Product managers’ changing skills and AI adoption

The Evolving Role of Product Managers in the AI Era: Skills, Trends, and Strategic Shifts

As artificial intelligence continues to reshape the landscape of product development, the role of product managers (PMs) is undergoing significant transformation. Central to this evolution is the debate over whether PMs need to possess coding skills in the current and future technological environment.

Do Product Managers Need to Code in the AI Era?

Traditionally, product managers were often expected to have a technical background, including coding abilities, to facilitate effective communication with engineering teams and to understand technical constraints. However, in the context of AI-driven products, the necessity of coding skills for PMs is a subject of ongoing discussion.

Teresa Torres highlights that while coding skills can enhance a product manager's understanding of technical possibilities, they are not strictly essential for all PMs. Instead, a strong grasp of AI capabilities, data literacy, and the ability to translate business needs into technical requirements are more critical. Torres emphasizes that effective communication, strategic vision, and user-centric design remain core competencies, with technical skills being a valuable but not mandatory supplement.

Practical Trends and Tactics for AI-Driven Product Management in 2026

Looking ahead to 2026, several practical trends are shaping how PMs approach AI integration:

• Data Fluency: PMs are expected to develop a deeper understanding of data science and machine learning concepts to better collaborate with data teams and interpret AI outputs.
• Tool Adoption: The rise of AI-powered tooling—like automated analytics, natural language processing interfaces, and predictive modeling platforms—reduces the need for PMs to code but requires familiarity with these tools’ capabilities and limitations.
• Rapid Prototyping and Experimentation: AI accelerates experimentation cycles, allowing PMs to test and iterate features with minimal coding, often through no-code or low-code platforms.
• Ethical and Responsible AI: PMs must also focus on ethical considerations, bias mitigation, and user trust, integrating these principles into product development processes.

According to the Gleap blog on "AI-Driven Product Management: Navigating 2026 Trends," successful PMs will leverage AI tools to streamline workflows, generate insights, and enhance decision-making, positioning themselves as strategic orchestrators rather than solely technical implementers.

Significance: Role Evolution, Hiring, and Tooling

The convergence of these trends signifies a substantial shift in the PM role:

• Role Evolution: PMs are transitioning from primarily technical or business-focused roles to hybrid positions that require a blend of strategic vision, data literacy, and familiarity with AI capabilities.
• Hiring Considerations: Organizations increasingly value candidates with experience in data analysis, AI literacy, and familiarity with AI tools, sometimes even over traditional coding skills.
• Tooling and Infrastructure: The proliferation of AI-enabled platforms means PMs must be adept at selecting, configuring, and managing these tools to maximize their impact, often with minimal coding.

In summary, while coding skills are no longer a strict prerequisite for product managers working with AI, a strong foundation in AI concepts, data fluency, and proficiency with emerging tools are essential for success in 2026. The role is evolving into a strategic, cross-disciplinary position that balances technical understanding with user-centric innovation, positioning PMs as key drivers in the AI-powered product landscape.