As AI tools become more common in software development, workflows and roles are shifting. AI can automate many time-consuming tasks, prompting the question: has AI transformed the product manager role or made it obsolete?
What is a Software Product Manager?
A software product manager guides a product from idea through launch and beyond. Key responsibilities include:
– Conducting market research and user analysis to understand trends and needs.
– Prioritizing features based on market fit, timeline, and budget.
– Ensuring the product meets client business goals and user requirements.
– Acting as the client’s primary contact.
– Supporting the team when obstacles or decisions arise.
Where Projects Still Need Product Managers
AI speeds up many parts of product development—market research, data collection, pattern detection—but product managers remain essential. Several parts of the role require judgment, context, and human oversight.
Turning Data into Insights
AI can gather and highlight large volumes of data and surface patterns, but it can’t convert those findings into strategic decisions that balance client priorities, technical constraints, and user needs. Humans interpret context, weigh trade-offs, and make the final calls.
Prioritizing Ethics and Morals
AI models carry ethical and legal risks: privacy, security, IP, regulatory concerns, and bias. Product managers must evaluate and mitigate these risks, review AI-generated content and code for harmful or biased outputs, and prevent damage to users, clients, and companies.
Collaborating and Communicating with Teams and Stakeholders
Product managers coordinate cross-functional teams—design, engineering, QA, and clients—and now also collaborate with data scientists, data engineers, and AI specialists. They translate between technical disciplines, align stakeholders, and keep projects focused.
Prioritizing AI’s Benefits Throughout the Product Lifecycle
Knowing that AI can be used isn’t enough. Product managers must understand each tool’s capabilities and limits to choose the right use cases and integrations. They must also know when not to use AI. Misaligned tools or poor integration can introduce bugs, slow delivery, or increase costs instead of reducing them.
Challenges for Product Managers Integrating AI
Lifelong Learning: Product managers now need familiarity with data science concepts and AI capabilities. Continuous upskilling is required to remain effective and competitive.
Transparency: As the intermediary for stakeholders, product managers must justify tool choices and explain behavior. The opacity of many AI models makes clear explanations and accountability harder.
Ongoing Maintenance: AI systems evolve rapidly. Products that embed AI require long-term monitoring, retraining, and maintenance to stay relevant and safe. Often, coordinating that effort falls to the product manager.
Successful Product Managers Make Successful Products
Product managers who understand AI can speed time to market, reduce costs, and create valuable features. They combine tool fluency with ethical oversight, stakeholder alignment, and strategic decision-making—roles AI alone cannot fully assume.
At Grio, our product managers lead the AI transition, applying AI thoughtfully across the development lifecycle to build better apps. To learn how our product managers can help shape your idea into a successful product, contact us for a free consultation.
