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The User-Centric Approach to Finding Success with AI Applications in PM

By Zabi Ulla – Product Head – A.I Labs, Subex

In product management, integrating artificial intelligence (AI) capabilities has become very important for product success. However, achieving success with AI applications requires a user-centric approach, where understanding user needs, empathy, and effective implementation play critical roles. In this article, we will explore the key aspects of leveraging AI in product management, from understanding the fundamentals of AI and machine learning (ML) to adopting a robust design framework and addressing critical considerations such as user empathy, data collection, mental models, and managing AI applications effectively.

Key Takeaways:

  • AI is the intelligence of a machine.
  • AI is a broader concept, while machine learning is a subset of it. 
  • Traditional software’s static rules may lead to false classifications and require frequent fine-tuning by domain experts. Machine learning models self-learn from data, continuously improving accuracy and reducing maintenance efforts.
  • AI can be applied to repetitive and boring tasks, and tasks suitable for augmentation.
  • There are 4 phases of design framework for AI applications- discovery phase, design phase, build phase and adopt phase.
  • User empathy lies at the heart of successful product design, especially when integrating artificial intelligence (AI) capabilities. 
  • Data collection serves as the foundation upon which successful machine-learning models are built. 
  • Certain things should be taken care of while using AI applications such as setting expectations for AI adoption, educational onboarding strategies and so on.
In this article
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    What is AI?

    In simple words-  AI is the intelligence of a machine. Just like humans have intelligence, machines can too, whether they’re hardware or software. In the industry, AI refers to computer systems that mimic human-like intelligence, such as understanding language, making decisions, and learning.

    In academia, AI is seen as a branch of computer science. It includes various domains within computer science, leveraging techniques and algorithms to create computer systems that mimic human intelligence.

    What is ML and How is it Different from Traditional Software?

    AI is a broader concept, while machine learning is a subset of it. Machine learning allows systems to learn from data. There are different types of learning methods, such as supervised, unsupervised, and reinforcement learning. Specific techniques like computer vision and natural language processing help AI systems recognize images, understand language and more. Machine learning includes a wide range of methods and techniques for systems to learn from data effectively.

    Traditional software operates based on pre-defined rules to solve specific problems. Users interact with the program, which generates outputs accordingly. In contrast, machine learning requires training a model with past data and desired outputs. For instance, training a machine learning model to identify fraud payment transactions involves providing it with past fraud and non-fraud data. Once trained, the model interacts with real-world data to identify frauds and non-frauds in new transactions.

    To understand better, let’s consider creating software for fraud detection. Traditional software would rely on rule-based engines, where predefined rules determine fraud and non-fraud transactions. However, machine learning involves training a model using past data to identify patterns and make predictions. When applied to real-world scenarios, machine learning models adapt and improve over time, reducing false positives and negatives.

    Traditional software’s static rules may lead to false classifications and require frequent fine-tuning by domain experts. On the other hand, machine learning models self-learn from data, continuously improving accuracy and reducing maintenance efforts.

    How are AI Products Different from Digital Products?

    When considering digital products and their evolution into AI products, it’s crucial to understand the distinctions and how machine learning plays an important role. Let’s take the example of a pizza app on your phone. You may notice a specific feature where machine learning is applied, such as generating targeted offers or identifying payment frauds. This application of machine learning enables critical features within the app, making it an AI product.

    On the other hand, when machine learning is applied to the core functionality of a digital product to solve focused problems, it becomes an integral part of the product. For instance, Google Search uses machine learning to recommend ads and promotions, making it a core part of their business model.

    Machine learning can also address broader problems within a digital product. Chatbots are a good example, where earlier versions were customized for specific businesses or purposes. 

    Understanding these distinctions is crucial because digital products today have the potential to evolve into AI products rapidly. Whether applying machine learning for specific features or broader purposes, the integration of AI transforms digital products, enhancing functionality and user experiences.

    How to Choose Where to Apply AI?

    When considering the application of AI to solve various problems, it’s essential to evaluate user tasks and determine where AI can be most beneficial. Here’s a guide to help you make informed decisions:

    1. Identify Repetitive and Boring Tasks

    Consider tasks that users find repetitive and boring. These tasks are ideal candidates for AI automation, as automation can streamline processes and improve efficiency for tasks that users find tedious.

    2. Assess Enjoyable and High-Value Tasks

    Tasks that users find enjoyable or are of high value should be handled with caution when considering AI applications. Users often want to retain control and accountability for these tasks, such as creating music or making important decisions. In such cases, AI may not be suitable.

    3. Evaluate Tasks Suitable for Augmentation

    Some tasks may be enjoyable but still acceptable for users to relinquish control. For example, recommending music or products on e-commerce websites. In these cases, AI can be applied in an augmentation fashion, where machine learning complements human decision-making, enhancing user experiences.

    4. Explore Greenfield Opportunities

    Identify tasks that humans cannot perform at scale, known as Greenfield opportunities. These tasks present in areas where AI products can generate significant value. Ola and Uber are great examples.

    What is the Success of AI Products?

    The success of any AI product depends on several critical factors. Let’s dive into what defines the success of AI products:

    1. Meeting User Needs

    Any successful AI product needs to fulfill user needs effectively. Understanding user requirements and designing products that address these needs such as streamlining tasks or providing personalized recommendations is essential.

    2. Facilitating User Adoption

    User adoption plays a crucial role in the success of AI products. Users should find the AI product intuitive and easy to use, with clear instructions and guidance. 

    3. Incorporating User Feedback

    User feedback is important for improving AI products over time. Users should feel empowered to provide feedback, whether through surveys, ratings, or direct communication channels. This enables developers to identify areas for improvement and refine AI algorithms accordingly.

    Design Framework for AI Applications

    Developing AI applications requires a good design framework that involves various phases, each crucial for success. To effectively implement the design framework, various tools come into play. These include user empathy for understanding user needs, guidelines for data collection and privacy, mental models for aligning user and system perspectives, error analysis for understanding AI behavior, and strategies for building trust with users.

    There are 4 important components of this framework:

    1. Discovery Phase

    The discovery phase marks the beginning of the design journey, focusing on understanding user needs and identifying opportunities for AI integration. It involves defining high-level problems, identifying user personas, and mapping user journeys to uncover pain points and opportunities. 

    2. Design Phase

    In the design phase, the focus shifts to determining specific tasks or actions suitable for AI automation or augmentation. It involves identifying areas where AI can add value, defining data requirements, and designing AI models to learn effectively from the available data. 

    3. Build Phase

    The build phase involves the technical implementation of the AI models designed in the previous phase. It involves gathering and preparing data, building AI models, and rigorously testing them to ensure alignment with design specifications. 

    4. Adopt Phase

    The adoption phase focuses on ensuring user acceptance and trust in the AI product. It involves leveraging tools such as user empathy, mental models, and error analysis to enhance user experience and build trust.

    User Empathy

    User empathy lies at the heart of successful product design, especially when integrating artificial intelligence (AI) capabilities. There are certain key steps involved in mastering user empathy:

    1. Understand Your Users

    The first step in user empathy is identifying and understanding your target users. Understand their behaviors, preferences, and pain points to gain insights into what matters most to them. By understanding your users, you can tailor the AI experience to cater to their specific needs.

    2. Define User Activities and Emotions

    Utilize tools like empathy maps to map out user activities and emotions associated with those activities. This step helps in defining the user journey and uncovering pain points and gains at each stage.

    3. Craft the To-Be Experience

    Based on the insights gathered, envision the ideal user experience without considering AI integration. Define what users should ideally experience at each stage of their journey, focusing on addressing pain points and delivering gains.

    4. Align User Needs with AI Capabilities

    Now, it’s time to align user needs with AI capabilities. Determine where AI can add value by automating tasks, providing human-centric interactions, or augmenting user experiences. Use a guideline or rule of thumb to identify which user needs can be fulfilled by applying AI and how AI should be applied to those tasks.

    5. Define AI Goals and Metrics:

    Once you’ve identified where AI can add value, define clear goals for AI to achieve for each task. Whether it’s recommending products or automating processes, define goals that are relevant, timely, and actionable. 

    6. Mitigate AI Risks

    Finally, mitigate risks associated with AI implementation. Consider potential pitfalls such as false positives, false negatives, fairness issues, and performance deterioration over time. Develop strategies to address these risks and ensure that AI continues to deliver value effectively.

    Data Collection

    Data collection serves as the foundation upon which successful machine-learning models are built. It’s essential to understand the intricacies of data collection to harness its full potential. Let’s explore the key steps involved in effective data collection.

    1. Understanding User Needs and Data Requirements

    Before diving into data collection, it’s important to comprehend user needs thoroughly. By identifying application areas where AI can be leveraged, you lay the groundwork for effective data collection. 

    2. Adhering to Regulatory Guidelines

    Regulatory and governance guidelines play a crucial role in data collection. Ensure compliance with industry-specific regulations and frameworks while collecting and managing data. Prioritize data privacy and security to maintain user trust and uphold ethical standards.

    3. Ensuring Data Completeness

    Complete data is important for training accurate machine learning models. Strive to gather comprehensive datasets that include all relevant data elements necessary for model training. Address any gaps or inconsistencies in the data to prevent biases and inaccuracies.

    4. Balancing Data Sources

    Balance internal and external data sources to ensure a diverse and representative dataset. Validate data sources to verify their accuracy and reliability. Avoid biases that may arise from skewed or incomplete data by sourcing data responsibly and ethically.

    5. Addressing Biases in Data Collection

    Mitigate biases by ensuring fairness and inclusivity in data collection. Consider demographic diversity and representation to prevent biased outcomes in AI models. Validate and audit data collection processes to identify and rectify any biases introduced during data labeling or curation.

    6. Responsible Data Labeling Practices

    Implement responsible data labeling practices to ensure the accuracy and reliability of labeled datasets. Select diverse and qualified labelers to minimize biases in data labeling. Monitor and evaluate labeling processes to maintain data quality and integrity.

    7. Leveraging Domain Expertise for Data Creation

    Harness domain expertise to create relevant variables and features in datasets. Supplement existing data with curated variables that enhance model performance and decision-making.

    Mental Model

    Mental models serve as the blueprint for both users and AI systems, shaping interactions and outcomes in the digital landscape. Mental models involve both the user’s perception of an application’s utility and the AI system’s approach to generating recommendations. 

    The key to a successful user experience lies in bridging the gap between user and system mental models. By aligning user expectations with system functionalities, creators can enhance usability and satisfaction.

    A user-centric framework involves addressing three fundamental questions: when to use the application, how to utilize its features, and what benefits it offers. 

    System mental models focus on how AI systems learn and make decisions based on data. Creators must strategize AI training methodologies, data requirements, and decision-making processes to ensure optimal performance. Understanding user behavior patterns and preferences is crucial for generating accurate recommendations.

    Achieving a balance between accuracy and reliability is essential in AI-driven applications. While machine learning algorithms strive to minimize false positives and false negatives, creators must implement backup strategies to mitigate potential errors and maintain user trust.

    Effective data collection is paramount for training AI models. Creators must curate diverse and representative datasets while adhering to regulatory guidelines and ethical standards. Leveraging domain expertise and responsible data labeling practices enhance the quality and integrity of training data.

    Creators must proactively identify and address biases in data collection, labeling, and decision-making processes. Diverse perspectives and validation mechanisms mitigate biases and promote equitable outcomes.

    Things to Take Care of while using AI Applications

    As AI and machine learning technologies continue to revolutionize digital experiences, it’s important for creators to educate users on AI models and empower them to provide valuable feedback for ongoing enhancements. Let us explore essential strategies to set the right expectations, onboard users effectively, and leverage feedback mechanisms for AI-driven applications.

    1. Setting Expectations for AI Adoption

    Transparency is key when introducing AI-driven features to users. Clearly communicate the capabilities and limitations of AI models, emphasizing their probabilistic nature and potential margin of errors. 

    2. Educational Onboarding Strategies

    Onboarding users to AI-powered applications requires a phased approach. Highlight the benefits of the product while providing upfront information on what the AI can and cannot do. Incorporate feedback mechanisms at each stage of onboarding to gather user input and address concerns promptly. 

    3. Implementing Effective Feedback Mechanisms

    Collecting feedback from users is essential for refining AI models and improving performance over time. Encourage users to provide both implicit and explicit feedback through designated channels within the application. 

    4. Transparent Communication Strategies

    Transparent communication is fundamental in building user trust and confidence in AI-driven applications. Use clear and concise messaging to explain how users can provide feedback and why it’s essential for improving the AI model. 

    5. Continuous Improvement and Iteration

    Embrace a culture of continuous improvement by iteratively refining AI models based on user feedback and usage data. Regularly evaluate AI performance metrics and iterate on the model to address evolving user needs and preferences. Engage users in the improvement process by soliciting feedback on new features and updates, ensuring alignment with user expectations and preferences.

    Hence, adopting a user-centric approach to AI applications in product management is crucial for achieving success in today’s evolving world of technology. By understanding user needs, leveraging AI capabilities effectively, and prioritizing user empathy and feedback, product managers can utilize the full potential of AI to drive innovation and efficiency in their products.

    About the Author:

    Zabi Ulla – Product Head – A.I Labs, Subex

    Frequently Asked Questions

    AI product management involves solving customer problems using data enabled by artificial intelligence and machine learning.

    In traditional product management, product behavior is usually binary and predetermined. AI product management deals with probabilistic outcomes.

    Challenges associated with AI product management include ambiguity of outcomes, difficulty in explaining the rationale behind the outcomes, addressing fairness and bias concerns, adapting to new infrastructure and tools, and selecting the right problems to solve with AI.

    It is highly unlikely that AI will fully replace product management. AI is used to assist product managers and enhance aspects of product management, like data analytics and customer support. But it cannot completely replace a product manager.

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