An Introduction to AI Product Management - Part 1 of 3

Welcome to the first article in a three-part series that focuses on AI Product Management, an area gaining importance as AI, particularly Generative AI and Large Language Models, becomes a standard component in business solutions. This series aims to provide an in-depth understanding of how AI technologies are changing product management.

Why is this important? For context, about 35% of startups supported by Y-combinator are AI-focused, indicating a growing demand for AI skills. Additionally, AI is expected to contribute to 7% of the world's GDP, opening up numerous job opportunities. So, understanding the impact of AI, including advanced forms like Generative AI, on product management is crucial for today's professionals.

In this series, I'll break down the subject into three articles. The first article will introduce you to the fundamentals of AI in product management, including its current role and significance. The second article will dig deeper into the lifecycle of an AI product, focusing on how AI technologies, such as LLMs, are changing each stage of product development. I will also share practical examples of products that have benefited from AI integration. Finally, the third article will outline best practices and ethical guidelines for managing AI-based products effectively.

What is AI?

Artificial Intelligence (AI) is a branch of computer science focused on creating machines capable of performing tasks that usually require human intelligence. These tasks include learning, reasoning, problem-solving, and understanding natural language. From its roots in the 1950s, AI has grown to become a technology that influences many aspects of our daily lives.

Here is a brief map of its timeline

Sub-Divisions of AI

• Machine Learning: This is a subset of AI that involves the development of algorithms enabling computers to learn from data. For example, Google's search algorithm uses machine learning to provide you with the most relevant search results.
• Deep Learning: Deep learning is a specialized area within machine learning that uses artificial neural networks to analyze different aspects of data. For example, Facial recognition systems, commonly used in security measures, are a practical application of deep learning.
• Large Language Models (LLMs) and Generative AI: Both Generative AI and Large Language Models (LLMs) are subdivisions within the realm of Deep Learning, which is itself a subset of machine learning. While they share common foundational technologies like neural networks, they serve different specialized functions. Generative AI is a broader category focused on creating new data that resembles a given dataset; this can include generating images, music, or text. On the other hand, LLMs are specifically engineered for language tasks, excelling in generating human-like text based on the data they've been trained on. They can perform a variety of tasks such as answering questions, summarizing text, and even writing code. In essence, LLMs are a particular type of Generative AI that specializes in understanding and generating text.

AI Products Before the Gen AI Era

Before the rise of General AI, the landscape of AI products was a mixed bag of specialized, yet impactful, technologies and ambitious projects that revealed the limitations of early AI. IBM's Watson, for example, was tailored for natural language processing and data analytics, gaining fame for outperforming humans on the quiz show "Jeopardy!". While Watson excelled in sectors like healthcare, it was largely confined to the tasks it was specifically programmed for.

Chatbots like ELIZA served as early examples of automated customer service, but their capabilities were limited to scripted responses and keyword matching. Despite these limitations, some pioneering AI projects showcased the potential of AI in real-world applications. Microsoft's Project Murphy, an AI-powered chatbot introduced in 2016, was designed to answer hypothetical questions and even merge two images into a humorous composite, demonstrating the potential for AI in creative applications.

Another Microsoft initiative, Tay bot, was a machine learning project aimed at understanding conversational language but ran into issues due to its inability to filter inappropriate content. Though Tay bot was a cautionary tale in AI development, it contributed valuable lessons about the importance of ethical considerations and data filtering.

On the successful side, machine learning algorithms have had notable successes, especially in the realm of data analytics, predictive modeling, and automation. In finance, machine learning models have been effectively used for risk assessment, fraud detection, and market trend analysis, proving the utility of AI in specialized tasks. These algorithms, although not as versatile as General AI, have been instrumental in solving specific problems, paving the way for the more advanced AI technologies we see today.

Gen AI Era and Product Development

The advent of General AI has ushered in a transformative phase for product development, offering capabilities that go far beyond the specialized tasks that earlier AI technologies could handle. For instance, OpenAI's GPT-3 gained widespread attention for its ability to perform a multitude of functions, from code generation to answering complex queries and even composing music. Its successor, GPT-4, has further pushed the boundaries, offering even more robust and versatile functionalities. These advanced models have lowered the barriers to integrating sophisticated AI features into products, as they can often replace traditional machine learning models with a simple API call and a prompt.

Similarly, other platforms like Google's PALM API and Meta's Llama 2 have emerged as powerful tools in this new era. PALM API offers advanced language understanding capabilities, while Llama 2 specializes in multi-modal learning, combining text and image understanding. The key advantage is the ease of integration. With well-crafted Large Language Model (LLM) integration, these advanced technologies can be plugged into existing systems seamlessly, making it easier than ever to add AI functionalities to a wide range of products. These developments indicate a significant shift, as complex AI capabilities that once required extensive resources and expertise can now be accessed more easily, accelerating the pace of innovation in product development.

Technical Proficiency and Ethics in AI Product Management

Product managers need to know about AI technology to make good decisions. Being able to understand how AI works helps in planning and making products better. Besides knowing the technology, it's also important to think about ethical issues like keeping people's data safe and making sure the AI is fair to everyone. For example, some facial recognition systems have been found to have problems identifying people of certain races or genders. This shows why it's important to follow ethical rules when making and using AI products.

Upgrading Product Managers with AI Skills

To keep up with the changes in technology, product managers need to learn new skills related to AI. This includes knowing how to look at data and understand what it means, understanding the basics of how machine learning works, and knowing the ethical rules for using AI. For example, if a product manager understands data analytics, they can better decide what features to add to a product based on what users like or don't like. Also, by understanding the basics of machine learning, a product manager can better work with engineers to build smarter products. Learning about ethics is also important so that the products they make are safe and fair for everyone to use.

Wrapping Up and What's Next: A Glimpse into Part 2

In this article, we've covered the basics of AI in product management. We looked at what AI is, its different types, and how products used to be before advanced AI like GPT-4 came along. We also discussed why it's important for product managers to understand both the technical and ethical sides of using AI in products.

Stay tuned for Part 2, where we'll dig deeper into how an AI product is made, from the first idea to the final product. We'll also talk about the tools and methods that help make AI products successful. You won't want to miss it!