use data to adjust what each user sees. Test whether those changes help.
User Feedback: Review comments and support requests. Look for a problem you can fix.

4. Protect information and privacy

Protect user data and limit access to people who need it.

Data security: Encrypt user data so others cannot read it without a key. Limit access to people who need it. Check for security problems often.
Follow privacy rules: Check which rules apply where you work. Explain what information you collect and how you use it.

5. Keep testing and improving the product

Keep testing the product. Record changes and results, then keep the changes that meet your goal.

Test one change at a time: Record what you changed and what happened.
Work across teams: Share needs and results with product, engineering, and data teams.
Learn the tools: Help the team learn what it needs for the product.