AI Hierarchy of Needs: Exploring the Building Blocks of AI Products

AI Hierarchy of Needs: Exploring the Building Blocks of AI Products

Exploring the foundational elements that make AI systems successful.

Similar to Maslow’s Hierarchy of Needs, which outlines the basic needs required for human survival, the AI hierarchy of needs outlines the foundational elements that every AI system needs to be successful.

AI Hierarchy of Needs

Let’s take a look at each of these levels.

AI Hierarchy Needs

Level 1: Data

AI systems require large and diverse amounts of training data to learn from. Asides quantity, the quality of this data matters as the data has to be clean, accurate and relevant. Data quality and quantity are critical for the success of any AI system, there should be a robust and reliable data pipeline.

You also consider what’s the best way to source your data, this could be open data, user-generated, data gathered from devices and sensors, external data, etc.

Level 2: Infrastructure

After sorting out your data source(s), the next important step is infrastructure. This is all about the computing power and necessary storage that can process and analyze large volumes of data. AI systems require reliable and easily scalable infrastructure to support their operations.

This includes both hardware and software components, as well as connectivity and security measures to ensure data integrity.

Level 3: Algorithms

AI systems require intelligent algorithms that can interpret and make sense of the data collected. These algorithms enable AI systems to learn from the data and make predictions or decisions based on what they learn.

Level 4: Applications

This is all about the specific use cases or tasks that an AI system is designed to perform. These can range from simple tasks, such as recognizing images or speech, to more complex applications, such as self-driving cars or medical diagnoses.

Level 5: Ethics & User Experience

This refers to the ethical considerations that must be taken into account when designing and deploying AI systems.

AI systems require ethical principles to guide their decision-making processes. This includes considerations such as fairness, transparency, and accountability, as well as the potential impact of AI systems on society as a whole.

AI systems must also be designed with the end user in mind. This includes ensuring the user experience of AI products is intuitive, seamless and easy to use. Good user experience helps ensure that the product is adopted and used effectively.


The AI hierarchy of needs provides a useful framework for understanding the various levels of development and deployment required for successful AI systems.

By addressing each level in turn, from data and infrastructure to algorithms, applications, user experience and ethics, AI researchers and developers can ensure that their systems are built on a solid foundation and designed to operate in a responsible and ethical manner.