How to Build Purposeful, Impactful and Disease-Free AI Products with Radical Product Thinking – Part 1

Introduction

How can we build AI Products that are successful, impactful, and responsible? How can we avoid the common pitfalls and biases that plague many AI products? How can we ensure that our AI products are aligned with our vision, our values, and our users?

Building AI products is not easy. It requires a deep understanding of the problem, the user, the data, and the technology. It also requires a clear vision, a strong strategy, and disciplined execution. Many AI products fail because they lack one or more of these elements, or because they are not aligned with the market needs, the user expectations, or the ethical standards. They not only have technical complexity, but also human and ethical complexity. AI Products need to be reliable, accurate, fair, transparent, and trustworthy. AI products need to be aligned with the vision, the strategy, the prioritization, the execution, and the measurement of success.

In this two-part series, we will explore how to apply the principles and learnings from Radical Product Thinking to building AI products, both predictive and generative. We will cover the following topics:

- Part 1: The context and principles of Radical Product Thinking and how they apply to AI products.

- Part 2: The toolkit and examples of Radical Product Thinking and how to use them to design and develop a Generative AI Product.

What is Radical Product Thinking?

Radical Product Thinking is based on the premise that AI Products are not just solutions to problems, but expressions of our vision for the world. Products are not just features or functions, but stories that we tell our users and customers. AI Products are not just outputs of our work, but outcomes of our impact.

Radical Product Thinking is a methodology that helps product teams create AI Products that are visionary, strategic, and iterative. It can help them to define a clear and compelling vision of how the world should be with their AI product. It can help them to design a strategy that aligns with the user needs, the market opportunities, and the ethical implications. It can help them to prioritize the most important and impactful features and experiments. It can help them to measure the success and impact of their AI product, using metrics and feedback that reflect the value and the vision.

It helps AI Product teams avoid the Product Diseases that cause products to fail, such as feature-itis, pivot-itis, or obsession-itis. It helps AI Product teams define their product vision, their product strategy, their product roadmap, and their product experiments. It helps AI Product teams measure their progress, learn from their feedback, and iterate on their product.

AI Products have some unique characteristics and challenges that require some adaptations and considerations when applying Radical Product Thinking. Some of these are:

AI Products are data-dependent. AI Products rely on data to train, test, and improve their algorithms. Data is the fuel and the feedback for AI Products. Data quality, quantity, and diversity are critical for AI products. Data sources, collection, and processing are complex and costly for AI Products. Data privacy, security, and ethics are sensitive and important for AI Products.

AI Products are technology-intensive. AI Products use advanced and evolving technologies to create and deliver their value. Technology choices, architectures, and platforms are strategic and technical for AI products. Technology capabilities, limitations, and risks are significant and uncertain for AI products. Technology standards, regulations, and best practices are emerging and evolving for AI Products.

AI Products are user-centric. AI Products aim to solve problems, create value, or enhance experiences for users and customers. User needs, preferences, and behaviors are essential and variable for AI products. User inputs, outputs, and interactions are dynamic and diverse for AI products. User trust, satisfaction, and loyalty are challenging and crucial for AI Products.

Here are some of the key issues with AI Product Building that Radical Product Thinking can help address:

Chasing Shiny Objects vs. Solving Real Problems: There's so much hype and excitement around the latest AI breakthroughs that it's easy to get caught up in chasing the newest models or techniques without a clear tie-back to customer needs. Radical Product Thinking's emphasis on a vision-driven, customer-centric approach can help AI Product Leaders stay focused on solving real problems.

Lack of Strategic Clarity: AI can be applied to so many domains and problems that AI teams often suffer from a lack of focus or clear prioritization. Radical Product Thinking's guidance on translating a vision into a specific strategy with clear choices can help AI Product Leaders define a coherent roadmap.

Difficult to Measure Impact: Many AI teams struggle with knowing if their work is having a real impact. They may track improvements in model accuracy or performance but lose sight of actual customer or business outcomes. Radical Product Thinking's focus on measuring impact, not just outputs, is crucial for AI Products.

Siloed Teams and Lack of Domain Expertise: AI teams can become isolated from the rest of the organization, lacking the domain expertise to build effective solutions. Radical Product Thinking's approach of organizing teams around customer problems vs. technical skills and including domain experts directly in the product development process, can lead to better outcomes.

Ethical and Responsibility Concerns: As AI becomes more powerful and prevalent, there are increasing concerns about ethics, fairness, transparency, and responsible use of AI. While not addressed directly, Radical Product Thinking's principles around vision and strategy can help AI Product Leaders think through the broader implications and human impact of their work.

What are some signs of Product Diseases in AI Products?

"Product Diseases" in the context of AI Products, refer to common pitfalls or malpractices that can derail AI Product development from its intended purpose, impact, and vision. These diseases affect the health of the product lifecycle, leading to misaligned outcomes, wasted resources, and products that fail to deliver meaningful value to users and stakeholders. Here are some examples and their implications for AI Product Management:

Pivotitis: This disease occurs when a AI Product team constantly shifts focus from one idea to another, often in response to new technologies, market trends, or competitor actions, without making significant progress towards a meaningful, overarching goal. In the AI context, this could manifest as frequently changing the application or focus of an AI Business Model without allowing adequate time for each iteration to learn and improve based on user feedback or empirical evidence.

Strategic Swelling: This refers to the continuous expansion of an AI Product's scope and objectives without clear prioritization or alignment with the core vision. For AI Products, strategic swelling can happen when new features or capabilities are added without a clear understanding of how they contribute to the product's intended impact, leading to bloated solutions that are difficult to use, maintain, or scale.

Obsessive Sales Disorder: This disease occurs when short-term sales goals overshadow the AI Product's long-term vision and purpose, pushing the product to evolve based on what can be easily sold rather than what should be built to meet the user's real needs. In AI projects, this might lead to overpromising AI capabilities or implementing high-demand features that compromise the product's integrity or ethical standards.

Feature Factory: A condition where the focus is on continuously churning out new features at the expense of addressing fundamental problems or improving the core functionality. For AI Products, this could mean adding more AI-driven features without ensuring they are integrated in a way that enhances the user experience or without validating their effectiveness and impact.

Metrics Myopia: This disease is characterized by an overemphasis on quantifiable metrics such as user engagement or model accuracy, potentially at the expense of qualitative outcomes like user satisfaction, fairness, or ethical considerations. In AI, this might lead to optimizing models for performance on specific metrics without considering broader implications, such as bias or long-term user trust.

Narcissus Complex: A scenario where AI product teams become overly fascinated with their product's technology or capabilities, to the detriment of understanding and addressing the real needs of their users. This complex is named after Narcissus, a character from Greek mythology who fell in love with his own reflection, symbolizing self-admiration, and an inability to see beyond oneself. When AI Product teams are caught in the Narcissus Complex, they prioritize technological innovation for its own sake rather than focusing on how these innovations solve specific user problems or improve their lives. This can lead to products that are impressive in terms of technological achievements but fail to resonate with users, solve meaningful problems, or deliver value in practical, user-friendly ways.

The Narcissus Complex can manifest in several ways:

Innovation Without Purpose: Pursuing the latest technologies, features, or trends without a clear linkage to user needs or product strategy.

Complexity Over Usability: Creating products that are technically complex or feature-rich but difficult for the average user to understand or benefit from.

Ignoring User Feedback: Focusing on the product's technology rather than the feedback from actual users, leading to a disconnect between what the product does and what users need.

Digital Pollution: AI Products, in an effort to constantly engage users and drive growth metrics, can end up bombarding people with endless notifications, emails, recommendations, and features that ultimately create more noise and distraction than value. This "digital pollution" can lead to user frustration, disengagement, and even a sense of addiction or manipulation. For AI products specifically, Digital Pollution Product Disease can manifest as:

Overwhelming Recommendations: AI-powered recommendation engines and personalization features can sometimes create an endless stream of content or product suggestions that feel more overwhelming than helpful.

Lack of User Control: Some AI Products make decisions or take actions on behalf of users without sufficient transparency or user control, leading to a sense of lost autonomy.

Attention-Grabbing Notifications: AI Systems designed to maximize engagement can end up sending a barrage of notifications and prompts that distract and interrupt users.

Creepy or Invasive Experiences: Poorly designed AI features can sometimes feel creepy or invasive, like the product is spying on you or knows too much.

Algorithmic Bias and Filter Bubbles: AI Algorithms can sometimes reinforce biases or create "filter bubbles" that limit users' exposure to diverse content and perspectives.

How do you apply Radical Product Thinking to AI Products?

Radical Product Thinking proposes a five-step framework to create products that are visionary, strategic, and iterative. The five steps are:

Step 1: Define your AI Product vision. Your product vision is your statement of how the world should be, not how it is. It is your aspiration, your purpose, your why. Your product vision should be clear, concise, and compelling. It should inspire your team, your users, and your stakeholders.

Step 2: Define your AI Product strategy. Your product strategy is your plan of how to achieve your product vision. It is your approach, your direction, your how. Your product strategy should be specific, measurable, and actionable. It should guide your decisions, your priorities, and your trade-offs.

Step 3: Define your AI Product roadmap. Your product roadmap is your sequence of steps to execute your product strategy. It is your roadmap, your timeline, your when. Your product roadmap should be realistic, flexible, and transparent. It should communicate your goals, your milestones, and your dependencies.

Step 4: Define your AI Product experiments. Your product experiments are your tests to validate your product strategy. They are your experiments, your hypotheses, your what. Your product experiments should be lean, focused, and data-driven. They should generate your insights, your learnings, and your feedback.

Step 5: Iterate on your AI Product. Your product iteration is your process of improving your product based on your feedback. It is your iteration, your adaptation, your so what. Your product iteration should be fast, frequent, and consistent. It should incorporate your feedback, your changes, and your results.

These five steps are not linear, but cyclical. They are not one-time, but ongoing. They are not independent, but interdependent. They form a loop that product teams should follow to create products that are visionary, strategic, and iterative.

?Conclusion

Radical Product Thinking offers several principles and practices that can help address the key challenges facing AI product development today. By combining a bold vision with a focused strategy, cross-functional collaboration, and a relentless focus on customer impact, AI Product Leaders can tackle these issues head-on and build AI Products that make a transformational difference. The key is to not get caught up in the hype but stay grounded in solving real problems for real people.