With the booming growth of AI in product management, AI product managers (AI PMs) are likely to face a lot of challenges in implementing AI. Today, major internet companies are embracing the AI revolution, while conventional businesses continue to grapple with digital transformation. Project managers, product managers, executives, and entrepreneurs leading this new era are all contemplating the same question: What obstacles will project and product management encounter in the age of AI? Teams focused on artificial intelligence are responsible for product development. To clarify our terminology, when we mention "AI product management," we are referring to the creation of products powered by AI.
The management of AI products encounters several challenges. Most product managers concentrate on AI-driven applications rather than the underlying infrastructure. Risks related to technical feasibility arise from the probabilistic nature of generative AI and issues with training data. Usability risks involve the need to clarify what AI can do and to build trust in its capabilities. Value risks necessitate the delivery of concrete value. Additionally, business continuity risks are heightened due to legal, ethical, and economic considerations. AI product managers must possess a comprehensive understanding of various elements and a strong literacy in AI to effectively tackle these challenges.
Risks in AI-driven Products
A crucial distinction exists in prioritizing AI-driven applications over the foundational AI infrastructure used in model training. This distinction mirrors the difference between platform products and experience products. Platform products facilitate the development of experience products. Both categories are important, yet most AI product managers will focus on experience products—applications—so our emphasis here will be on these.
Many products come with considerable risks, and product teams are typically cross-functional, possessing a range of skills to mitigate these risks. Few products underscore the pressing need for strong product management as much as AI products do. An AI product utilizes AI technologies to deliver problem-solving experiences for customers or businesses. The term "AI" includes both traditional AI (such as machine learning) and generative AI. These technologies support features like intelligent recommendations, personalized experiences, or marketplace matching. Examples of AI applications include smart home devices that utilize voice and natural language processing, fraud detection systems, and advanced generative AI capabilities such as content creation, summarization, and synthesis.
Solution
AI products introduce distinct challenges regarding risk. Product managers, designers, and technical leads must work closely together to devise effective solutions. Although AI product managers may not have machine learning scientists as integral team members—particularly in application-centric environments—they must seek the advice of such experts. This collaboration is essential for effectively harnessing the underlying AI technologies.
Technical Feasibility Risks
Generative AI operates on a probabilistic basis rather than a deterministic one. Unlike traditional solutions that consistently deliver the same outputs for the same inputs, generative AI systems can analyze billions of inputs, with model weights adapting through learning, which may result in varying outputs over time. Some products and features are well-suited to probabilistic solutions, while others are not. This distinction is crucial. For example, occasional discrepancies in a personalized news feed might be tolerable, but a product that manages insulin dosage must strictly follow medical protocols.
AI product managers need to ensure that the technology is in line with the product’s objectives. This brings forth essential quality assurance inquiries: What is the permissible error rate? What kinds of errors could arise? How will the product address each type of error? Can user experience design help reduce errors?
The quality of training data is critical. Product managers should have a thorough understanding of the data and the model training methodologies. All extensive datasets come with inherent biases and limitations. While the ethical ramifications of data bias will be examined in the context of business continuity risks, AI product managers must comprehend how these challenges appear in the final product. For many AI products currently available, the primary obstacle is the training data itself—whether it is sufficient in quantity or quality to enable feasible commercial solutions.
Solution
When considering technical feasibility, AI product managers must work closely with technical leads and machine learning experts to make the best trade-offs. For instance, high-precision models may necessitate significant investments in training data, processing power, and computational resources, which can affect user experience, scalability, and costs. Additionally, technical debt and infrastructure must be evaluated: Does the organization have the necessary infrastructure to support AI products? Aspects such as data storage, processing capabilities, and ongoing maintenance expenses are important. Excessive technical debt can impede scalability, technical feasibility, and business continuity.
Risks in Usability
User experience is essential for any product, but it becomes more complex with AI. In the case of AI products, UX design must make clear what the technology is capable of and what it cannot achieve, as well as how the product functions on a conceptual level. Transparency fosters trust and reduces frustration when limitations are encountered. Historically, product managers have depended significantly on designers to build user trust. However, the introduction of AI brings additional constraints and complexities, many of which arise from probabilistic outputs.
Solution
Users and customers require reassurance regarding data usage and AI functionalities, which may necessitate innovative interaction paradigms. Designers and AI product managers need to collaborate closely to ensure that AI experiences are intuitive, reliable, and easy to comprehend. In certain applications, it becomes vital to explain the reasoning behind AI decisions. This level of transparency enhances confidence. What degree of explanation is required to establish trust? Similar to feasibility evaluations, product managers and designers must assess the trade-offs that impact UX.
For instance, a highly accurate AI recommendation system may lead to slower response times, causing user frustration, while a more straightforward model might compromise accuracy for quicker responses. Striking a balance between accuracy, speed, operational costs, and user experience is crucial.
Risk Due to Value
Value continues to be a fundamental risk. AI products offer significant value, leading to widespread global adoption. However, many available today are simply driven by buzzwords. The main responsibility of the AI product manager is to guarantee that AI-enhanced features provide real, incremental value by solving genuine problems more effectively than current solutions or tackling challenges that were previously unsolvable. It is essential to avoid using AI as a marketing ploy or for competitive advantage. The value of the product must be evident and persuasive.
Solution
Similar to intricate features, evaluating value necessitates the integration of quantitative data (such as A/B testing) with qualitative feedback (like user testing). Working closely with product marketing is crucial for effectively communicating value. Marketing initiatives must consider user privacy and the ethical use of data, ensuring these aspects are clearly articulated when necessary.
Business Continuity Risk
Despite the potential of AI to provide value, challenges related to business continuity are considerable, and errors in this domain frequently make headlines. For any product, it is crucial to ensure effective marketing, sales, service, financing, monetization, legal compliance, and adherence to regulations. These risks are even more pronounced for AI products.
From the perspective of unit economics, AI products are still in their early stages but come with high costs. Furthermore, ongoing concerns regarding data sourcing, copyright issues, bias, and the implications of probabilistic recommendations remain. Companies continue to face challenges related to legal liabilities and their consequences. Ethical considerations are becoming increasingly pressing—beyond data bias, what are the legal and moral consequences if users misinterpret results or if models generate harmful "hallucinations"?
Solution
Probabilistic AI systems have the potential to save lives through enhanced accuracy or to jeopardize them through mistakes. It is imperative for companies to proactively tackle these challenges.AI product managers must also be vigilant about the potential for misuse by malicious actors. Safeguarding company assets and reputation is integral to maintaining business continuity. Depending on the specific use cases, AI products may have broader societal or environmental effects. Product managers need to evaluate these risks and work closely with legal teams to protect both customers and the organization.
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