The Influence of AI in Product Management

Introduction

Technology usually evolves to adapt to the needs and processes of markets, companies, and teams, aiming to improve productivity and efficiency. As Product Managers (PMs), we are increasingly seeing in our daily work that AI and other technological tools are becoming a key support across various fronts.

To begin with, many of us are using AI to handle daily tasks, such as writing emails, summarizing conversations and meetings (for instance, condensing discussions in channels like Slack), creating actionable task lists, and preparing presentations, reports, and articles.

But digging deeper, we also see that AI is beginning to facilitate the product discovery phase. For example, it helps prepare key questions before a meeting with stakeholders and offers examples that inspire PMs to identify use cases and potential risks. It also simplifies localization and cultural adaptation processes, helps identify test cases and validation scenarios, and accelerates the design of mockups and initial prototypes. These capabilities are especially helpful for PMs who are new to an industry, product, or region, allowing them to better understand their context and considerations, thus improving the quality of their work.

Similarly, AI can assist in writing user stories and scenarios, as well as in typical Scrum Master tasks: creating queries, tracking a team's velocity, or identifying bottlenecks, among others. This helps free up time for teams to focus on higher-value strategic activities.

Does AI Bring Changes to Processes?

In recent times, AI has not only facilitated operational tasks within teams, but it also seems to be introducing some changes in product processes. A clear example is the faster and more cost-effective creation of POCs (Proof of Concept). AI tools enable the creation of evolving prototypes in much shorter times, meaning companies can validate their ideas more frequently and quickly. Modeling solutions and automatic frontend creation tools also lead to more efficient development, reducing the technical burden and shortening the time needed to bring a product to market.

Additionally, AI is transforming automated testing. Today, AI not only generates tests automatically, but also facilitates the automated writing of user stories, use cases, and test scenarios, while helping to identify risks. It provides examples and suggestions as "benchmarks" to avoid reinventing the wheel or overlooking important cases. This speeds up the validation process and ensures broader coverage, allowing teams to focus on higher-value strategic aspects.

Consequences of Change

We believe that one of the most immediate effects of this technological acceleration is more aggressive competition in the market. As companies adopt AI and other advanced technologies, they are forced to move at a faster pace. The ability to innovate is accelerated, driven both by technological changes and the emergence of new capabilities, pushing companies not to fall behind. This increase in development productivity raises the question: how are these additional resources being used? Some companies may choose to create more MVPs, launching more experiments in the market to quickly validate ideas. Others might opt to improve the quality of their products, focusing on adding more features, refining the user experience (UX), or improving code quality and conducting more tests. On the other hand, companies could seek to optimize their operations to minimize costs, reducing the size of teams or even decreasing the number of teams required to manage product development. These trends are beginning to take shape, and companies that do not adopt AI risk falling behind in increasingly competitive markets.

Technology allows us to do more with less, but it remains crucial to have experienced people who know how to leverage AI and discern what is useful and what is not. Consequently, roles and the number of people needed in the process could be redefined, depending on both team composition and management’s strategic decisions.

A More Efficient and Focused Future

AI not only brings speed and efficiency but also changes the way companies conceive their product processes. It's no longer just about automating tasks but about leveraging this new potential to be more strategic, reducing uncertainty, testing more ideas, and optimizing both costs and product quality.

Some Things Don’t Change

Despite of the improvements mentioned, the product management framework has not fundamentally changed. The key responsibilities and tasks of Product Management remain, but now they are supported by tools that facilitate various stages. However, real validation remains a deeply human process, and we believe the timing in this area is unlikely to decrease significantly, especially if the goal is to obtain real and accurate data instead of superficial results.

More Innovation and Competition

The biggest impact for product teams is that AI frees up time and resources, allowing them to focus more on continuous discovery and more robust validations, potentially leading to a more constant cycle of innovation. Additionally, tasks that can be more easily automated indicate that more operational roles are likely to decrease, while strategic roles like Product Management, will remain essential. Thus, teams can spend less time on repetitive tasks and more time exploring new opportunities and validating hypotheses, strengthening the strategic capacity of the product area.The question for organizations now is not whether they should adopt AI, but how to use it effectively to leverage its transformative potential.