I have frequently received questions about the impact of Artificial Intelligence (AI) on the product management function. I’ve even heard concerns about whether AI will replace product management and I’ve seen job advertisements for AI Product Managers. In this article, I describe how I see the impact of AI on the product management function.
With ChatGPT, the AI-based product from the company OpenAI that creates responses to questions and demands from a text base, which was launched at the end of November 2022, many people began to come into contact with the use of Artificial Intelligence, Generative AI, which is the ability of software to create texts, images, music, audio, video, and other types of content based on demands and/or questions and a large information base, such as other texts, images, music, etc. ChatGPT was originally launched as a text-generative AI.
ChatGPT is perhaps the most popular AI product and several others have emerged after it, but AI is not something new. It is an area of study that began to be discussed by Alan Turing in the 1950 article “Computing Machinery and Intelligence” where he proposed the famous Turing test, which tests the ability of a computer to exhibit intelligent behavior equivalent to that of a human being, or indistinguishable from it.
Since then, AI has developed a lot, until reaching the point of Generative AI and ChatGPT as the product that popularized AI.
Therefore, if you have been working in product development and management before 2023 and only started to be interested in AI last year, you are late, very late! You should have been working with data, data science, machine learning, and artificial intelligence for some time and should already have at least one test case for using AI in one of your products.
I wrote about the importance of data for people managing products in 2012, in the article Be a “Data Geek” and reinforced the importance not only of data but also of data science, machine learning, and artificial intelligence for product management in another article in 2017.
At Locaweb, in 2014, we started using data science to try to predict which customers were more likely to churn. At Conta Azul, in 2017, our bank reconciliation feature understood the type of expense that was being recorded based on its description and learned as it was used, making increasingly accurate expense categorization suggestions and recommendations. At Gympass, in 2019, we started using AI to suggest new gyms for users to try, based on usage from other users and previous recommendations we had made.
At Lopes, we used to say that we were going beyond digital transformation, committed to transforming based on data and artificial intelligence. Understanding the enormous potential of our database, which was not only large but was updated with new data at a very fast pace, we decided in 2021 to make a strong investment in creating a data-driven culture. We had a team we called Big Data that was organized into three functions: data engineering, to create and manage the infrastructure and tools for data management; data analysis, for report creation and management; and data science, to look for patterns and insights from our data.
Along with this Big Data team, we had a data product manager. This team created and led several initiatives to help us achieve the goal of creating a strong data culture across the company and exploiting this strength we had:
All this before the “AI boom” that happened with the launch of ChatGPT. See in the graphs below that ChatGPT helped popularize AI, but AI has existed and generated interest for a long time:
What we saw in 2023 about AI has happened other times in the past. A technology that had already existed for several years became the main focus of the media, companies, and professionals due to a popular application of this technology. I will cite 3 examples:
When we have a new technology, or a technology that already exists but has gained popularity, we need to evaluate, or re-evaluate, together with the product development team the impact of this technology on our product from 3 different perspectives:
Based on this assessment, or reassessment, together with the team we must design and execute a plan to make the best possible use of these impacts.
Virtual reality, augmented reality, and metaverse are already well-known concepts. Already in the 1950s, the American Air Force used flight simulators to train its pilots, in virtual reality technologies. Augmented reality began in the 1960s, and in the 2010s we saw Google launch Google Glass but with limited success. In the metaverse field, Second Life, Minecraft, and more recently Roblox are well-known virtual worlds. And Facebook made a move to change its name to Meta, betting on the metaverse, but also with little success so far.
In June 2023, Apple introduced the Apple Vision Pro. It will be available for pre-order on Jan 19, and available from Feb 2.
Could it be that, like with the iPhone, Apple will once again create an inflection point for virtual reality, augmented reality, and metaverse technologies with what they are calling “spatial computing”? As product people, we must not only follow closely but also understand the potential impact of the popularization of these technologies on the products we care for.
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