The future of AI in product management – with Mike Todasco
Chad McAllister, PhD
Product management professor, practitioner, and top-1% podcast host. I help product managers and leaders prepare for the next step in their careers.
To listen to the interview, search for Product Mastery Now on your favorite podcast player.
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:
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 ModelPayPal’s Inclusive ApproachDesignated innovation teamsOpen to all employeesStructured innovation timesFlexible engagementRigid definition of innovationAdaptable interpretationTop-down innovation goalsSelf-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.
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 MethodQuick Comparison MethodCreate test scenariosOpen multiple tool windowsTest across different AI modelsInput identical promptsScore responses systematicallyCompare immediate responsesEvaluate reasoning patternsAssess response quality
Available AI Tools for Product Managers
Mike outlines several key AI platforms product managers should consider:
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:
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 PhaseAI RoleHuman RoleInitial ConceptRapid persona-based testingDefine customer personasEarly ValidationMultiple iteration cyclesInterpret resultsMarket TestingAutomated feedback analysisCustomer interviewsLaunch PreparationMessage testingStrategic 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.
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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:
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 AreaWarning SignsPreventive MeasuresCustomer UnderstandingOver-reliance on AI-generated personasRegular real customer interactionsDecision MakingAutomatic acceptance of AI suggestionsStructured human review processContent CreationMinimal editing of AI outputsThorough human verificationMarket AnalysisExclusive use of AI interpretationsCross-reference with human insights
Balancing AI and Human Input
Mike emphasizes several key principles for maintaining effective AI integration:
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:
Evolution of Workspace Integration
Looking five years ahead, Mike envisions AI becoming seamlessly integrated into everyday work environments:
Current StateFuture IntegrationIndividual AI interactionsAI-enabled conference roomsManual note-takingAutomated meeting synthesisScheduled brainstormingContinuous AI collaborationText-based AI interactionMulti-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:
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:
Innovation Quote
“The best way to have a good idea is to have lots of ideas.” – Linus Pauling
Application Questions
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.
To listen to the interview, search for Product Mastery Now on your favorite podcast player.
Product Management Executive | Driving Strategic Roadmaps, Agile Innovation & Revenue Growth | Expert in SaaS & Digital Transformation
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