Product Management in the Age of AI

Join Maria Van Wambecke and industry experts Mark Barbir (Earmark), Kelly Kawa (Invisible Technologies), and Rachel Miller (Reddit) for a practical discussion on integrating AI into product management workflows.

This session cuts through the hype to address real challenges facing product teams today, such as:

• Managing overstretched teams juggling countless priorities,
• Automating repetitive tasks that drain strategic thinking time,
• Implementing AI responsibly without compromising security or ethics.

With 80% of organizations still in early adoption stages—experimenting but not yet scaling—this panel offers concrete strategies for moving forward at your own pace.

What You’ll Learn:

• How to identify and prioritize AI integration opportunities that address your team’s specific pain points
• Practical approaches to automating documentation, meeting summaries, and repetitive workflows without sacrificing quality
• Change management strategies for successful AI tool adoption, including vetting processes and security considerations
• Methods for balancing speed with responsibility when deploying AI features to customers
• The evolving role of product managers as AI shifts boundaries between strategic and operational work

Get ready to learn how to identify internal pain points worth solving, start with small wins that build organizational confidence, and scale toward strategic transformation—all while maintaining human oversight, protecting sensitive data, and upholding ethical responsibility throughout the product development lifecycle.

Key Takeaways

• Start with operator pain points, not technology: The most successful AI implementations focus on day-to-day problems that independent contributors face, rather than top-down mandates. Small, incremental steps—like automating task summaries or using AI for meeting transcriptions—build internal advocacy and hands-on experience before scaling.
• Human oversight remains non-negotiable: AI excels at automating repetitive tasks and serving as a thought partner for brainstorming, but product managers must verify outputs and maintain context. The division is clear: humans own judgment, empathy, and strategic decisions; AI handles automation and pattern recognition.
• Apply standard change management practices: Don’t skip steps in the rush to adopt AI. Vet tools through normal security and procurement processes, establish clear success criteria, and protect sensitive data through proper agreements. Classic change management principles matter more than ever in the AI hype cycle.
• Build ethical considerations into the process from day one: Responsible AI deployment starts at the discovery phase, not after launch. Consider whether your user group is vulnerable, assess the sensitivity of the problem you’re solving, and work closely with engineers to understand model limitations before committing to solutions.
• AI creates capacity for strategic work, not replacement: Product managers spending 60% of their time in meetings can reclaim hours by automating artifacts like user stories, specifications, and status updates. This freed capacity enables deeper customer relationships, more thoughtful planning, and proactive rather than reactive work.

Reference

• Earmark
• Invisible Technologies
• Reddit