Will AI Reshape Product Management?

Aveneel Waadhwa, Product Manager at Microsoft working on the Azure Optimization team, shares his thoughts on product management in the age of AI. Imagine a world where product managers possess the superpower to predict customer needs with pinpoint accuracy, streamline workflows effortlessly, and make data-driven decisions in real-time— all through the transformative power of artificial intelligence. In this article, I dive into how AI revolutionizes product management, enhances efficiency, and creates new opportunities while exploring the ethical responsibilities accompanying these advancements. Can AI Replace Product Managers? Let’s address the elephant in the room: Can AI replace product managers (PMs)? For now, the answer is no. With their unique blend of hard and soft skills, product managers bring an irreplaceable human touch to technology. They wield the power to make decisions that could enhance or disrupt systems, a responsibility AI cannot replicate. This reassurance should instill confidence in product managers about the value of their skills in the age of AI. While AI can aid in various aspects of product management, such as data analysis and automation, it falls short in areas requiring human creativity, emotional intelligence, and “soft skills”— indispensable for effective product management. For example, while tools like Gamma AI for presentations or Otter.ai for meeting notes streamline our tasks, product managers uniquely provide the nuanced understanding of customer needs and the empathy necessary to build strong teams. That said, I’d argue that this new wave of AI will make product managers more critical than ever. PMs are accountable for making responsible decisions across their product portfolios while also holding the power to shape the future of AI products and, in turn, the future of our world. With great power comes great responsibility, which should make product managers feel critical, influential, and responsible for the ethical use of AI in their work. Leveraging AI at Work In my role at Microsoft, I regularly utilize AI to enhance decision-making and streamline processes. For instance, Azure Machine Learning analyzes vast volumes of data to uncover patterns and insights that inform our product strategies. Recently, my team launched a Copilot plugin for internal customers, reducing access time to view and edit Azure budgets, forecasts, and projections by approximately 50 percent. This plugin also makes it easier to surface cost-saving recommendations to customers and help answer their FAQs and product queries. During a project to optimize our organization’s Azure services, I had one of the most impactful experiences with AI. By integrating Azure Machine Learning, we identified subtle customer preferences that we would have otherwise missed. This led to a more personalized product experience and higher customer satisfaction. Using Power BI for real-time data visualization allows us to make informed decisions swiftly, adapting our strategies to market changes almost instantaneously. Copilot has saved me countless hours drafting initial document versions, allowing me to focus on strategic planning and team coordination. Here are some AI tools that I frequently use at work: Azure Machine Learning: Data analysis and trend identification. Copilot in Microsoft 365: Assisting in drafting documents like Product Requirement Documents (PRDs). Power BI: Creating interactive dashboards for real-time insights. Dynamics 365 AI: Understanding customer feedback and predicting market trends. Copilot plugins: Managing internal tasks and tooling, including dogfooding the plugin I launched. Integrating Microsoft’s AI tools into my workflow enhances productivity and frees up time for innovation and creative problem-solving, resulting in improved product outcomes. Equipping PMs for the AI Revolution Proficiency in AI fundamentals and key machine learning concepts is now more crucial than ever. According to the World Economic Forum, 23 percent of global jobs will change in the next five years, driven by advancements in artificial intelligence and other text, image, and voice processing technologies. Reflecting on my decision to major in Data Science, I value the hands-on experience gained from running machine learning models and exploring AI libraries and frameworks. This knowledge has been invaluable in understanding the capabilities and limitations of AI tools. Future computer science and data science curricula will likely incorporate responsible AI requirements. Programs like Stanford’s Symbolic Systems or my alma mater, UC Berkeley’s Cognitive Science, which blends technical skills with humanities, will become more relevant. These programs prepare PMs to navigate the ethical considerations of AI, ensuring privacy, data protection, and bias mitigation are prioritized in product development. Prioritization is a significant responsibility for any product manager, especially those involved in AI products. Beyond speed, considerations of privacy, data protection, ethics, and biases must inform the crafting of product roadmaps. While this shift in mindset may take time for some PMs, it’s a necessary change. The responsible use of AI is not a passing trend; it’s a prerequisite for the future of product management. Emphasizing responsibility should empower product managers and underscore their accountability for the ethical use of AI in their work. Ethical Use of AI The ethical use of AI is paramount in today’s technological landscape. Product managers must ensure that AI is used responsibly, minimizing risks and maximizing benefits for society. Here are some concrete examples of how PMs can use AI ethically in their work: Transparency and Explainability: Ensure that AI models are transparent and their decisions can be explained. Using AI tools like Explainable AI (XAI) can help product managers understand and communicate how AI makes decisions, which is crucial for maintaining user trust. Lack of transparency can lead to mistrust among users and stakeholders, potentially resulting in the rejection of AI solutions. For example, a lack of transparency in AI algorithms used in hiring practices can lead to legal challenges and reputational damage for companies. Bias Mitigation: Actively work to identify and mitigate biases in AI models. This includes using diverse datasets and regularly auditing AI systems for discriminatory patterns. Tools like IBM’s AI Fairness 360 can help in this process. Failure to address biases can result in discriminatory practices, leading to unfair treatment of certain groups. This can cause significant harm to affected individuals and expose the company to legal and regulatory repercussions. For instance, biased AI in credit scoring systems has led to unfair loan denials and increased regulatory scrutiny. Data Privacy: Prioritize data privacy and protection. Ensure that AI systems comply with regulations like GDPR and CCPA and implement robust data anonymization techniques to protect user information. Neglecting data privacy can lead to severe data breaches, resulting in substantial financial penalties and loss of customer trust. For example, the GDPR has imposed hefty fines on companies for data privacy violations, highlighting the importance of compliance. Ethical AI Usage Policies: Develop and enforce ethical AI usage policies within the organization. This includes setting clear guidelines on the acceptable use of AI and training employees on ethical AI practices. Without clear policies, companies risk unethical use of AI that can lead to public backlash and damage the company’s reputation. Ethical lapses in AI deployment can also result in long-term negative impacts on brand perception and customer loyalty. Stakeholder Involvement: Involve diverse stakeholders in developing and deploying AI systems. This ensures that different perspectives are considered and the AI system aligns with the values and needs of various user groups. Ignoring stakeholder input can result in AI solutions that do not meet user needs or ethical standards, leading to poor adoption and potential failures. Stakeholder engagement is crucial for developing effective and ethically sound AI. The Future of AI in Product Management As AI evolves, its impact on product management and the broader technology landscape will be profound. Increased regulation of AI technologies is expected to ensure ethical and responsible use. For instance, the European Union’s AI Act aims to establish a legal framework for AI, focusing on transparency, accountability, and ethical use. This act will categorize AI systems based on risk levels, ensuring higher-risk applications undergo rigorous scrutiny. Similarly, the United States is considering the Algorithmic Accountability Act, which would require companies to assess the impact of their AI systems and mitigate any potential biases or harms. Detecting deepfakes and AI-generated content is also a challenge, with companies like Reality Defender and GPTZero working with governments and universities to solve it. Product managers must stay well-versed in these regulations to ensure compliance and ethical product development. This requires continuously updating their knowledge to align with new legal requirements and societal expectations. As AI tools become more sophisticated, PMs will play a crucial role in interpreting AI-generated insights and ensuring these technologies benefit users and society. While AI tools might suggest a reduced demand for product managers due to increased efficiency, the reality is more nuanced. AI will streamline many tasks, but the role of PMs will also expand to include new responsibilities, such as ensuring AI alignment with regulatory requirements and interpreting AI-generated insights. This will likely increase the need for skilled PMs who can navigate this complex landscape. AI Tools for Product Managers Here are some AI tools that product managers can use to streamline their daily tasks: Gamma AI: For AI-powered presentations. Copilot on Word or Notion AI: For drafting documents. ChatPRD or WriteMyPRD: For crafting 1-pagers and PRDs ClickUp: For automating workflows. Mixpanel: For AI-driven product analytics. Productroadmap.ai: For creating product roadmaps efficiently. Copilot on Teams or Otter.ai: For effortlessly taking meeting notes. Enterpret: For analyzing customer feedback across channels. These tools significantly save time and offer a glimpse into an exciting future where PMs can focus more on strategic decision-making and less on mundane tasks. AI and Techno-Optimism Techno-optimism is the belief that technology, especially new technologies such as AI, will ultimately improve our lives and solve many of humanity’s problems. I am optimistic about AI’s potential to balance technical and soft skills in the tech industry. Developing empathy and collaboration skills will be crucial in the age of AI. Tech companies, often criticized for lacking humanity, can benefit from this shift as AI encourages more human-centric decision-making. A great example of this is Blank Street Coffee. The company uses automation to handle routine tasks, freeing up their service workers to focus on engaging with customers and improving their experience. Instead of just making coffee, employees can spend more time building relationships with customers, enhancing overall satisfaction. This model shows how AI can foster better human interactions by allowing workers to focus on what they do best—providing excellent customer service. As we stand on the brink of the AI revolution, it’s clear that product managers are uniquely positioned to harness this technology for profound impact. AI will transform product management, technology, and the world. By embracing AI responsibly and leading with empathy and innovation, product managers can shape a future where technology serves humanity in meaningful and ethical ways. Let’s champion solutions to significant problems and ensure that as AI grows more intelligent, it enhances our human experience and drives us toward a better and brighter future. Aveneel Waadhwa is a Product Manager at Microsoft, based in New York City, working on the Azure Optimization team. He is also the co-founder of Aspiring Product Manager, a non-profit organization dedicated to helping aspiring product managers break into the tech industry through mentorship, guidance, and job application feedback. With a background in Data Science from UC Berkeley, Aveneel is highly knowledgeable about AI and its applications in product management. Outside work, Aveneel enjoys traveling to new countries, hiking, hosting coffee events, playing soccer, and watching movies. Aveneel Waadhwa, Product Manager at Microsoft working on the Azure Optimization team, shares his thoughts on product management in the age of AI. Aveneel Waadhwa, Product Manager at Microsoft working on the Azure Optimization team, shares his thoughts on product management in the age of AI. Aveneel Waadhwa, Product Manager at Microsoft working on the Azure Optimization team, shares his thoughts on product management in the age of AI. Imagine a world where product managers possess the superpower to predict customer needs with pinpoint accuracy, streamline workflows effortlessly, and make data-driven decisions in real-time— all through the transformative power of artificial intelligence. In this article, I dive into how AI revolutionizes product management, enhances efficiency, and creates new opportunities while exploring the ethical responsibilities accompanying these advancements. Can AI Replace Product Managers? Let’s address the elephant in the room: Can AI replace product managers (PMs)? For now, the answer is no. With their unique blend of hard and soft skills, product managers bring an irreplaceable human touch to technology. They wield the power to make decisions that could enhance or disrupt systems, a responsibility AI cannot replicate. This reassurance should instill confidence in product managers about the value of their skills in the age of AI. While AI can aid in various aspects of product management, such as data analysis and automation, it falls short in areas requiring human creativity, emotional intelligence, and “soft skills”— indispensable for effective product management. For example, while tools like Gamma AI for presentations or Otter.ai for meeting notes streamline our tasks, product managers uniquely provide the nuanced understanding of customer needs and the empathy necessary to build strong teams. That said, I’d argue that this new wave of AI will make product managers more critical than ever . PMs are accountable for making responsible decisions across their product portfolios while also holding the power to shape the future of AI products and, in turn, the future of our world. With great power comes great responsibility, which should make product managers feel critical, influential, and responsible for the ethical use of AI in their work. this new wave of AI will make product managers more critical than ever Leveraging AI at Work In my role at Microsoft, I regularly utilize AI to enhance decision-making and streamline processes. For instance, Azure Machine Learning analyzes vast volumes of data to uncover patterns and insights that inform our product strategies. Recently, my team launched a Copilot plugin for internal customers, reducing access time to view and edit Azure budgets, forecasts, and projections by approximately 50 percent. This plugin also makes it easier to surface cost-saving recommendations to customers and help answer their FAQs and product queries. During a project to optimize our organization’s Azure services, I had one of the most impactful experiences with AI. By integrating Azure Machine Learning, we identified subtle customer preferences that we would have otherwise missed. This led to a more personalized product experience and higher customer satisfaction. Using Power BI for real-time data visualization allows us to make informed decisions swiftly, adapting our strategies to market changes almost instantaneously. Copilot has saved me countless hours drafting initial document versions, allowing me to focus on strategic planning and team coordination. Here are some AI tools that I frequently use at work: Azure Machine Learning: Data analysis and trend identification. Copilot in Microsoft 365: Assisting in drafting documents like Product Requirement Documents (PRDs). Power BI: Creating interactive dashboards for real-time insights. Dynamics 365 AI: Understanding customer feedback and predicting market trends. Copilot plugins: Managing internal tasks and tooling, including dogfooding the plugin I launched. Azure Machine Learning: Data analysis and trend identification. Azure Machine Learning: Copilot in Microsoft 365 : Assisting in drafting documents like Product Requirement Documents (PRDs). Copilot in Microsoft 365 Power BI : Creating interactive dashboards for real-time insights. Power BI Dynamics 365 AI : Understanding customer feedback and predicting market trends. Dynamics 365 AI Copilot plugins: Managing internal tasks and tooling, including dogfooding the plugin I launched. Copilot plugins: Integrating Microsoft’s AI tools into my workflow enhances productivity and frees up time for innovation and creative problem-solving, resulting in improved product outcomes. Equipping PMs for the AI Revolution Proficiency in AI fundamentals and key machine learning concepts is now more crucial than ever. According to the World Economic Forum, 23 percent of global jobs will change in the next five years, driven by advancements in artificial intelligence and other text, image, and voice processing technologies. World Economic Forum, Reflecting on my decision to major in Data Science, I value the hands-on experience gained from running machine learning models and exploring AI libraries and frameworks. This knowledge has been invaluable in understanding the capabilities and limitations of AI tools. Future computer science and data science curricula will likely incorporate responsible AI requirements. Programs like Stanford’s Symbolic Systems or my alma mater, UC Berkeley’s Cognitive Science , which blends technical skills with humanities, will become more relevant. These programs prepare PMs to navigate the ethical considerations of AI, ensuring privacy, data protection, and bias mitigation are prioritized in product development. Stanford’s Symbolic Systems UC Berkeley’s Cognitive Science Prioritization is a significant responsibility for any product manager, especially those involved in AI products. Beyond speed, considerations of privacy, data protection, ethics, and biases must inform the crafting of product roadmaps. While this shift in mindset may take time for some PMs, it’s a necessary change. The responsible use of AI is not a passing trend; it’s a prerequisite for the future of product management. Emphasizing responsibility should empower product managers and underscore their accountability for the ethical use of AI in their work. The responsible use of AI is not a passing trend; it’s a prerequisite for the future of product management. Ethical Use of AI The ethical use of AI is paramount in today’s technological landscape. Product managers must ensure that AI is used responsibly, minimizing risks and maximizing benefits for society. Here are some concrete examples of how PMs can use AI ethically in their work: Transparency and Explainability: Ensure that AI models are transparent and their decisions can be explained. Using AI tools like Explainable AI (XAI) can help product managers understand and communicate how AI makes decisions, which is crucial for maintaining user trust. Lack of transparency can lead to mistrust among users and stakeholders, potentially resulting in the rejection of AI solutions. For example, a lack of transparency in AI algorithms used in hiring practices can lead to legal challenges and reputational damage for companies. Bias Mitigation: Actively work to identify and mitigate biases in AI models. This includes using diverse datasets and regularly auditing AI systems for discriminatory patterns. Tools like IBM’s AI Fairness 360 can help in this process. Failure to address biases can result in discriminatory practices, leading to unfair treatment of certain groups. This can cause significant harm to affected individuals and expose the company to legal and regulatory repercussions. For instance, biased AI in credit scoring systems has led to unfair loan denials and increased regulatory scrutiny. Data Privacy: Prioritize data privacy and protection. Ensure that AI systems comply with regulations like GDPR and CCPA and implement robust data anonymization techniques to protect user information. Neglecting data privacy can lead to severe data breaches, resulting in substantial financial penalties and loss of customer trust. For example, the GDPR has imposed hefty fines on companies for data privacy violations, highlighting the importance of compliance. Ethical AI Usage Policies: Develop and enforce ethical AI usage policies within the organization. This includes setting clear guidelines on the acceptable use of AI and training employees on ethical AI practices. Without clear policies, companies risk unethical use of AI that can lead to public backlash and damage the company’s reputation. Ethical lapses in AI deployment can also result in long-term negative impacts on brand perception and customer loyalty. Stakeholder Involvement: Involve diverse stakeholders in developing and deploying AI systems. This ensures that different perspectives are considered and the AI system aligns with the values and needs of various user groups. Ignoring stakeholder input can result in AI solutions that do not meet user needs or ethical standards, leading to poor adoption and potential failures. Stakeholder engagement is crucial for developing effective and ethically sound AI. Transparency and Explainability: Ensure that AI models are transparent and their decisions can be explained. Using AI tools like Explainable AI (XAI) can help product managers understand and communicate how AI makes decisions, which is crucial for maintaining user trust. Lack of transparency can lead to mistrust among users and stakeholders, potentially resulting in the rejection of AI solutions. For example, a lack of transparency in AI algorithms used in hiring practices can lead to legal challenges and reputational damage for companies. Transparency and Explainability: Explainable AI (XAI) Bias Mitigation: Actively work to identify and mitigate biases in AI models. This includes using diverse datasets and regularly auditing AI systems for discriminatory patterns. Tools like IBM’s AI Fairness 360 can help in this process. Failure to address biases can result in discriminatory practices, leading to unfair treatment of certain groups. This can cause significant harm to affected individuals and expose the company to legal and regulatory repercussions. For instance, biased AI in credit scoring systems has led to unfair loan denials and increased regulatory scrutiny. Bias Mitigation: IBM’s AI Fairness 360 biased AI Data Privacy: Prioritize data privacy and protection. Ensure that AI systems comply with regulations like GDPR and CCPA and implement robust data anonymization techniques to protect user information. Neglecting data privacy can lead to severe data breaches, resulting in substantial financial penalties and loss of customer trust. For example, the GDPR has imposed hefty fines on companies for data privacy violations, highlighting the importance of compliance. Data Privacy: hefty fines on companies Ethical AI Usage Policies: Develop and enforce ethical AI usage policies within the organization. This includes setting clear guidelines on the acceptable use of AI and training employees on ethical AI practices. Without clear policies, companies risk unethical use of AI that can lead to public backlash and damage the company’s reputation. Ethical lapses in AI deployment can also result in long-term negative impacts on brand perception and customer loyalty. Ethical AI Usage Policies: impacts Stakeholder Involvement: Involve diverse stakeholders in developing and deploying AI systems. This ensures that different perspectives are considered and the AI system aligns with the values and needs of various user groups. Ignoring stakeholder input can result in AI solutions that do not meet user needs or ethical standards, leading to poor adoption and potential failures. Stakeholder engagement is crucial for developing effective and ethically sound AI. Stakeholder Involvement: Stakeholder engagement The Future of AI in Product Management As AI evolves, its impact on product management and the broader technology landscape will be profound. Increased regulation of AI technologies is expected to ensure ethical and responsible use. For instance, the European Union’s AI Act aims to establish a legal framework for AI, focusing on transparency, accountability, and ethical use. This act will categorize AI systems based on risk levels, ensuring higher-risk applications undergo rigorous scrutiny. Similarly, the United States is considering the Algorithmic Accountability Act , which would require companies to assess the impact of their AI systems and mitigate any potential biases or harms. Detecting deepfakes and AI-generated content is also a challenge, with companies like Reality Defender and GPTZero working with governments and universities to solve it. European Union’s AI Act Algorithmic Accountability Act Reality Defender GPTZero Product managers must stay well-versed in these regulations to ensure compliance and ethical product development. This requires continuously updating their knowledge to align with new legal requirements and societal expectations. As AI tools become more sophisticated, PMs will play a crucial role in interpreting AI-generated insights and ensuring these technologies benefit users and society. While AI tools might suggest a reduced demand for product managers due to increased efficiency, the reality is more nuanced. AI will streamline many tasks, but the role of PMs will also expand to include new responsibilities, such as ensuring AI alignment with regulatory requirements and interpreting AI-generated insights. This will likely increase the need for skilled PMs who can navigate this complex landscape. AI Tools for Product Managers Here are some AI tools that product managers can use to streamline their daily tasks: Gamma AI: For AI-powered presentations. Copilot on Word or Notion AI: For drafting documents. ChatPRD or WriteMyPRD: For crafting 1-pagers and PRDs ClickUp: For automating workflows. Mixpanel: For AI-driven product analytics. Productroadmap.ai: For creating product roadmaps efficiently. Copilot on Teams or Otter.ai: For effortlessly taking meeting notes. Enterpret: For analyzing customer feedback across channels. Gamma AI : For AI-powered presentations. Gamma AI Copilot on Word or Notion AI : For drafting documents. Copilot on Word Notion AI ChatPRD or WriteMyPRD : For crafting 1-pagers and PRDs ChatPRD WriteMyPRD ClickUp : For automating workflows. ClickUp Mixpanel : For AI-driven product analytics. Mixpanel Productroadmap.ai : For creating product roadmaps efficiently. Productroadmap.ai Copilot on Teams or Otter.ai : For effortlessly taking meeting notes. Copilot on Teams Otter.ai Enterpret : For analyzing customer feedback across channels. Enterpret These tools significantly save time and offer a glimpse into an exciting future where PMs can focus more on strategic decision-making and less on mundane tasks. AI and Techno-Optimism Techno-optimism is the belief that technology, especially new technologies such as AI, will ultimately improve our lives and solve many of humanity’s problems. I am optimistic about AI’s potential to balance technical and soft skills in the tech industry. Developing empathy and collaboration skills will be crucial in the age of AI. Tech companies, often criticized for lacking humanity, can benefit from this shift as AI encourages more human-centric decision-making. A great example of this is Blank Street Coffee . The company uses automation to handle routine tasks, freeing up their service workers to focus on engaging with customers and improving their experience. Instead of just making coffee, employees can spend more time building relationships with customers, enhancing overall satisfaction. This model shows how AI can foster better human interactions by allowing workers to focus on what they do best—providing excellent customer service. Blank Street Coffee As we stand on the brink of the AI revolution, it’s clear that product managers are uniquely positioned to harness this technology for profound impact. AI will transform product management, technology, and the world. By embracing AI responsibly and leading with empathy and innovation, product managers can shape a future where technology serves humanity in meaningful and ethical ways. Let’s champion solutions to significant problems and ensure that as AI grows more intelligent, it enhances our human experience and drives us toward a better and brighter future. Aveneel Waadhwa is a Product Manager at Microsoft, based in New York City, working on the Azure Optimization team. He is also the co-founder of Aspiring Product Manager, a non-profit organization dedicated to helping aspiring product managers break into the tech industry through mentorship, guidance, and job application feedback. With a background in Data Science from UC Berkeley, Aveneel is highly knowledgeable about AI and its applications in product management. Outside work, Aveneel enjoys traveling to new countries, hiking, hosting coffee events, playing soccer, and watching movies. Aveneel Waadhwa Aveneel Waadhwa is a Product Manager at Microsoft, based in New York City, working on the Azure Optimization team. He is also the co-founder of Aspiring Product Manager, a non-profit organization dedicated to helping aspiring product managers break into the tech industry through mentorship, guidance, and job application feedback. With a background in Data Science from UC Berkeley, Aveneel is highly knowledgeable about AI and its applications in product management. Outside work, Aveneel enjoys traveling to new countries, hiking, hosting coffee events, playing soccer, and watching movies.