Superheros of Product Discovery: The Product Discovery Canvas and Artificial Intelligence

Get ready to embark on a product discovery journey like never before! In 2015, I unleashed the Product Discovery Canvas (PDC), a one-page document that draws on the operational principles of the Business Model Canvas and Lean Canvas, incorporating product innovation insights from the thought leadership of David Hussman, Jeff Patton, Alberto Savoia, and Eric Ries. But guess what? The PDC has evolved, bringing along some AI superheroes!

Picture this: ChatGPT 4 and the PDC joining forces, like Batman and Robin, to save the day for product discovery. They're the dynamic duo of ideation with context-aware dialogue and multimodal-aware wizardry. And let's not forget about the retrieval augmented generation MindMeld - it's like they can read minds! This isn't old-school product brainstorming; this is the future! So buckle up and get ready to discover products like never before with this unstoppable team.

Join me in unveiling the revamped Product Discovery Canvas at this workshop. Bring your A-game, your product team, product ideas, and, of course, your trusty laptop or tablet. We're diving deep into Collaborative Product Chartering, Story Mapping, Pretotyping, and Lean Startup. It's not just a workshop; it's a playground for innovation.

The Product Discovery Canvas has been used in Fortune 500 companies across various industries, including FinTech, Big Data, Telecommunications, and Automotive. Now, let’s revolutionize your product ideation – because why settle for ordinary when you can have extraordinary? See you there!

Target Audience

This workshop is suitable for a wide audience, from learners to experts, of product owners, developers, business analysts, managers, startup founders, and agile enthusiasts interested in communicating product needs more effectively.

Learning Outcomes

In this hands-on workshop, participants will gain practical experience utilizing Ask: PDC, a ChatGPT-like AI tool, to conduct product discovery using the principles first explored in the Product Discovery Canvas. Specifically, participants will take away good practices of AI "Prompt Design" to rapidly develop these product assets:

• Collaborative Product Charter.
• Story Map.

With the time-warping superpowers of AI, we might be able to achieve an awesome Iteration-1 of these items:

• Pretotypes to validate product ideas rapidly.
• MVP to apply Validated Learning.

Information for the Speaker Selection Committee

NOTE: I will provide attendees with a pre-built environment for the AI tools used during the workshop. This enables the ease of Plug-N-Play to iterate through each of the product discovery strategies rapidly.

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Workshop Time Breakdown — 90 conference workshop

Introductions - 10 minutes 

AI-Centered Product Discovery -60 minutes
• Prompt design 
• Bring your own data
• Collaborative Product Charter
• Story Map - 30 minutes
• Iteration-1 of Pretotyping
• Iteration-1 of MVP

Wrap up and Q&A - 20 minutes

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Notes on Pretotyping; this is not a spelling error.

Pretotyping is a set of tools, techniques, and tactics designed to help you validate any idea for a new product quickly, objectively, and accurately. The goal of pretotyping is to help you make sure that you are building The Right It before you build It right.

Pretotyping was originally developed at Google in 2010 and since then has been tested, refined, taught, and put into practice with great success in hundreds of projects and organizations.

In his book, Alberto Savoia shows you how to use pretotyping tools to beat the odds and make sure that your ideas succeed in the market. With dozens of examples and case studies, insights from his time at Google and Stanford, as well as his experience as an entrepreneur and innovator, The Right It is full of powerful, effective, and immediately actionable tools, techniques, and tactics.
~https://www.pretotyping.org/
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Presentation History

https://speakerdeck.com/billagileinnovator

Detecting Bias in AI: Building Fair and Ethical Models

As AI increasingly shapes our reality, recognizing and mitigating potential biases in these technologies is crucial. Large language models (LLMs), despite being trained on vast datasets, can reflect and amplify existing societal biases present in the vast and diverse datasets used to train them.

We'll discuss how biases in training data can propagate through AI systems, potentially perpetuating societal inequalities. As Dr. Joy Buolamwini, founder of the Algorithmic Justice League, emphasizes, “When AI systems are used as the gatekeeper of opportunities, it is critical that the oversight of the design, development, and deployment of these systems reflect the communities that will be impacted by them.” [1]

While many emerging AI companies aim to ensure their technology benefits humanity, the rapid expansion of AI applications introduces new ethical challenges. This session explores practical techniques for implementing AI observability, governance, and responsible AI management aligned with industry standards.

This presentation will delve into the standard and technical practices necessary to detect and mitigate AI systems' bias. You'll learn how to:
- Identifying bias sources in AI systems
- Implementing fair AI practices throughout the development lifecycle
- Ensuring compliance with emerging AI regulations and ethics guidelines

Target Audience:

This presentation is designed for professionals across various stages of AI adoption who prioritize ethical AI implementation.

Learning Outcomes:

Upon completion of this session, attendees will be equipped to:
1. Assess and mitigate bias in AI training data
2. Identify and document performance disparities in AI models
3. Develop and implement comprehensive testing strategies for detecting unfair biases
4. Implement governance frameworks for responsible AI development and deployment

Info for the Speaker Selection Committee

[1] OpenAI’s technology is upending our everyday lives. It’s overseen exclusively by wealthy, White men

https://www.cnn.com/2023/12/15/tech/openai-board-diversity/index.html

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Time Breakdown for 90-minute workshop

Introductions - 10 mins

Detecting examples of bias in AI - 65 mins
• Why biases are undesirable
• Evaluating model fairness
• Metrics for quantifying discrimination
• Bias mitigation metrics and toolkits
• Auditability, explainability, and accountability

Wrap up and Q&A - 15 minutes

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Presentation History

https://speakerdeck.com/billagileinnovator

Leveraging Gen AI in Agile Teams: Becoming High Performing

Artificial Intelligence (AI) is seen as a transformative opportunity in the fast-changing world of technology. This workshop will explore the transformative impact of Generative AI on various aspects of agile team operations:

From the Developers' perspective, we'll examine how Gen AI can revolutionize coding practices. Imagine having an intelligent assistant that helps you code and test more effectively to optimize your development workflow.

For Product Owners and Dev Managers, Gen AI's ability to analyze complex requirements and manage traceability between code, tests, and requirements will be a key focus area.

Scrum Masters aren't left out. We'll explore how Gen AI can assist in creating Collaborative Charters, User Story Maps, and Behavior-Driven Development scenarios to orchestrate dynamic understandings for the team.

This workshop includes role-based breakout sessions where participants will use a GPT, digital whiteboard, and an AI code editor to perform everyday agile team tasks. No prior AI experience is required - participants need only bring their enthusiasm and a phone, tablet, or laptop to participate in guided exercises that provide immediate, practical value for their agile teams.

Join us for this transformative opportunity to discover how Gen AI can empower your agile team to become High-Performing.

Target Audience

This talk is ideal for anyone at the intersection of Agile and AI.

Learning Outcomes

By attending this talk, you will gain the following insights:

1) Utilize Generative AI to Enhance Coding and Testing Practices
• Attendees will learn how developers can integrate Gen AI tools, such as Cursor or bolt.new, to enhance coding efficiency, improve testing processes, and optimize overall workflows.

2) Enhance Requirement Analysis and Traceability with Gen AI
• Participants will discover methods for Product Owners and Dev Managers to utilize custom chatGPT prompts to analyze complex requirements and maintain robust traceability between code, tests, and requirements.

3) Leverage AI for Advanced Agile Facilitation Techniques
• Scrum Masters will gain insights into using the Atlassian AI Scrum Assistant to create Collaborative Charters, develop User Story Maps, and craft Behavior-Driven Development scenarios.

4) Identify and Mitigate Challenges of Implementing Gen AI in Agile Frameworks
• Participants will be equipped to recognize potential obstacles and learn effective approaches to overcome them.

Time Breakdown for 90-minute workshop

Introduction and Objectives (5 minutes)
• Brief introduction to Generative AI and its relevance to Agile teams
• Workshop objectives and outcomes

Scrum Masters' Role (25 minutes)
• Group exercise exercise: Use Atlassian AI Scrum Assistant and Miro to create Collaborative Charters, User Story Maps and Behavior-Driven Development scenarios for a simple web app.

Developers' Perspective (15 minutes)
• Group exercise: Use bolt.new to generate code, test, and deploy a simple web app.

Product Owners and Dev Managers (25 minutes)
• Group exercise: Write custom ChatGPT prompts to inspect the traceability between code, tests, and requirements for the simple web app.

High-Performing Agile Teams (15 minutes)
• Discussion: Challenges and successes in implementing Gen AI

Conclusion and Next Steps (5 minutes)
• Q&A session

Setup and Equipment Required

A phone, tablet, or laptop that can run a browser and access the internet.

Is My Agile Toolchain Free from AI Bias?

Many emerging AI companies strive to ensure their technologies benefit humanity. However, the rapid growth of AI applications presents new ethical challenges. As AI technologies are increasingly applied to the Agile development ecosystem—impacting everything from defining requirements to completing code and conducting automated testing—agile teams must be vigilant against the unintentional embedding of biases. Large Language Models (LLMs) offer remarkable capabilities; however, their training on vast datasets from the internet can reflect and amplify societal biases. This may negatively affect end-user satisfaction, user interface interactions, and team dynamics.

This hands-on workshop will arm Agile practitioners with essential strategies to audit their AI-enhanced toolchains for hidden biases. Participants will engage in practical, interactive exercises to:

• Diagnose points within their Agile workflows where AI tools are employed.
• Understand the origins and pathways of AI bias.
• Utilize state-of-the-art tools to uncover and address these biases.
• Foster productive cross-disciplinary dialogues that emphasize ethical product development.

Target Audience:

This workshop is tailored for Scrum Masters, Agile Coaches, Product Owners, Technical Leads, and Development Team Members who are committed to ethical AI integration within their practices.

Participants will leave with practical templates, checklists, and techniques they can immediately apply to make their Agile practices more bias-aware and responsible.

Learning Outcomes

Participants will exit the workshop equipped with actionable templates, checklists, and strategies to enhance their Agile methodologies with a keen awareness of bias.

They will:

• Map AI Bias within Agile Toolchains: Identify and document potential bias sources within their toolchains.

• Comprehend Bias Sources and Mechanisms: Gain insight into various AI bias types, including data, algorithmic, and human-induced biases, enhancing their ability to mitigate these in real-world scenarios.

• Apply Tools for Bias Detection: Explore and apply AI fairness tools and frameworks like Fairlearn and AI Fairness 360, along with demonstrations of industry-leading solutions like Google's AI Fairness toolkit.

• Enhance Cross-Functional Collaboration: Understand the importance of collaboration between developers, data scientists, ethicists, and business leaders in ensuring that products are developed and deployed ethically, and are aligned with organizational values and ethics guidelines.

Time Breakdown for 90-minute workshop

Introduction and Context (10 minutes)
• Brief overview of AI bias and its impact on society
• Opening exercise: Participants will work in small groups to map their current agile toolchain

Understanding AI Bias Sources and Propagation (20 minutes)
• How biases in training data can propagate through AI systems
• Group exercise: Identify and discuss real-world examples of AI bias in various industries

Low-tech and high-tech techniques for Bias Detection (30 minutes)
• Tool demonstration: Using multiple GPTs to detect bais in AI responses
• Tool demonstration of Open-source libraries: e.g., Fairlearn, AI Fairness 360
• Tool demonstration of Commercial tools: e.g., Google's AI Fairness, Microsoft's Fairness, Accountability, and Transparency (FAT)

Cross-Functional Collaboration for Ethical Product Development (20 minutes)
• Discussion on the importance of cross-functional collaboration when using AI-driven toolchains for product development. This outcome emphasizes the need for a holistic approach involving legal experts, data scientists, ethicists, and business leaders.
• Closing exercise: With knowledge gained, participants will work in small groups to revise their agile toolchain map

Q&A (10 minutes)
• Additional resources and next steps

Presentation History

https://speakerdeck.com/billagileinnovator

Agile 2024 - Technical Practices for Detecting Bias in AI: Building Fair and Ethical Models

Beer City Code - Technical Practices for Detecting Bias in AI: Building Fair and Ethical Models

BITCON2024 - Becoming a Super Learner - AI in Education

Product Discovery, Eleven Years On: From the Canvas to the Product Sprint

Back in November 2015 I wrote that building products customers actually want is really hard, and that most product efforts fail. I still believe that. What I've changed my mind about is what I thought the fix was.

That's the year I put out the Product Discovery Canvas, drawing on people like David Hussman, Steve Blank, Jeff Patton, Alberto Savoia, and Eric Ries. The idea behind it was simple: real learning comes from real users interacting with real things, not more meetings, not better decks. I told teams blanks were okay, that an empty box was just an honest picture of what you didn't know yet. And I pushed pretotyping hard, fake it before you make it, because making it was expensive.

Eleven years and a lot of client work later, I'd still defend every bit of that thinking. What's changed is the economics it was built on. Pretotyping existed because building the real thing meant months of engineering and real budgets on the line, so faking it was the smart move. Here's the argument I want to make: agentic AI hasn't made pretotyping obsolete, it's produced a new kind of pretotype. The old one avoided building the mechanism because the mechanism was expensive, a landing page or a human behind the curtain standing in for the real thing. The new one builds the mechanism, disposably, because an agent can now do it cheaply enough to throw away. Same instinct, compress the distance between idea and evidence, running on different material.

In this talk I'll go through what held up from the canvas, what I got wrong, and what we actually do now, a 6-Week Product Sprint that runs those two lanes of discovery, the old pretotype and the new one, through weeks of building with real users instead of a wall of sticky notes. I'll get into why a failed checkpoint in week two counts as a win, and why I think discovery didn't go away, it just stopped producing documents and started producing working software.

Target Audience
Product managers, product owners, engineering leads, startup founders, and agile practitioners who've used (or fought with) discovery frameworks like the Lean Canvas, Design Sprints, or story mapping, and want to know what still holds up now that AI Agents can build.

Duration
30 minute talk, including Q&A

Learning Outcomes
•Apply the enduring discovery principles (shared understanding, honoring the unknown, validating before building) regardless of tooling.

•Tell the two lanes of pretotyping apart: the classic kind (landing pages, paper prototypes, Wizard-of-Oz tests) for questions about desire and willingness to pay, and the new kind, a disposable but real AI-built version, for questions about whether the thing actually works.

•Structure a discovery effort as a phased, gated sprint (concept to working version to beta to launch-ready) with acceptance/behavioral tests defined up front.

•Reframe a failed validation checkpoint as a successful, cheap outcome rather than a failure to be hidden.

This closes out an 11-year, multi-part series on product discovery that started with the Product Discovery Canvas in 2015. It's reflective, not a product pitch, and works as an opening or closing session or a track talk for product/agile audiences.

Companion blog post: https://agileinnov.com/blog/product-discovery-eleven-years-on-from-the-canvas-to-the-sprint