517: How to conduct an AI Design Sprint – with Mike Hyzy

Product Mastery Now for Product Managers, Leaders, and Innovators

A custom ChatGPT model that helps accelerate product innovation

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TLDR

In this episode, I interview Mike Hyzy, Senior Principal Consultant at Daugherty Business Solutions. He explains how to conduct an AI-powered design sprint that transforms product concepts into clickable prototypes in just hours instead of weeks. Using a custom ChatGPT model combined with collaborative team workshops, product teams can rapidly move from initial customer insights to validated prototypes while incorporating strategic foresight and market analysis.

Key Topics:

• Strategic foresight approach to product development, focusing on customer needs 2-5 years ahead
• Triple diamond decision framework for analyzing problems, customers, and markets
• Integration of team collaboration, AI assistance, and external validation
• Rapid wireframe and UI design generation using ChatGPT and DALL-E
• Creation of interactive prototypes using CodePen for immediate testing
• Custom AI model prompts and best practices for design sprint facilitation
• Early go-to-market strategy integration in the product development process
• Practical implementation of AI tools to accelerate product innovation

Introduction

Imagine taking a product concept from initial customer insight to clickable prototype in just a few hours. That’s exactly what I witnessed at PDMA’s recent Inspire Innovation Conference, where Mike Hyzy demonstrated a groundbreaking approach to AI Design Sprints that’s revolutionizing product development acceleration.

By combining strategic foresight, a custom ChatGPT model, and collaborative workshop techniques, Mike led how six teams to achieve what typically takes weeks of work in just under three hours. As a product management professor and practitioner, I’ve seen many methodologies for speeding up innovation, but this approach was different – transforming ChatGPT into a virtual team member that accelerates every phase of the development process, from initial concept through digital product prototyping, while ensuring teams focus on solving tomorrow’s customer needs rather than just today’s problems.

In this episode, Mike will take us through the steps he led product teams through during his AI Design Sprint workshop.

The Critical Role of Strategic Foresight in Product Innovation

At the beginning of the workshop, Mike explained the importance of strategic foresight. He emphasized a fundamental shift in how we should approach product development. Instead of focusing solely on today’s customer problems, product teams need to look 2-5 years into the future. This strategic foresight approach to product development isn’t just about making predictions – it’s about understanding how customer needs and market conditions will evolve over time.

Mike shared a sobering statistic that highlights why this forward-thinking approach matters: 42% of companies cite “no market need” as their main reason for failure. This happens when teams solve today’s problems without considering how those needs might change by the time their product actually launches. As I’ve seen in my own product management experience, the traditional product development cycle can take months or even years. By the time we launch, the market may have moved on from the problem we originally set out to solve.

The Triple Diamond Framework Components

To address this challenge, Mike introduced the Triple Diamond Decision Framework, a structured approach that helps teams look ahead while making concrete decisions. Here’s how the framework breaks down:

1. Jobs to be Done Diamond
   • Explore future customer problems and needs
   • Identify emerging pain points
   • Converge on the most critical future needs
2. Customer Analysis Diamond
   • Map potential future customer segments
   • Analyze evolving customer behaviors
   • Focus on the most promising future customers
3. Market Analysis Diamond
   • Investigate market trends and opportunities
   • Evaluate potential market sizes
   • Select the most viable future markets

What makes this framework particularly powerful in an AI design sprint is how quickly teams can move through each diamond. Mike explains that traditional market analysis might take weeks of research, but with AI assistance, teams can gather initial market insights, including total addressable market (TAM) and serviceable market data, in minutes rather than weeks.

The key to success with this approach lies in the balance between divergent and convergent thinking at each stage. Teams start by thinking broadly about all possible needs, customers, or markets, then use data and insights to narrow down to the most promising opportunities. Mike emphasizes that this isn’t about rushing through the process – it’s about using AI tools to accelerate the research and analysis phases so teams can spend more time on creative problem-solving and validation.

This strategic foresight foundation sets the stage for the entire AI design sprint process. By starting with a future-focused mindset and using AI to accelerate market research, teams can avoid the common trap of building products for yesterday’s problems while ensuring they’re creating solutions that will still be relevant when they reach the market.

Three Key Elements That Power AI-Powered Design Sprints

In this episode, Mike outlines how the success of an AI design sprint relies on the synergy between three core elements. Rather than simply replacing traditional methods with AI tools, this approach creates a powerful combination of human creativity, artificial intelligence, and real-world validation.

1. Team Collaboration

The foundation of every successful AI design sprint starts with effective team collaboration. As I observe during the workshop, the magic happens when small groups work together to explore ideas and challenge assumptions. Mike explains that having multiple perspectives around the table leads to insights that neither AI nor individual team members would discover alone.

For example, during our workshop session, when one team member mentioned the need for pricing tiers in their product concept, it triggered a deeper discussion about what would motivate users to upgrade from a free version to a paid tier. This kind of nuanced thinking emerges naturally from team interactions.

2. AI Tool Integration

The integration of AI tools, particularly through Mike’s custom ChatGPT model, serves as a catalyst for rapid product development. Here’s how AI enhances the process:

• Accelerates market research and data gathering
• Identifies potential blind spots in thinking
• Suggests alternative approaches and solutions
• Generates rapid prototypes and iterations
• Provides structured frameworks for decision-making

What makes this element particularly powerful is how the AI tool becomes like another team member, offering insights and suggestions while the human team maintains control over creative decisions and strategic direction.

3. External Validation

The third critical element involves getting feedback from outside the immediate team. During the workshop, Mike structures this through team-to-team interactions, where each group presents their concepts to another team for feedback. In a real-world setting, this would involve:

Validation Level	Purpose	Timing
Initial Feedback	Quick reality check on concepts	Early in the sprint
Feature Validation	Confirm priority features	Mid-sprint
Prototype Testing	User experience validation	Late sprint

What makes this three-element approach particularly effective is how each component complements the others. The team’s creative energy feeds into the AI tool’s capabilities, while external validation helps refine and improve the outputs from both human and AI contributions.

Mike emphasizes that the real power comes from the rapid iteration possible when these three elements work together. Teams can quickly move from initial concept to validated prototype, with each element providing different types of input and validation along the way. This combination helps ensure that the final product concept isn’t just technically feasible but also genuinely meets market needs.

Breaking Down the AI Design Sprint Process

In this episode, Mike walks us through the step-by-step process of conducting an AI-powered design sprint. In his workshop, teams used Mike’s custom ChatGPT model, AI Design Sprint. What’s particularly impressive is how this approach compresses what traditionally takes weeks into just a few hours, while still maintaining the rigor needed for effective product development.

Discovery Phase

The discovery phase sets the foundation for the entire sprint. Mike structures this phase into distinct segments, each building on the previous one:

1. Initial Idea Generation

The first prompt for ChatGPT tells it that you’re going to use the triple diamond decision framing to explore needs, customers, and markets. You’ll work through it one stage at a time, starting with the discovery stage. It directs ChatGPT to read your input and ask corresponding questions. You’ll finish one section before moving on to the next one.

The AI tool supports this process by:

• Providing prompting questions to spark discussion
• Suggesting potential angles teams might have missed
• Organizing ideas into structured formats
• Documenting key insights for later reference

During the workshop, my team worked on the question, How do I use the space I have in my yard to create a garden? During the Discovery phase, once we told ChatGPT our initial ideas, its asked us the questions:

• Who would benefit from this product?
• What are their needs?

We typed our answers into ChatGPT, which used them to build a customer persona.

2. Needs Analysis

Once initial ideas are captured, teams dive deeper into understanding customer needs. The AI assistant helps accelerate this process by:

Analysis Type	AI Support	Team Input
Customer Pain Points	Market research synthesis	Real-world experience validation
Unmet Needs	Pattern recognition	Context and nuance addition
Future Needs	Trend analysis	Industry expertise application

Definition Phase

Moving into definition, teams begin to shape their solution. Mike shows how the AI tool helps teams:

Synthesize Key Insights

• Compile user insights from discovery
• Identify core opportunities
• Define essential functionality

Prioritize Features

• Create MVP feature sets
• Identify secondary features
• Tag nice-to-have additions

Development Phase

The development phase is where the AI-powered approach really shines. Mike demonstrates how teams can rapidly move through:

1. Wireframing

Using the AI tool’s connection to DALL-E, teams can generate wireframes for each feature. What’s remarkable is how quickly teams can iterate on these designs. Mike shows us how to:

• Generate individual screens for each feature
• Refine layouts based on team feedback
• Maintain consistency across the interface

2. UI Design

The sprint moves from wireframes to more detailed UI designs. Teams can specify:

• Color schemes (like “Earth tones similar to Whole Foods”)
• Design patterns (such as Google’s Material Design)
• Typography and spacing preferences

3. Interactive Prototype

The final step involves creating a clickable prototype using:

• HTML generated by the AI tool
• CSS for styling
• JavaScript for interactivity

Mike shows how teams can use CodePen as a free platform to bring these elements together into a working prototype. This allows for immediate testing and validation of the user experience.

What makes this process particularly valuable is its flexibility. While Mike guides us through all these steps, he emphasizes that teams can adjust the focus based on their specific needs. Some teams might spend more time in discovery, while others might need to iterate more on the prototype phase.

Best Practices for Implementing AI Design Sprints

Drawing from his experience leading multiple AI-powered design sprints, Mike shares key tips and strategies to help teams maximize the value of this approach. These implementation guidelines ensure teams can effectively combine human creativity with AI capabilities while maintaining focus on creating valuable products.

AI Interaction Best Practices

Mike emphasizes the importance of structuring your interaction with AI tools effectively. Here’s how to get the best results:

Practice	Purpose	Example
One Stage at a Time	Maintain focus and clarity	Complete market analysis before moving to features
Clear, Specific Prompts	Get targeted responses	“Create separate wireframes for each feature”
Regular Progress Saving	Preserve work across sessions	Save summaries after each major phase

Prototype Development Guidelines

When it comes to creating prototypes, Mike shares several key strategies:

Wireframe Creation

• Request separate screens for each feature
• Be specific about design preferences
• Iterate based on team feedback

Code Structure

• Keep HTML, CSS, and JavaScript organized
• Use CodePen for quick testing
• Maintain consistent naming conventions

Feedback Integration

• Capture team input immediately
• Make rapid adjustments
• Test changes in real-time

Go-to-Market Integration

One of Mike’s most valuable insights is the importance of thinking about go-to-market strategy early in the process. He recommends:

• Developing marketing messages during the sprint
• Creating 30-second elevator pitches
• Testing value propositions with other teams
• Planning launch strategies alongside development

Time Management Tips

To keep the sprint moving efficiently, Mike suggests:

• Use 10-minute focused sessions for each activity
• Set clear objectives for each sprint segment
• Build in quick breaks between major phases
• Allow flexibility for deeper exploration when needed

Common Pitfalls to Avoid

Through his experience, Mike has identified several challenges teams should watch out for:

• Getting stuck in endless iterations without moving forward
• Relying too heavily on AI without human insight
• Skipping validation steps to save time
• Forgetting to save progress between sessions

What I find particularly valuable about Mike’s approach is how he balances efficiency with effectiveness. While the AI-powered sprint can move quickly, he ensures teams don’t sacrifice quality for speed. He emphasizes that the goal isn’t just to create a prototype faster – it’s to create a better product by allowing teams to explore more options and gather more feedback in less time.

Making AI Design Sprints Work: Key Success Factors

In this episode, Mike shares the critical elements that determine the success of an AI-powered design sprint. As I observe during the workshop, these factors make the difference between simply using AI tools and truly transforming the product development process.

Team Dynamics

The human element remains crucial even in AI-powered sprints. Mike identifies several key team factors:

Factor	Impact	Implementation
Balanced Input	Ensures diverse perspectives	Mix of technical and business roles
Cross-functional Expertise	Enriches solution development	Include design, tech, and product skills
Collaborative Spirit	Drives rapid iteration	Encourage building on others’ ideas

Strategic Foresight Integration

Mike emphasizes that successful teams consistently maintain a future focus throughout the sprint:

Market Evolution

• Consider technological trends
• Anticipate changing customer needs
• Factor in competitive landscape shifts

Solution Longevity

• Design for future scalability
• Plan for evolving user expectations
• Build in adaptation capabilities

Validation Approach

Effective validation proves crucial for sprint success. Mike recommends:

• Test concepts with other teams during the sprint
• Use AI insights to challenge assumptions
• Maintain a balance between human feedback and AI analysis
• Document validation findings for future reference

Tool Mastery

Understanding how to effectively use AI tools makes a significant difference. Mike shares these best practices:

• Start with simple prompts and build complexity
• Save successful prompts for future use
• Learn from how the AI responds to different input styles
• Maintain a library of effective prompt patterns

Outcome Focus

Successful teams keep their eyes on meaningful outcomes:

Outcome Type	Success Indicator
Product Concept	Clear value proposition validated by feedback
Market Fit	Identified target market with validated need
Technical Feasibility	Realistic implementation path defined
Business Viability	Compelling business case established

What makes these success factors particularly powerful is their interconnected nature. Mike demonstrates how each element supports the others, creating a robust framework for innovation. The combination of human creativity, AI capabilities, and structured validation helps teams not just move faster, but also make better decisions throughout the product development process.

Real Results: Workshop Outcomes and Next Steps

In this episode, Mike shares the impressive results from the PDMA workshop, demonstrating how AI-powered design sprints can transform product development. The outcomes show both the immediate value and long-term potential of this approach.

Workshop Achievements

The teams in the workshop accomplished several key deliverables in under three hours:

Deliverable	Traditional Timeline	Sprint Timeline
Market Analysis	2-3 weeks	15-20 minutes
Feature Definition	1-2 weeks	30 minutes
UI Design	1-2 weeks	45 minutes
Interactive Prototype	1-3 weeks	60 minutes

Practical Applications

Mike explains how teams can apply this methodology in different contexts:

Startup Environment

• Rapid concept validation
• Quick pivot capability
• Efficient resource use

Enterprise Setting

• Innovation acceleration
• Cross-team collaboration
• Risk reduction through rapid testing

Product Enhancement

• Feature validation
• User experience improvement
• Competitive response

Next Steps for Implementation

For teams looking to implement AI-powered design sprints, Mike recommends:

• Start with a small, focused project to build team confidence
• Use the provided custom ChatGPT model as a starting point
• Document and adapt the process for your organization’s needs
• Build a library of successful prompts and approaches

Long-term Benefits

Beyond the immediate sprint outcomes, Mike highlights several lasting advantages:

• Improved team collaboration patterns
• Enhanced decision-making processes
• Better integration of strategic foresight
• More efficient resource utilization
• Faster time to market for new products

Future Possibilities

Looking ahead, Mike sees several exciting possibilities:

• Integration with more sophisticated AI tools
• Enhanced prototype generation capabilities
• Improved market analysis accuracy
• More automated validation processes

What makes these outcomes particularly compelling is their practical nature. As I observe during the workshop, teams aren’t just creating theoretical concepts – they’re developing viable product solutions that could move directly into development. The combination of speed and quality demonstrates why AI-powered design sprints represent a significant evolution in product development methodology.

Mike emphasizes that while the technology is impressive, the real value comes from how it enables teams to spend more time on creative problem-solving and less time on routine tasks. This shift in focus helps ensure that the increased speed of development doesn’t come at the expense of innovation or product quality.

Conclusion

As this episode demonstrates, AI-powered design sprints represent a significant leap forward in product development methodology. Mike’s approach successfully combines the creative power of human teams with the efficiency of AI tools, enabling product managers to compress weeks of work into focused sessions while maintaining high-quality outcomes. The custom ChatGPT model he’s created, coupled with structured team activities and validation steps, provides a practical framework that any product team can implement.

What makes this methodology particularly valuable is its focus on future needs and market evolution. Rather than simply accelerating existing processes, these AI-powered design sprints help teams create better products by enabling rapid iteration, comprehensive market analysis, and meaningful validation. As Mike shows us, the future of product development isn’t just about working faster – it’s about working smarter by leveraging AI to enhance human creativity and strategic thinking.

Useful links:

• Check out Mike’s Custom AI Model for the AI Design Sprint
• Connect with Mike on LinkedIn
• Check out Mike’s website
• Check out Mike’s Substack
• Check out Mike’s book, Gamification for Product Excellence
• Learn more about PDMA

Innovation Quote

“Product innovation is about having the foresight, which is not about creating solutions for problems that we know exist today, but about anticipating challenges and opportunities that might emerge in the future.” – Mike Hyzy

Application Questions

1. How could your team integrate strategic foresight into your current product development process? Consider which aspects of your market and customer needs are most likely to evolve in the next 2-5 years.
2. How could you use AI tools to accelerate the parts of your product development process that currently take the most time? Think about areas like market research, competitive analysis, or prototype creation.
3. Looking at your most recent product development initiative, how could the triple diamond framework have helped you better validate the market need before investing in development?
4. How could your team structure a compressed design sprint using these AI-powered techniques while still ensuring you get meaningful validation from actual customers or stakeholders?

Bio

Mike Hyzy is a senior principal consultant at Daugherty Business Solutions. He advises executive teams on AI, innovation and strategic product management, combining data-driven insights with cutting-edge technology to drive transformational change. Previously he has been a product management consultant and has held senior product management roles.

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