Product & Design
Ready to connect with your peers in product and design? Grab your free ticket for Startup Boston Week on Sept 14 - 18 today!
Just Because You Can Build It Doesn't Mean You Should: The AI-Era Validation Playbook for Product Managers
AI has dramatically reduced the time it takes to build products, prototypes, and new features. The problem is that many teams are still using validation frameworks designed for a world where development was the bottleneck.
This session helps product managers and founders rethink validation for the AI era.
You will learn:
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How AI changes the traditional product validation timeline and what should be validated first
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Techniques for conducting problem-first customer discovery conversations
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How to design and run concierge MVPs before building complex AI functionality
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Ways to leverage AI during research, synthesis, and experiment design without introducing bias
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Metrics and decision frameworks for determining when to continue, pivot, or stop an initiative
Building is cheaper than ever. Validation is more important than ever. Join us to create a structured validation plan that helps you spend less time guessing and more time building products customers actually want.
The Future Is Co-Pilot, Not Autopilot: Designing Human-AI Collaboration
As AI agents become more capable, teams face a new challenge: deciding what should be automated, what should remain human-driven, and where the greatest value comes from collaboration between the two.
The most effective AI-powered workflows don't simply replace people. They combine human judgment, creativity, and context with AI's speed, scale, and pattern recognition.
This session explores how product, design, and operations leaders are building collaborative systems that improve outcomes across customer support, analytics, research, operations, and product development without sacrificing quality, trust, or accountability.
You will learn:
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Common patterns behind successful human-AI collaboration models
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Frameworks for deciding which tasks should be human-led, AI-led, or shared
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How to design workflows and interfaces that make AI contributions transparent and understandable
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Strategies for driving adoption and reducing resistance when introducing AI-powered workflows
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Ways to measure the impact of collaborative systems on quality, efficiency, and customer experience
The goal isn't automation for automation's sake. It's building systems where humans and AI each contribute what they do best. Join us to learn h
The Lab Is Not the Market: Translating Deeptech into Customer Value
Boston is home to some of the world's most impressive breakthroughs in robotics, biotech, AI, life sciences, and advanced computing. Yet many promising innovations struggle to make the leap from research achievement to successful product.
The challenge isn't always the technology. It's translating complex capabilities into clear customer value, intuitive product experiences, and compelling market positioning.
This session explores how product leaders, designers, and founders bridge the gap between technical innovation and real-world adoption through better product thinking, user experience design, and storytelling.
You will learn:
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How to identify a primary user and use case when a technology has multiple potential applications
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Approaches for simplifying complex products without oversimplifying the underlying science
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Ways to communicate technical differentiation without relying on jargon
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Strategies for helping product, design, and research teams work more effectively together
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Common product and UX mistakes that slow adoption of deeptech innovations
Great technology doesn't automatically become a great product. Join us to learn how founders and product teams transform complex innovations into experiences customers can understand, adopt, and champion.
The AI Money Pit: Unit Economics, Pricing, and Survival
Building an AI product is easier than ever. Building one with sustainable economics is a very different challenge.
As AI adoption grows, many founders are discovering that serving customers can become dramatically more expensive than acquiring them. Between inference costs, model selection, cloud spend, and unpredictable usage patterns, seemingly healthy growth can hide serious financial risks.
This session explores how experienced AI founders think about pricing, margins, infrastructure costs, and long-term sustainability when operating AI products at scale.
You will learn:
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What healthy unit economics look like for AI startups and how to benchmark their business
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Approaches for pricing AI products when serving costs fluctuate over time
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Frameworks for deciding when to optimize inference, switch models, or invest in proprietary solutions
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Practical strategies for controlling compute costs and negotiating infrastructure expenses
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How to communicate AI economics and efficiency improvements to investors and stakeholders
Revenue growth is only valuable if your business becomes stronger as it scales. Join us to learn how successful AI companies manage costs, improve margins, and avoid becoming victims of their own growth.
From Validation to Vigilance: Designing Products for AI Uncertainty
AI products don't fail because they make mistakes. They fail because nobody planned for what happens when they do.
Unlike traditional software, AI systems operate with uncertainty. Outputs can be incomplete, inaccurate, biased, or simply unexpected. The most successful AI products aren't the ones that eliminate every failure - they're the ones that help users understand uncertainty, recover from mistakes, and maintain confidence when things don't go according to plan.
This session explores how product teams design for the real-world challenges of AI, from hallucinations and edge cases to explainability, human oversight, and resilience.
You will learn:
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UX patterns that help users navigate uncertainty and maintain a sense of control
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How to design workflows that gracefully handle hallucinations, errors, and unexpected outputs
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When and how to use confidence scores, explainability features, and human-in-the-loop systems
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Approaches for identifying and mitigating bias before it becomes a customer problem
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Strategies for designing products that recover effectively when failures inevitably occur
The question isn't whether your AI will make mistakes. It's whether your product is prepared for them. Join us to learn how leading teams design AI experiences that perform reliably even when certainty isn't possible.
Meet Brian Bozzuto! Brian is our Product & Design Track Lead for Startup Boston Week 2026. This is his second year on the Startup Boston Organizing Team.