Future Forward: AI as Your Insight Co‑Pilot

As AI continues to shape our future as well as the future of product management and product-led organizations, its influence on how we make decisions, collaborate, and build products is undeniable.

In these monthly recurring Future Forward Series events, we’ll discuss the AI trends, role shifts, and process transformations redefining the product management landscape.

We’ll explore how AI is reshaping product discovery, customer insights, and decision-making throughout the product lifecycle. Additionally, we’ll examine the evolving role of product managers as AI becomes a critical part of the toolkit, and how product teams are navigating this new terrain.

• Who should attend: Product managers, aspiring PMs, entrepreneurs, software engineers, builders and anyone interested in understanding how AI is changing the role of product management and innovation.
• Takeaway: Gain practical insights into how AI is transforming our lives, what we can learn from it and apply to our personal and professional lives, and also learn strategies needed to navigate these changes.
• Format: No presentations, no monologues; interactive roundtable discussions, where everyone will share experiences, discuss challenges, and exchange ideas on the future of AI.
• Featured Product: Menlo Research

## FLOW:

## A. Continuous Discovery 2.0: AI as Your Insight Co‑Pilot (60 min interactive session)

1. Welcome & Framing
• Quick round‑robin: “What’s one thing you hope AI can help you discover?”
• What is “Continuous Discovery 2.0.” ?

2. AI Foundations in Discovery
• What unique strengths does AI bring to ongoing discovery?
• How does AI shift our mindset from “annual research” to “always‑on insights”?

3. Building Real‑Time Feedback Loops
• Which customer signals can AI surface automatically?
• How do you balance automated insight with human interpretation?

4. Tools & Techniques
• What AI tools or platforms have you tried for discovery?
• Share a quick tip or hack for integrating AI into interviews, prototypes, or analytics.

5. Overcoming Bias & Ethical Pitfalls
• How might AI introduce bias into your discovery data?
• What guardrails or review processes should you build?

6. Team Roles & Collaboration
• Who “owns” AI‑powered discovery in your team?
• How do you ensure cross‑functional alignment when insights come from machine as well as humans?

7. Action Planning & Wrap‑Up
• Please name one action item you will plan on running based on your learnings within your next sprint.

8. Menlo Research Section :
-Which metrics do you track to measure product–market fit for your AI solution ?
-How do you balance rapid feature delivery with ethical considerations in your AI models ?
-What does your continuous discovery cadence look like; how often do you run user tests or interviews ?
-How do you ensure data quality and mitigate bias when training your AI ?
-Which AI tools or platforms have been most effective for surfacing customer insights in your process ?
-How do you integrate feedback from engineering, design, and sales into your AI product roadmap ?

## B. Networking Until The End Of The Night

This is more than just a learning opportunity; it’s an exciting chance to network, reconnect, share stories, and celebrate our in-person collaboration.

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