How AI Has Impacted Product Management

At this point, it might be a little hard to imagine an industry that has not yet been affected by AI at least to some degree. So when it comes to tech, talking about the impact artificial intelligence has had on a certain direction, you need expertise to fully understand the changes it is causing and how far it has come. I can tell you that AI is still far from replacing us, but it can already help you do crucial things in regards to product management. Honestly, I find my hand reaching for GPT quite a lot, in a way similar to how developers utilize things like Copilot — more as a partner than a standalone solution. But in this article, I want to talk more about the broader influence of AI in product management tools, more than I’d like to go into chatbot-specific applications. That is for another article. Why? Because for those leading the industry, understanding the reality is very important: AI is already embedded in almost every tool we use at work and will continue to weave itself into every aspect of our industry. You need to understand the scope and capabilities of AI to recognize the boundaries and possibilities it has. Now, many processes are very democratized and accessible which has transformed how products are managed and developed. Classification and Examples of AI Technologies in Product Management Introduction to AI Technologies For product managers, navigating the AI ecosystem can change a lot. The way we design, improve, and manage our products is dependent on our brain, and adding a tool that can diversify the process can bring strikingly different results. AI is not just a single technology but a broad spectrum of tools and methods. With it, you can turn boring tasks into opportunities for growth, all you need is to actually know what is available on the market. If you understand the tech that is available to you, this knowledge can empower you to make informed decisions and leverage the right tools for your projects. And you should know, it’s not about asking ChatGPT to create a roadmap or to act like your target audience and answer your questions. What is it about then, you might ask? Let’s first recall the general concepts of AI: Kinds of AI Tech 1. Artificial Intelligence (AI) are machines designed to mimic human intelligence and perform tasks such as recognizing patterns, making decisions, and solving problems. For product management, AI can do things like automating routine data analysis, enhancing decision-making processes, and streamlining user interactions. Machine Learning (ML) is a subset of AI that gives systems the ability to automatically learn and improve from experience without actually being programmed to do that. So, in product management, ML can be very useful for predictive analytics, helping anticipate market trends or customer behavior based on historical data. Deep Learning (DL) is an even more complex subset of ML, one that has and uses advanced computational power to process data through layers of neural networks. You can find this useful for image and speech recognition features, making new better ways for how users interact with our products. Example Tool: TensorFlow – an open-source platform that lets you develop DL models. It's key for building in things like image recognition systems that can automatically tag photos in a social media app or analyze user videos. Natural Language Processing (NLP) is crucial AI tech for products that deal a lot with human language. Why? It enables computers to understand, interpret, and generate human text in a useful and meaningful way. For products that rely on customer feedback, support systems, and content generation, this can be key. Example Tool: OpenAI GPT – hopefully, does not need an introduction, but it can generate contextually relevant text based on the input it receives. Can be used in chatbots and customer service tools to provide responses that are accurate and make sense. Generative AI – AI methods that can generate new content, from text to images and music, automating creative processes and generating relevant user content. Example Tool: Adobe Firefly is famous for making images from text descriptions. You can use Firefly to generate unique user interface elements or marketing materials, which readily streamlines the design process. And creativity! Main Fields of AI Implementation in Product Management It is a given that to really harness the full available potential of AI you need to understand what’s going on, and where and how you can integrate it into your management routine. To start off, here are three areas that can really use AI’s power: 1. Data Analysis Focus: Operating efficiencies and pattern recognition. Data is essential for product management, and it so happens that AI is great at sorting through large datasets to spot trends, anomalies, and patterns that might not be immediately clear. This is especially helpful during initial market research, where you really need to understand customer behaviors, market conditions, and potential opportunities to get better and bigger. Even after you launch your product, AI can continue to analyze, let’s say, user interactions with the product, which will be very helpful in future updates and improvements. 2. Experimentation Focus: Scaling innovation excellence. To innovate you need to take risks and experiment. And that includes testing. AI can automate and optimize testing (such as A/B) which makes these experiments more efficient and statistically sound for you. And when you plan and develop you can use AI to simulate user reactions to different features or changes, so you can refine your approaches before going to market. With AI here you get less risk of failure and faster testing/innovation cycles. Communication Focus: Elevated and effective interactions. Effective communication is the foundation to successful product management. AI, particularly through NLP, can enhance communication channels within a product team, and even between the team and its stakeholders. Things like AI-driven chatbots and automated customer service tools create round-the-clock support and feedback collection, which is highly important during the user testing and market feedback stages. AI can also make better documentation internally and more efficient meeting summaries, making sure that no critical info gets lost in the Zoom meeting. Use Cases in Product Management Practice With AI you can get powerful solutions to common challenges. That is how it’s reshaping the industry. Sometimes, understanding is easier with examples, because seeing how exactly a certain tool can be applied will give you inspiration on how to use it in your work. I suggest we have a look at a few use cases, each showing AI’s capabilities in line with the technologies I listed in Section 1. Use Case 1: Quantitative Analytics Technology Focus: Machine Learning, AI AI's ability to sift through massive datasets and perform complex calculations is a godsend in all quantitative analytics processes. With it you can formulate performance metrics, predict trends, and make data-driven decisions efficiently. For example, AI tools can analyze user interaction data to identify features that are most engaging or areas where users face difficulties, giving you the opportunity to quicker direct your improvement efforts. AI is already widely implemented in a few tools that are staples in our field. Google Analytics uses AI for advanced user behavior tracking and conversion analytics, while Mixpanel uses it for detailed interaction analysis and user segmentation. Adobe Analytics uses AI to provide predictive insights and map out customer journeys comprehensively. It all sounds great, but it’s important to keep in mind that the effectiveness of tools depends mainly on the quality and completeness of your input data. Accurate and thorough data is crucial: AI systems base their analysis and predictions on the information they receive. Misleading data can lead to inaccurate outputs, which could potentially and unexpectedly guide product decisions in the wrong direction. We don’t want that. And while AI can provide great insights, they should not be used as standalone reasoning. Apply a critical eye to the results and consider them in the context of broader market conditions and your specific business objectives. Human judgment + AI = balanced decisions. Use Case 2: Qualitative Data Analysis Technology Focus: Natural Language Processing (NLP) Qualitative data is where NLP can shine, since it can extract key themes, sentiments, and pain points from qualitative data and give you a better understanding of the customer experience. It’s great for interpreting and analyzing human language in customer feedback, reviews, and support tickets. And it will also help you structure after. All of this will help you prioritize product adjustments more effectively and respond to customer needs efficiently. Here's a case for you from real product management practice: In an enterprise-level product, the product team was facing huge amounts of user feedback across multiple product lines. An avalanche even. An avalanche of unstructured data that was growing every day. So an NLP API helped categorize the feedback into relevant themes like usability issues, feature requests, or performance bugs. This streamlined the review process and enabled quick action on pressing issues. The tool also used Sentiment Analysis to identify whether comments were positive, negative, or neutral. This was much needed for assessing user satisfaction and the urgency of the feedback. And at the end, by structuring the data, NLP helped to optimize processes and detect trends for making strategic decisions for future product updates and enhancements. This AI application proved to be most impactful during the post-launch phase, where immediate user feedback was essential for quick changes. But the benefits extended throughout the product life cycle, from initial user testing in the development phase to ongoing enhancements in the maturity phase. The example I just showed you focuses on internal data; however, there are many other sources available for necessary analysis, from social media to search data. Use Case 3: Streamlining Routine Product Management Tasks Technology Focus: AI, Machine Learning In product management, the small yet consistent improvements made by AI in those routine tasks may not cause a revolution, but are the building blocks of your future operational efficiency. For example, Adobe Workfront, with its growing AI capabilities, can optimize resource allocation and task prioritization to the max. It uses machine learning to automate these two processes, assigning resources based on availability and skills, and prioritizing tasks based on their strategic importance and deadlines. Additionally, Workfront's AI features can predict potential roadblocks and suggest optimal project timelines. This could prevent delays and ensure smoother product development cycles, which is particularly useful in maintaining momentum during product launches by highlighting critical path activities that require immediate attention or are at risk of causing cascading delays. These improvements significantly reduce the administrative burden on product managers, allowing you to focus more on strategic product decisions and less on operational details. With a clearer focus on delivering value and driving product innovation, you can more effectively steer your product towards market success. Use Case 4: Generative AI for Improved Product Experience Dynamic content and intuitive interfaces are best explored in the hands of generative AI. Figma is a great example. It uses AI plugins to automate design tasks and analyze user interactions and these plugins can generate design elements that adapt to user feedback or create heatmaps of page interactions, giving you insights into how users engage with your product. Generative AI can also be great at automating the development of interactive user guides and tutorials. Tools like WalkMe use AI to design customized guidance systems embedded directly into applications. This way, the user onboarding process is streamline and interactive help from AI keeps users more engaged. In the phases when you need enhancement in design and UX, Generative AI automates the creation of personalized and adaptive interfaces and helps your team deliver solutions that are aesthetically pleasing but also functional. It saves time and makes sure that products remain user-centric, enhancing overall satisfaction and driving product success. Conclusion Understanding the general use cases of AI in product management offers a framework for addressing various challenges in your everyday professional life. Recognizing how AI can streamline operations, enhance decision-making, and improve customer interactions is not just about expanding the scope of your capabilities as a product manager — it’s also crucial for strategic planning and innovation. However, while AI can provide powerful tools and insights, it's important to maintain a balanced perspective. Over-reliance on AI can lead to potential pitfalls. It’s essential to remember that AI systems are tools that require oversight and human judgment to be truly effective. They should complement, not replace, the nuanced decision-making processes that product managers are known for. By integrating AI thoughtfully into your workflow, you can leverage these technologies to their fullest potential without losing sight of the human elements that define successful products. As AI continues to evolve and integrate into more aspects of product management, staying informed and adaptable will be key. By understanding the boundaries and capabilities of current AI technologies, you can better navigate the complexities of modern product management, ensuring that you use AI as a robust ally in your strategic arsenal. At this point, it might be a little hard to imagine an industry that has not yet been affected by AI at least to some degree. So when it comes to tech, talking about the impact artificial intelligence has had on a certain direction, you need expertise to fully understand the changes it is causing and how far it has come. I can tell you that AI is still far from replacing us, but it can already help you do crucial things in regards to product management. Honestly, I find my hand reaching for GPT quite a lot, in a way similar to how developers utilize things like Copilot — more as a partner than a standalone solution. But in this article, I want to talk more about the broader influence of AI in product management tools, more than I’d like to go into chatbot-specific applications. That is for another article. Why? Because for those leading the industry, understanding the reality is very important: AI is already embedded in almost every tool we use at work and will continue to weave itself into every aspect of our industry. You need to understand the scope and capabilities of AI to recognize the boundaries and possibilities it has. Now, many processes are very democratized and accessible which has transformed how products are managed and developed. Classification and Examples of AI Technologies in Product Management Classification and Examples of AI Technologies in Product Management Introduction to AI Technologies Introduction to AI Technologies For product managers, navigating the AI ecosystem can change a lot. The way we design, improve, and manage our products is dependent on our brain, and adding a tool that can diversify the process can bring strikingly different results. AI is not just a single technology but a broad spectrum of tools and methods. With it, you can turn boring tasks into opportunities for growth, all you need is to actually know what is available on the market. If you understand the tech that is available to you, this knowledge can empower you to make informed decisions and leverage the right tools for your projects. And you should know, it’s not about asking ChatGPT to create a roadmap or to act like your target audience and answer your questions. What is it about then, you might ask? Let’s first recall the general concepts of AI: Kinds of AI Tech Kinds of AI Tech 1. Artificial Intelligence (AI) are machines designed to mimic human intelligence and perform tasks such as recognizing patterns, making decisions, and solving problems. For product management, AI can do things like automating routine data analysis, enhancing decision-making processes, and streamlining user interactions. 1. Machine Learning (ML) is a subset of AI that gives systems the ability to automatically learn and improve from experience without actually being programmed to do that. So, in product management, ML can be very useful for predictive analytics, helping anticipate market trends or customer behavior based on historical data. Deep Learning (DL) is an even more complex subset of ML, one that has and uses advanced computational power to process data through layers of neural networks. You can find this useful for image and speech recognition features, making new better ways for how users interact with our products. Example Tool: TensorFlow – an open-source platform that lets you develop DL models. It's key for building in things like image recognition systems that can automatically tag photos in a social media app or analyze user videos. Machine Learning (ML) is a subset of AI that gives systems the ability to automatically learn and improve from experience without actually being programmed to do that. So, in product management, ML can be very useful for predictive analytics, helping anticipate market trends or customer behavior based on historical data. Machine Learning (ML) is a subset of AI that gives systems the ability to automatically learn and improve from experience without actually being programmed to do that. So, in product management, ML can be very useful for predictive analytics, helping anticipate market trends or customer behavior based on historical data. Machine Learning (ML) Deep Learning (DL) is an even more complex subset of ML, one that has and uses advanced computational power to process data through layers of neural networks. You can find this useful for image and speech recognition features, making new better ways for how users interact with our products. Example Tool: TensorFlow – an open-source platform that lets you develop DL models. It's key for building in things like image recognition systems that can automatically tag photos in a social media app or analyze user videos. Deep Learning (DL) is an even more complex subset of ML, one that has and uses advanced computational power to process data through layers of neural networks. You can find this useful for image and speech recognition features, making new better ways for how users interact with our products. Deep Learning (DL) Example Tool: TensorFlow – an open-source platform that lets you develop DL models. It's key for building in things like image recognition systems that can automatically tag photos in a social media app or analyze user videos. Example Tool: Natural Language Processing (NLP) is crucial AI tech for products that deal a lot with human language. Why? It enables computers to understand, interpret, and generate human text in a useful and meaningful way. For products that rely on customer feedback, support systems, and content generation, this can be key. Example Tool: OpenAI GPT – hopefully, does not need an introduction, but it can generate contextually relevant text based on the input it receives. Can be used in chatbots and customer service tools to provide responses that are accurate and make sense. Natural Language Processing (NLP) is crucial AI tech for products that deal a lot with human language. Why? It enables computers to understand, interpret, and generate human text in a useful and meaningful way. For products that rely on customer feedback, support systems, and content generation, this can be key. Example Tool: OpenAI GPT – hopefully, does not need an introduction, but it can generate contextually relevant text based on the input it receives. Can be used in chatbots and customer service tools to provide responses that are accurate and make sense. Natural Language Processing (NLP) is crucial AI tech for products that deal a lot with human language. Why? It enables computers to understand, interpret, and generate human text in a useful and meaningful way. For products that rely on customer feedback, support systems, and content generation, this can be key. Natural Language Processing (NLP) Example Tool: OpenAI GPT – hopefully, does not need an introduction, but it can generate contextually relevant text based on the input it receives. Can be used in chatbots and customer service tools to provide responses that are accurate and make sense. Example Tool: OpenAI GPT – Generative AI – AI methods that can generate new content, from text to images and music, automating creative processes and generating relevant user content. Example Tool: Adobe Firefly is famous for making images from text descriptions. You can use Firefly to generate unique user interface elements or marketing materials, which readily streamlines the design process. And creativity! Generative AI – AI methods that can generate new content, from text to images and music, automating creative processes and generating relevant user content. Example Tool: Adobe Firefly is famous for making images from text descriptions. You can use Firefly to generate unique user interface elements or marketing materials, which readily streamlines the design process. And creativity! Generative AI – AI methods that can generate new content, from text to images and music, automating creative processes and generating relevant user content. Generative AI – Example Tool: Adobe Firefly is famous for making images from text descriptions. You can use Firefly to generate unique user interface elements or marketing materials, which readily streamlines the design process. And creativity! Example Tool: Main Fields of AI Implementation in Product Management Main Fields of AI Implementation in Product Management It is a given that to really harness the full available potential of AI you need to understand what’s going on, and where and how you can integrate it into your management routine. To start off, here are three areas that can really use AI’s power: 1. Data Analysis 1. Data Analysis Focus: Operating efficiencies and pattern recognition. Focus: Data is essential for product management, and it so happens that AI is great at sorting through large datasets to spot trends, anomalies, and patterns that might not be immediately clear. This is especially helpful during initial market research, where you really need to understand customer behaviors, market conditions, and potential opportunities to get better and bigger. Even after you launch your product, AI can continue to analyze, let’s say, user interactions with the product, which will be very helpful in future updates and improvements. 2. Experimentation 2. Experimentation Focus: Scaling innovation excellence. Focus: To innovate you need to take risks and experiment. And that includes testing. AI can automate and optimize testing (such as A/B) which makes these experiments more efficient and statistically sound for you. And when you plan and develop you can use AI to simulate user reactions to different features or changes, so you can refine your approaches before going to market. With AI here you get less risk of failure and faster testing/innovation cycles. Communication Communication Communication Focus: Elevated and effective interactions. Focus: Elevated and effective interactions. Effective communication is the foundation to successful product management. AI, particularly through NLP, can enhance communication channels within a product team, and even between the team and its stakeholders. Things like AI-driven chatbots and automated customer service tools create round-the-clock support and feedback collection, which is highly important during the user testing and market feedback stages. AI can also make better documentation internally and more efficient meeting summaries, making sure that no critical info gets lost in the Zoom meeting. Use Cases in Product Management Practice Use Cases in Product Management Practice With AI you can get powerful solutions to common challenges. That is how it’s reshaping the industry. Sometimes, understanding is easier with examples, because seeing how exactly a certain tool can be applied will give you inspiration on how to use it in your work. I suggest we have a look at a few use cases, each showing AI’s capabilities in line with the technologies I listed in Section 1. Use Case 1: Quantitative Analytics Use Case 1: Quantitative Analytics Technology Focus: Machine Learning, AI Technology Focus: AI's ability to sift through massive datasets and perform complex calculations is a godsend in all quantitative analytics processes. With it you can formulate performance metrics, predict trends, and make data-driven decisions efficiently. For example, AI tools can analyze user interaction data to identify features that are most engaging or areas where users face difficulties, giving you the opportunity to quicker direct your improvement efforts. AI is already widely implemented in a few tools that are staples in our field. Google Analytics uses AI for advanced user behavior tracking and conversion analytics, while Mixpanel uses it for detailed interaction analysis and user segmentation. Adobe Analytics uses AI to provide predictive insights and map out customer journeys comprehensively. It all sounds great, but it’s important to keep in mind that the effectiveness of tools depends mainly on the quality and completeness of your input data. Accurate and thorough data is crucial: AI systems base their analysis and predictions on the information they receive. Misleading data can lead to inaccurate outputs, which could potentially and unexpectedly guide product decisions in the wrong direction. We don’t want that. Google Analytics Google Analytics Mixpanel Mixpanel Adobe Analytics Adobe Analytics And while AI can provide great insights, they should not be used as standalone reasoning. Apply a critical eye to the results and consider them in the context of broader market conditions and your specific business objectives. Human judgment + AI = balanced decisions. Use Case 2: Qualitative Data Analysis Use Case 2: Qualitative Data Analysis Technology Focus: Natural Language Processing (NLP) Technology Focus: Qualitative data is where NLP can shine, since it can extract key themes, sentiments, and pain points from qualitative data and give you a better understanding of the customer experience. It’s great for interpreting and analyzing human language in customer feedback, reviews, and support tickets. And it will also help you structure after. All of this will help you prioritize product adjustments more effectively and respond to customer needs efficiently. Here's a case for you from real product management practice: In an enterprise-level product, the product team was facing huge amounts of user feedback across multiple product lines. An avalanche even. An avalanche of unstructured data that was growing every day. So an NLP API helped categorize the feedback into relevant themes like usability issues, feature requests, or performance bugs. This streamlined the review process and enabled quick action on pressing issues. The tool also used Sentiment Analysis to identify whether comments were positive, negative, or neutral. This was much needed for assessing user satisfaction and the urgency of the feedback. And at the end, by structuring the data, NLP helped to optimize processes and detect trends for making strategic decisions for future product updates and enhancements. Here's a case for you from real product management practice: In an enterprise-level product, the product team was facing huge amounts of user feedback across multiple product lines. An avalanche even. An avalanche of unstructured data that was growing every day. So an NLP API helped categorize the feedback into relevant themes like usability issues, feature requests, or performance bugs. This streamlined the review process and enabled quick action on pressing issues. The tool also used Sentiment Analysis to identify whether comments were positive, negative, or neutral. This was much needed for assessing user satisfaction and the urgency of the feedback. And at the end, by structuring the data, NLP helped to optimize processes and detect trends for making strategic decisions for future product updates and enhancements. Here's a case for you from real product management practice: This AI application proved to be most impactful during the post-launch phase, where immediate user feedback was essential for quick changes. But the benefits extended throughout the product life cycle, from initial user testing in the development phase to ongoing enhancements in the maturity phase. The example I just showed you focuses on internal data; however, there are many other sources available for necessary analysis, from social media to search data. Use Case 3: Streamlining Routine Product Management Tasks Use Case 3: Streamlining Routine Product Management Tasks Technology Focus: AI, Machine Learning Technology Focus: In product management, the small yet consistent improvements made by AI in those routine tasks may not cause a revolution, but are the building blocks of your future operational efficiency. For example, Adobe Workfront , with its growing AI capabilities, can optimize resource allocation and task prioritization to the max. It uses machine learning to automate these two processes, assigning resources based on availability and skills, and prioritizing tasks based on their strategic importance and deadlines. Adobe Workfront Adobe Workfront Additionally, Workfront's AI features can predict potential roadblocks and suggest optimal project timelines. This could prevent delays and ensure smoother product development cycles, which is particularly useful in maintaining momentum during product launches by highlighting critical path activities that require immediate attention or are at risk of causing cascading delays. These improvements significantly reduce the administrative burden on product managers, allowing you to focus more on strategic product decisions and less on operational details. With a clearer focus on delivering value and driving product innovation, you can more effectively steer your product towards market success. Use Case 4: Generative AI for Improved Product Experience Use Case 4: Generative AI for Improved Product Experience Dynamic content and intuitive interfaces are best explored in the hands of generative AI. Figma is a great example. It uses AI plugins to automate design tasks and analyze user interactions and these plugins can generate design elements that adapt to user feedback or create heatmaps of page interactions, giving you insights into how users engage with your product. Generative AI can also be great at automating the development of interactive user guides and tutorials. Tools like WalkMe use AI to design customized guidance systems embedded directly into applications. This way, the user onboarding process is streamline and interactive help from AI keeps users more engaged. WalkMe WalkMe In the phases when you need enhancement in design and UX, Generative AI automates the creation of personalized and adaptive interfaces and helps your team deliver solutions that are aesthetically pleasing but also functional. It saves time and makes sure that products remain user-centric, enhancing overall satisfaction and driving product success. Conclusion Conclusion Understanding the general use cases of AI in product management offers a framework for addressing various challenges in your everyday professional life. Recognizing how AI can streamline operations, enhance decision-making, and improve customer interactions is not just about expanding the scope of your capabilities as a product manager — it’s also crucial for strategic planning and innovation. However, while AI can provide powerful tools and insights, it's important to maintain a balanced perspective. Over-reliance on AI can lead to potential pitfalls. It’s essential to remember that AI systems are tools that require oversight and human judgment to be truly effective. They should complement, not replace, the nuanced decision-making processes that product managers are known for. By integrating AI thoughtfully into your workflow, you can leverage these technologies to their fullest potential without losing sight of the human elements that define successful products. As AI continues to evolve and integrate into more aspects of product management, staying informed and adaptable will be key. By understanding the boundaries and capabilities of current AI technologies, you can better navigate the complexities of modern product management, ensuring that you use AI as a robust ally in your strategic arsenal.