520: The future of AI in product management – with Mike Todasco

Product Mastery Now for Product Managers, Leaders, and Innovators

How product managers are transforming innovation with AI tools

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TLDR

In this deep dive into AI’s impact on product innovation and management, former PayPal Senior Director of Innovation Mike Todasco shares insights on how AI tools are revolutionizing product development. From enhancing team brainstorming and prototype development to product iteration, AI is becoming an essential tool for product managers. However, Mike emphasizes the importance of balancing AI capabilities with human oversight, warning against over-reliance on AI. The discussion explores practical applications of AI tools like ChatGPT and Claude in product development, including MVP refinement, customer testing, and marketing content creation. Drawing from his experience building PayPal’s Innovation Labs, Mike also shares valuable insights on creating an innovation culture that empowers all employees to contribute to product innovation, regardless of their role.

Key Topics:

• Building Innovation Culture (PayPal Case Study)
• AI as a Brainstorming Partner
• AI Tools in Product Development
• Product Development Acceleration
• AI Implementation Cautions
• Future of AI in Product Development
• Customer Testing and Validation

AI’s Impact on Product Innovation and Management: A New Era for Product Teams

In this episode of Product Mastery Now, I’m interviewing Mike Todasco, former Senior Director of Innovation at PayPal and current visiting fellow at the James Silberrad Brown Center for Artificial Intelligence. Mike brings valuable insights about the revolutionary transformation of product development through artificial intelligence. Through our discussion, Mike shares how this dramatic acceleration in product development processes signals a fundamental shift for product teams. Drawing from his experience leading innovation at PayPal and holding over 100 patents, Mike explains how AI tools are creating new opportunities for innovation, faster iteration cycles, and more comprehensive market understanding while maintaining a balance between artificial intelligence and human insight.

Building Innovation Culture: Lessons from PayPal’s Innovation Lab

In our discussion, Mike shares insights from his experience building PayPal’s Innovation Lab following the company’s separation from eBay in 2015. He explains that their approach to innovation deliberately avoided the common pitfall of creating a two-tiered system where only designated “innovators” were responsible for new ideas.

Creating an Inclusive Innovation Environment

The foundation of PayPal’s innovation success rested on a culture of trust and autonomy. Mike points to their unlimited vacation policy as a symbol of this trust-based culture, where employees were treated as responsible adults capable of managing their time and contributions. This philosophy extended to how employees could engage with the Innovation Lab, allowing them to pursue innovative projects alongside their regular responsibilities.

Traditional Innovation Model	PayPal’s Inclusive Approach
Designated innovation teams	Open to all employees
Structured innovation times	Flexible engagement
Rigid definition of innovation	Adaptable interpretation
Top-down innovation goals	Self-directed innovation

Implementation Strategy

PayPal deliberately kept the definition of innovation flexible. Rather than imposing a strict interpretation, they allowed different roles to define innovation in ways that made sense for their work. Mike encouraged employees to include innovation in their annual goals but never forced this approach.

• Innovation goals were customized to individual roles and responsibilities
• The Innovation Lab served as a gathering space for collaborative work
• Employees had freedom to explore projects in their spare time
• Leadership encouraged but didn’t mandate innovation participation

This approach helped create a culture where innovation wasn’t seen as an additional burden but as an organic part of the workplace. While some areas of the company found this adjustment challenging, PayPal’s long-standing history of innovation made the cultural shift more natural. The success of this approach demonstrates how creating the right environment for innovation can be more effective than mandating it through formal structures.

Leveraging AI in Product Development: A Practical Approach

Mike shares examples of how AI is transforming product development, starting with his own daily interactions with tools like Claude and ChatGPT. His examples demonstrate the versatility of AI in both personal and professional contexts.

AI as Your Development Partner

Through our discussion, Mike explains how AI can serve as a brainstorming partner for product managers. He illustrates this with a recent experience helping an entrepreneur develop a video analysis product. What stands out is their approach to rapid iteration – continuously challenging themselves to simplify their concept, moving from four-week solutions to one-week versions, and ultimately to one-day tests. This methodology helps teams identify the core value proposition quickly.

Choosing the Right AI Tools

When it comes to selecting AI tools for product development, Mike shares several practical approaches to compare different models:

30-Minute Evaluation Method	Quick Comparison Method
Create test scenarios	Open multiple tool windows
Test across different AI models	Input identical prompts
Score responses systematically	Compare immediate responses
Evaluate reasoning patterns	Assess response quality

Available AI Tools for Product Managers

Mike outlines several key AI platforms product managers should consider:

• Claude: Excels at analytical tasks and detailed explanations
• ChatGPT: Strong general-purpose tool with quick responses
• Gemini: Google’s AI with robust integration capabilities
• Copilot: Particularly useful for technical development
• Mistral: Emerging option worth exploring

The key takeaway from our discussion is that AI tools aren’t just about automation – they’re about augmenting human creativity and decision-making in product development. Mike notes that while no single tool is perfect for every task, having multiple AI resources available allows product managers to leverage the right tool for specific needs.

The quality of AI’s work is not as good as human’s work, but its speed is superhuman, and product managers can take advantage of that.

AI Applications Across Product Development Phases

In our discussion, Mike provides valuable insights into how AI can enhance each stage of product development, particularly emphasizing the importance of rapid testing and validation. His perspective on using AI to accelerate the MVP (Minimum Viable Product) process is particularly enlightening. Product managers can use AI to help make their tests simpler.

Early Stage Development with AI

Mike strongly advocates for the 24-hour testing principle – the idea that teams should strive to test core concepts within a single day. He explains that AI tools can help product teams:

• Rapidly refine MVP concepts through multiple iterations
• Generate and evaluate multiple solution approaches quickly
• Test core assumptions before investing significant resources
• Create basic prototypes for initial feedback

Customer Testing and Validation

One of the most innovative approaches Mike shares is using AI for initial customer testing. However, he emphasizes that this should complement, not replace, traditional customer research.

Testing Phase	AI Role	Human Role
Initial Concept	Rapid persona-based testing	Define customer personas
Early Validation	Multiple iteration cycles	Interpret results
Market Testing	Automated feedback analysis	Customer interviews
Launch Preparation	Message testing	Strategic decisions

Mike suggests an experimental approach to using AI in early customer testing, though he emphasizes this is something he hasn’t fully implemented yet. He explains that product teams could potentially feed customer personas into AI models and run multiple tests to gauge reactions to different product options. For example, if you run the same prompt ten times and the AI selects option A eight times versus option B two times, this might indicate a preference pattern.

However, Mike strongly emphasizes that this approach should never replace actual customer research. He explains that while AI might help teams get their product into a better place before customer testing, it’s important to remember that AI models are trained on internet data, not real customer thoughts and behaviors. As he puts it, “People are weird complex beings,” and AI might not always catch the nuances of real customer behavior.

The key takeaway from Mike’s discussion is that while AI can be a useful tool for early-stage testing and iteration, it should be used to supplement, not replace, traditional customer research methods.

Product Launch and Marketing

Mike shares how AI can significantly enhance product launch activities:

• Generating initial marketing messages for different customer segments
• Testing various positioning approaches
• Creating customized content for different channels
• Analyzing market response patterns

What makes Mike’s approach particularly effective is his emphasis on using AI to accelerate the learning process while maintaining human oversight for strategic decisions. He explains that the goal isn’t to automate the entire development process but to remove bottlenecks and speed up iteration cycles.

Cautions in AI Implementation

Mike provides a word of caution. He introduces the metaphor of “falling asleep at the wheel” – if we over-rely on a driverless car that is not 100% perfect, we could be in trouble. Similarly, we should not over-trust AI in product development. This analogy serves as a reminder of the importance of maintaining human oversight in AI-assisted processes.

Understanding the Risks

Mike shares real-world examples of AI implementation failures, citing incidents at Sports Illustrated and CNET where over-reliance on AI led to publishing errors. He explains that these situations often occur not because the AI tools failed completely, but because human oversight gradually decreased after seeing consistent success.

Risk Area	Warning Signs	Preventive Measures
Customer Understanding	Over-reliance on AI-generated personas	Regular real customer interactions
Decision Making	Automatic acceptance of AI suggestions	Structured human review process
Content Creation	Minimal editing of AI outputs	Thorough human verification
Market Analysis	Exclusive use of AI interpretations	Cross-reference with human insights

Balancing AI and Human Input

Mike emphasizes several key principles for maintaining effective AI integration:

• AI should not be a replacement for interactions with real customers
• Use AI as a complement to human expertise, not a replacement
• Maintain regular customer contact through traditional research methods
• Implement structured review processes for AI-generated content
• Regularly validate AI insights against real-world data

The most valuable insight Mike shares is that AI tools should enhance rather than replace human judgment. He explains that while AI can process information and generate options at superhuman speeds, the final decisions about product direction should always incorporate human experience and intuition. This balanced approach ensures that teams can benefit from AI’s capabilities while avoiding the pitfalls of over-automation.

The Future of AI in Product Development: Team Collaboration

In our discussion, Mike shares an exciting vision of how AI will transform team collaboration in product development. Drawing from his experience running innovation sessions at PayPal, where teams of 5-25 people would gather in the innovation lab, he explains how AI could enhance these collaborative environments.

AI as a Team Member

Mike describes several ways AI could augment team interactions:

• Acting as a neutral, knowledgeable participant in brainstorming sessions
• Capturing and synthesizing team discussions in real-time
• Providing fresh perspectives when conversations hit a lull
• Helping teams maintain energy and creativity during intensive sessions

Evolution of Workspace Integration

Looking five years ahead, Mike envisions AI becoming seamlessly integrated into everyday work environments:

Current State	Future Integration
Individual AI interactions	AI-enabled conference rooms
Manual note-taking	Automated meeting synthesis
Scheduled brainstorming	Continuous AI collaboration
Text-based AI interaction	Multi-modal AI communication

Emerging Collaboration Patterns

Mike shares how these changes are already beginning to appear. He points to WhatsApp’s integration of AI into group chats as an example of how AI collaboration is evolving. In these environments, AI can:

• Contribute to group discussions when prompted
• Help teams find information or resources quickly
• Assist with scheduling and coordination
• Provide real-time analysis of ideas and suggestions

The key insight Mike emphasizes is that this future isn’t about replacing human collaboration but enhancing it. He explains that AI can help teams overcome common barriers in collaborative work, such as mental fatigue during intensive brainstorming sessions or the challenge of capturing and organizing multiple threads of discussion.

Conclusion

Throughout our discussion, Mike Todasco shares valuable insights about integrating AI tools into product development processes, drawing from his experience at PayPal’s Innovation Lab and his current work in artificial intelligence. His practical approach to using AI as a development partner while maintaining human oversight provides a blueprint for product managers looking to enhance their innovation processes.

The key to success lies in striking the right balance – using AI to accelerate ideation, streamline product development, and enhance team collaboration while maintaining the human judgment essential for product success. As Mike emphasizes, AI tools aren’t replacing product managers; they’re empowering them to work more efficiently and innovatively. For product teams ready to embrace this transformation, the combination of AI-powered product development tools and human creativity opens new horizons for product innovation and market success.

Useful links:

• Connect with Mike on LinkedIn
• Read Mike’s articles on Medium
• Subscribe to Mike’s Newsletter, AI Conversations
• Learn more about the James Silberrad Brown Center for Artificial Intelligence at San Diego State University SDSU

Innovation Quote

“The best way to have a good idea is to have lots of ideas.” – Linus Pauling

Application Questions

1. How could you restructure your current sprint process to incorporate AI tools while maintaining the most valuable human interactions?
2. How could your team use AI to get faster feedback on product concepts while ensuring you’re still capturing genuine customer insights?
3. What safeguards could you put in place to prevent over-reliance on AI while still taking full advantage of its capabilities?
4. How could you integrate AI into your team’s brainstorming sessions in a way that enhances rather than replaces human creativity?
5. How could you balance the speed of AI-powered development with the need for thoughtful product decisions and human oversight?

Bio

Mike Todasco is a former Senior Director of Innovation at PayPal and a current Visiting Fellow at the James Silberrad Brown Center for Artificial Intelligence at SDSU. With over 100 patents to his name, Mike played a key role in fostering a culture of innovation across PayPal’s 20,000+ employees. A recognized expert in AI and innovation, he explores how AI can enhance creativity and revolutionize business processes and personal tasks. Passionate about democratizing advanced technology, Mike advocates for enabling innovation without requiring deep technical expertise. He frequently shares his insights on AI’s impact on innovation, decision-making, and cognition through articles on Medium and LinkedIn.

Thanks!

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