How to Build AI-Driven Products?
- product management
- 5 min read
By Malavika Lakireddy – Vice President, Zeotap
Artificial Intelligence (AI) is no longer an area confined to tech experts and scientists; it has permeated everyday conversations and industries, promising transformative potential across sectors.
Artificial Intelligence (AI) is revolutionizing industries by providing unprecedented capabilities in data analysis, pattern recognition, and decision-making. From basic regression models to advanced deep learning algorithms, AI technologies are transforming how businesses operate and interact with their customers.
Understanding the intricacies of AI—from its foundational principles to its cutting-edge applications—is essential to grasp its potential and navigate its impact on our society.
But what exactly is AI, how has it evolved to impact our lives and businesses, and how can we build AI products that stand the test of time? Let us explore this blog to gain more insight into these topics.
Key Takeaways:
- AI evolution progresses from Narrow Intelligence (ANI) with specialized task capabilities, towards General Intelligence (AGI), aiming at human-like cognitive abilities, and anticipates the possibility of Artificial Super Intelligence (ASI) surpassing human capabilities.
- Discriminative AI classifies input data into predefined categories based on learned patterns, whereas Generative AI creates new, original data outputs not present in the training data.
- The AI tech stack comprises layers including infrastructure, pre-trained models, domain-specific models, MLOps tools, and applications, facilitating the development and deployment of AI solutions.
- AI capabilities include classifying, predicting, verifying, translating, generating, and summarizing data.
- Building machine learning models involves starting with user interface design, progressing through application layers interfacing with ML models, leveraging a mixture of experts for tasks, and ensuring a data layer for quality and standardization.
What is AI?
Artificial Intelligence (AI) is a comprehensive scientific field beyond traditional machine learning (ML) and algorithms, aiming to replicate human intelligence in machines.
Exploring the layers of AI reveals a hierarchy: starting with simple regression models as the entry point, progressing to various ML models tailored for specific tasks, advancing further to deep learning for complex tasks like natural language processing and image recognition, and culminating in Generative AI (Genera VI) models that drive creativity by generating diverse content and art.
Each AI model serves unique purposes across industries. Regression models enable basic predictive analytics, ML models power recommendation engines, pattern recognition, deep learning excels in complex tasks, and Generative AI leads innovation with creative outputs.
Understanding this AI landscape empowers organizations to leverage its transformative potential, driving efficiencies and unlocking new possibilities across sectors through intelligent automation and innovative creativity.
Evolution of AI
The evolution of Artificial Intelligence (AI) unfolds in distinct stages, each representing a significant leap in technological capability and potential.
1. Artificial Narrow Intelligence (ANI)
The journey begins with Artificial Narrow Intelligence (ANI), where models excel at specific tasks, delivering targeted outcomes. Examples include predicting optimal prices, forecasting demand, or offering personalized product recommendations to online users. ANI focuses on narrow applications, leveraging data for precise decision-making.
2. Artificial General Intelligence (AGI)
Advancing beyond ANI, the industry comes closer to Artificial General Intelligence (AGI). Although not fully realized, AGI promises broader cognitive abilities similar to human intelligence. This transition marks a crucial phase where AI systems exhibit more comprehensive understanding and problem-solving capabilities.
3. Artificial Super Intelligence (ASI)
Looking forward, the talk about Artificial Super Intelligence (ASI) is becoming more significant. ASI imagines machines with superior awareness and abilities that go beyond what humans can achieve. This idea pushes the limits of what we thought was possible with AI, envisioning machines that can tackle tasks that are currently beyond human capabilities.
Discriminative AI
Discriminative AI revolves around generating specific outputs based on prior data input. This model excels in classifying information or predicting probabilities, leveraging historical data to make informed decisions.
Common applications include:
- Email classification (spam vs. non-spam)
- Gender identification in images
- Predicting customer behavior (e.g., purchasing, site engagement, churn probability, product affinity)
In traditional programming, rules dictate specific outcomes based on input. In contrast, machine learning (ML) trains models with data and desired outcomes, allowing the AI to discern patterns and generate its own rules for future predictions. AI’s effectiveness hinges on quality data. ML models learn from datasets to make accurate predictions, emphasizing the critical role of data in developing robust AI solutions.
Understanding Discriminative AI is foundational for developing AI products that leverage data-driven insights effectively. Harnessing AI’s potential requires a comprehensive grasp of its principles and applications.
Generative AI
Generative AI represents a groundbreaking advancement that has transformed the landscape of artificial intelligence. Unlike traditional models, Generative AI has the unique capability to produce outputs that are entirely distinct from the input, opening up a realm of new possibilities and applications.
Generative AI extends far beyond text generation, encompassing a wide range of outputs:
- Images
- Videos
- Audio
- Music Composition
- Synthetic data creation
- Design and code generation
Enterprises leverage Generative AI for various applications:
- Creating new music compositions
- Generating synthetic data for testing and development
- Designing and automating code processes
Leading companies like Alibaba are pushing boundaries with Generative AI. For instance, Alibaba utilizes models like the “EMO model” to produce entire videos from images, aligning emotions with voice to create lifelike experiences. Generative AI empowers businesses to innovate and enhance user experiences.
Differences between Discriminative and Generative AI
There are 5 key differences between discriminative and generative models of AI:
Parameter | Discriminative AI | Generative AI |
Objective | To classify input data into predefined categories or make specific decisions based on similarity to training data | To generate new, original data based on learned patterns or prompts |
Training Approach | Trained to learn the decision boundary between different classes of data | Trained to learn the underlying distribution of data to generate new samples |
Type of Learning | Supervised learning where models are trained on labeled data. | Often involves unsupervised or semi-supervised learning, focusing on generating new data samples |
Data Generation | Does not generate new data; makes decisions based on existing patterns in the data | Actively generates new data based on learned patterns or given prompts |
Decision Boundary | Learns a clear boundary between different classes of data, optimizing for accuracy in classification tasks | Does not rely on a fixed decision boundary; generates outputs without strict classification limits |
Differences between Conventional Software, Discriminative AI, and Generative AI
Let’s explore how conventional software, discriminative AI, and generative AI are different from each other.
Conventional SW | Discriminative AI | Generative AI | |
Description | Human-crafted rules are coded into software to process data based on predefined instructions. | Learns from input-output pairs to discern patterns and make decisions based on learned rules. | Advanced models trained on diverse datasets to generate nuanced responses to input data. |
Pros | – Explicit control over rules | – Learns from examples – Adaptable | – Reliant on specific training data – May struggle with novel scenarios |
Cons | – Limited adaptability and scalability | – Handles diverse tasks – Learns nuanced patterns | – Requires extensive computing resources – Potential for biased outputs |
Differences between AI Products and AI-Driven Products
It’s essential to distinguish between AI products and AI-driven products. These two categories play distinct roles in shaping user experiences and business outcomes.
AI Products | AI-Driven Products |
Services or infrastructure components that offer pre-built machine learning models as a service. | Products that utilize AI services to enhance user experiences and drive specific use cases. |
AI products serve as foundational tools or services. | AI-driven products leverage AI services to deliver enhanced experiences. |
They provide access to pre-built machine learning models. | They use AI to drive features, recommendations, and decision-making. |
Developers and businesses integrate these AI models into their applications. | Product managers play a crucial role in understanding and integrating AI capabilities into these products. |
Examples include ChatGPT, Google AI, Pinecone, and Longchain. | Examples include Netflix, Alexa, Grammarly, Amazon |
AI Tech Stack
It’s crucial to comprehend the AI tech stack—the layered structure of technologies that power AI solutions. Let’s delve into the components of this stack to gain insights into how the industry is progressing.
1. Application Layer
At the top of the AI tech stack, we have the application layer. This is where AI models are utilized to deliver specific use cases and applications across various industries. From recommendation engines in e-commerce to personalized healthcare solutions, AI-driven applications abound in today’s market. Real-world applications include E-commerce recommendation systems, personalized healthcare diagnostics, and autonomous driving technologies.
2. Enablers (MLOps Tools)
Facilitating the AI lifecycle are the enablers—tools and platforms that streamline MLOps (Machine Learning Operations). These include low-code/no-code platforms, compliance tools, and other utilities crucial for deploying and managing AI models efficiently.
Key Enablers include MLOps platforms, compliance tools, and low-code/no-code development platforms.
3. Domain-Specific Models
Domain-specific models represent a pivotal area of opportunity within the AI stack. While cloud providers offer generic models, specialized models tailored to specific industries or use cases are becoming increasingly essential. For instance, Google has introduced healthcare-focused AI models, addressing the need for highly accurate results in specialized fields. Emerging Specializations include healthcare AI models and industry-specific AI solutions.
4. Foundation Models
The next layer includes the foundation models—pre-trained AI models developed by cloud providers using extensive datasets. These models serve as the building blocks for various AI applications, catering to diverse output types. We’re now witnessing a shift towards multimodal models capable of handling different inputs and outputs. Key players here are Google Cloud AI Platform, AWS AI services, and Microsoft Azure AI.
5. Infrastructure Layer
At the foundation of the AI tech stack is the infrastructure layer, provided by cloud service providers like Google Cloud, AWS (Amazon Web Services), and Microsoft Azure. These providers furnish the essential computing hardware and resources needed to run AI models efficiently.
AI Product Lifecycle
The AI product lifecycle has 4 main stages- Identify, Build, Launch, and Improve. Let’s explore each of these stages.
1. Identify
Begin by researching market needs and gathering insights from stakeholders to identify high-impact use cases suitable for AI implementation. Prioritize use cases aligned with business goals and feasibility for AI development.
2. Build
Collect and prepare relevant datasets, then build and train AI models using machine learning algorithms. Test and validate models to ensure accuracy and reliability before moving to the deployment phase.
3. Launch
Integrate AI models into existing systems, provide user training and support, and monitor system performance post-launch. Gather initial feedback to optimize the solution for real-world use.
4. Improve
Continuously fine-tune AI models based on performance metrics and user feedback. Adapt models to changing data patterns and user behaviors through iterative development to ensure long-term effectiveness and alignment with evolving business needs.
AI Capabilities
AI (Artificial Intelligence) capabilities include a diverse range of functionalities that empower machines to perform complex tasks. Let’s delve into the key AI capabilities:
1. Classifying
AI excels in classifying data by categorizing it into distinct groups or “buckets.” This process can involve sorting data into multiple categories based on specific criteria, enabling efficient data organization and retrieval.
2. Predicting
AI’s predictive abilities enable it to forecast the likelihood of events or outcomes. By analyzing historical data and patterns, AI models can predict future trends, behaviors, or probabilities with a high degree of accuracy.
3. Verifying
AI can verify or compare information by assessing similarities or differences between datasets. This capability is vital for tasks such as data validation, fraud detection, and quality assurance across various domains.
4. Translating
One of AI’s transformative capabilities is language translation. Modern AI systems can seamlessly translate text or speech from one language to another, achieving natural language fluency and improving translation quality significantly.
5. Generating
AI’s generative capability involves creating new content autonomously based on learned patterns and data. This transformative capability allows AI systems to generate text, images, or music, mimicking human creativity.
6. Summarizing
AI’s summarization capability involves distilling large volumes of data into concise and meaningful summaries. Generative AI, in particular, can extract essential points and insights from extensive datasets, facilitating efficient data analysis and decision-making.
Identifying Use cases
When choosing between discriminative and generative AI models, it’s essential to consider their strengths and tradeoffs for different use cases:
1. Discriminative AI Use Cases
This is ideal for precise predictions and classifications based on existing data patterns. Discriminative AI excels with limited data variety and prioritizes accuracy. Some use cases include:
-
- Image Recognition: Accurately classifying images into predefined categories.
- Sentiment Analysis: Analyzing text to determine sentiment with high accuracy.
- Fraud Detection: Identifying anomalies in financial transactions.
- Medical Diagnosis: Diagnosing diseases based on patient data.
2. Generative AI Use Cases
Suited for tasks requiring creativity and content generation. Generative AI is innovative but may produce unrealistic outputs (hallucinations) and require diverse datasets. Some use cases include:
-
- Text Generation: Creating human-like text, such as articles or dialogues.
- Image Synthesis: Generating realistic images based on descriptions.
- Music Composition: Creating original music compositions.
- Data Augmentation: Generating synthetic data for training.
Different Use Cases of AI
AI (Artificial Intelligence) is transforming various industries with its versatile applications. Let’s explore key use cases where generative AI is making a significant impact:
1. Code Generation
Generative AI powers innovative tools like GitHub Copilot, which generates code based on prompts and context. This capability streamlines software development by assisting developers in writing code more efficiently.
2. Insight Generation from Tabular Data
AI can generate insights from tabular data using prompts, enabling the extraction of SQL queries to fetch data from backend systems. This code generation capability enhances data visualization and analysis.
3. Text Generation
Tools like Grammarly leverage generative AI to enhance writing by providing grammar and style suggestions. Companies like Jasper use AI for marketing content generation, and maintaining brand voice and tone.
4. Chatbots and Text-Based Interactions
Chatbots use text generation to interact with users by generating responses based on predefined knowledge bases. This AI capability improves customer support and engagement.
5. Image Generation
AI-powered apps like Artbreeder and Adobe Firefly generate realistic images based on user preferences. Design tools like Figma leverage image generation to assist in creating visual content efficiently.
6. Text-to-Audio Conversion
AI technologies like Murf AI convert text into lifelike audio for voiceovers, enhancing content creation and accessibility. Tools like Google Meet use AI for real-time language translation, enabling seamless communication.
7. AI in Classification and Healthcare
Generative AI is also valuable in classification tasks, including fraud detection and healthcare imaging analysis. Its ability to handle large volumes of unstructured data makes it indispensable for complex use cases in these domains.
Determine KPIs and Success Criteria
Determining Key Performance Indicators (KPIs) and success criteria upfront is crucial for ensuring that AI initiatives align with business objectives and deliver meaningful outcomes. Let’s break down the importance of KPIs into two main categories: model KPIs and business KPIs.
1. Model KPIs
Model KPIs focus on assessing the performance and accuracy of AI models during development and deployment.
These metrics include:
- Response Rate: Measuring the rate at which the AI system responds to queries or inputs.
- Accuracy: Evaluating the overall correctness of predictions made by the AI model.
- Precision and Recall: Assessing the model’s ability to retrieve relevant information accurately.
- Error Rate: Calculating the frequency of errors or inaccuracies in model predictions.
It’s essential to measure these metrics independently to ensure the effectiveness and reliability of the AI model.
2. Business KPIs
Business KPIs measure the direct impact of AI solutions on business outcomes and user experiences.
Examples of business KPIs include:
- Reduction in User Effort: Determining how AI reduces the time and effort required to complete tasks.
- Reduction in Personnel Needed: Assessing the impact of AI on resource allocation and workforce efficiency.
- Self-Service Improvement Rate: Monitoring the adoption and effectiveness of self-service AI solutions.
- Clickthrough Rate (CTR) or Conversion Rate: Measuring the effectiveness of AI-driven recommendations or interactions.
- Revenue Uplift: Evaluating the direct impact of AI implementations on revenue generation and business growth.
Combining model KPIs with business KPIs provides a holistic view of AI performance and its contribution to achieving strategic business objectives.
The required model accuracy varies based on specific use cases and objectives. For intent inference in chatbots, an accuracy of 85% may be sufficient for production deployment.
Higher accuracy (95-98%) is essential for tasks like customer segmentation using AI-driven prompts. Accuracy requirements should align with the criticality and impact of AI outputs on business operations and user experiences.
Building ML Models
Let us discuss the foundational elements of building machine learning (ML) models.
1. User Interface as a Starting Point
The journey of building ML models begins with the user interface (UI), emphasizing user experience (UX) principles. This phase sets the tone for understanding user needs and expectations.
2. Application Layer (App Layer)
Sitting atop the UI is the application layer, where the interaction with machine learning occurs. This layer often interfaces with ML models and facilitates data flow.
3. ML Layer
The ML layer is pivotal, processing tasks and data to generate meaningful outcomes. There is a prevalence of a “mixture of experts,” an ensemble of models breaking down tasks into manageable subtasks.
4. Data Layer: Foundation of ML Models
The data layer serves as the bedrock for ML models. Ensuring data quality and standardization is paramount for the success of AI applications.
5. Trends: Mixture of Experts
The speaker introduces the concept of a “mixture of experts,” highlighting its efficacy in leveraging diverse models to accomplish complex tasks efficiently.
6. Model Selection
Choosing between out-of-the-box and self-hosted models depends on specific project requirements.
7. RAG (Retrieve and Generate): Enhancing Model Output
RAG enables models to incorporate external data sources, enriching outputs beyond training data.
8. User Feedback Loop
Post-output, user feedback becomes crucial. Evaluating user satisfaction and understanding the alignment of model outputs with user expectations is imperative.
9. Data Quality and Standardization
There is a critical role of data quality and standardization in optimizing AI applications.
Improving AI Output Quality
The following are the main ways to improve AI output quality:
1. Investing in Robust Data Collection
The foremost strategy for improving AI quality is to invest in robust data collection. This involves gathering extensive data related to the specific use case. Data can be augmented with synthetic or derived data to enhance quality.
2. Optimizing Data Utilization
Considerations such as the recency and volume of data play a critical role. For instance, e-commerce data may require less historical data to account for seasonality compared to financial services.
3. Role of Product Managers (PMs)
Product Managers are urged to become intimately familiar with data, understanding its context and nuances in detail. This knowledge is crucial for effective AI product management.
Build Vs Buy Trade-Offs
To better understand the trade-offs between building custom AI models and using pre-existing solutions, let’s examine key differences across various dimensions:
Parameter | Buy | Build |
Initial Investment | Low initial investment required; primarily involves API usage fees. | Higher initial investment due to resource allocation for model development and infrastructure. |
Time to Market | Rapid deployment for prototyping and validation; shorter time to market. | Longer development cycle for model creation, training, and testing; delayed time to market. |
Flexibility | Limited flexibility to fine-tune with proprietary datasets; constrained by model capabilities. | High flexibility to tailor models to specific use cases; complete control over model design and output. |
Data Privacy | Relies on public datasets; may raise privacy concerns for sensitive data. | Enables use of proprietary datasets; mitigates privacy risks associated with public data usage. |
Regulatory Compliance | Suitable for less regulated industries; may not comply with stringent privacy regulations. | Ideal for industries with strict regulations; allows adherence to data protection and compliance norms. |
Resource Requirements | Requires minimal AI expertise and infrastructure; leverages external model providers. | Demands advanced AI capabilities and dedicated resources; necessitates an in-house model development team. |
Cost Considerations | Cost-effective for short-term experimentation and validation; primarily API usage fees. | Higher long-term costs are associated with development, maintenance, and infrastructure management. |
Prompt Engineering
Prompt engineering is a strategic approach to refining AI behavior and optimizing output quality. Here are key practices that enhance prompt engineering:
1. Persona Definition
Assigning a persona (e.g., marketer, engineer) guides AI responses to specific roles or contexts, ensuring relevant and accurate outputs.
2. Detailed Instructions
Clear and comprehensive instructions clarify desired outcomes, enabling the AI to interpret and generate accurate outputs.
3. Output Guardrails
Establishing boundaries and criteria for acceptable outputs prevents the AI from generating irrelevant or inaccurate results, maintaining quality standards.
4. Including Examples
Incorporating specific examples within prompts clarifies expected output formats, helping the AI generate relevant and contextually accurate responses.
What is Retrieval Augmented Generation (RAG)?
Retrieval augmented generation (RAG) involves augmenting a base model with external data to improve output quality and accuracy. This approach mitigates hallucination (generating false information) and ensures richer outputs, especially when the base model lacks the necessary data.
RAG serves as a preventive measure against hallucination within AI models. For instance, when querying current events like the prime minister of Pakistan post-election, the AI may lack updated data or provide inaccurate information. RAG allows validation through open web queries to verify and enhance the accuracy of generated responses.
In practical scenarios, RAG is instrumental in refining AI responses, particularly for dynamic or evolving topics. By integrating external data sources seamlessly, RAG empowers AI models to deliver informed and reliable outputs, improving overall user experience and trust in AI-generated content.
Putting Human in the Loop
For enterprise clients requiring high accuracy, relying solely on automated AI (such as Geni) may not be advisable. Instead, a human-in-the-loop strategy is recommended. Output from AI models is presented to end-users for validation before final use. Workflows or segments are auto-created but not enabled automatically, allowing users to review, edit, and make necessary changes.
In email marketing automation, AI generates email content and subject lines, but users have the final say before scheduling. This approach ensures user control and mitigates liability for AI-generated outputs. Implementing feedback mechanisms like thumbs-up or thumbs-down gathers user input on AI model outputs. Specific feedback helps refine AI behaviors based on user expectations.
Privacy and Compliance
With the emergence of new regulations worldwide, such as the EU’s AI Regulation Act, organizations face increasing scrutiny and guidelines for utilizing AI technologies. These regulations provide frameworks for assessing AI applications based on risk levels and compliance requirements.
1. Risk-Based Approach
A risk-based approach categorizes AI use cases into minimal risk, limited risk, high risk, and unacceptable risk categories. This framework guides organizations in determining appropriate compliance measures and disclosures based on the nature and impact of AI applications.
2. Compliance Requirements
For AI implementations, compliance requirements extend to data collection, model optimization, and user interactions. Organizations must incorporate disclosures, opt-outs, and anonymization practices to uphold user privacy and regulatory standards.
3. Mitigating Liability
Implementing clear declarations regarding AI accuracy and limitations helps mitigate potential legal liabilities. By setting transparent expectations, organizations protect themselves from liabilities arising from AI-generated outputs.
4. Human-Centric Approaches
Incorporating user validation and control within AI workflows empowers users to verify and adjust AI-generated outputs before final deployment. This human-in-the-loop approach ensures accountability and fosters trust in AI technologies.
5. Addressing Bias and Misinformation
Addressing bias and misinformation requires robust data governance practices and proactive measures to ensure fair and accurate AI outcomes. Collaborative efforts between legal teams and AI developers are essential to navigating legal complexities and upholding ethical AI practices.
Optimizing AI Product Launches
Launching an AI product necessitates a strategic approach, particularly with AB testing. This method involves selectively exposing the AI model or its results to a subset of users while maintaining the standard experience for others.
AB testing is essential to objectively measure the impact of AI on business outcomes. By comparing AI-driven experiences with standard ones, businesses can assess improvements in efficiency, user experience, and key performance indicators.
This controlled rollout mitigates risks associated with unproven AI models and fosters data-driven decision-making.
Beta Launches and Early Adopter Program
Launching enterprise AI products requires strategic planning, including beta testing and early adopter programs. Prioritize internal testing with key stakeholders before a wider release to gather valuable feedback and optimize the product.
At Zootap, internal user testing proved instrumental in refining product positioning and optimizing features before the external launch.
To overcome adoption challenges, focus on maximizing product usage rather than immediate monetization. Offer free trial periods or incentives to encourage widespread adoption and gather crucial data for refining AI models.
Proactively Collect User Feedback
Collecting user feedback proactively is vital for optimizing product performance and ensuring user satisfaction. Experts emphasize the importance of integrating robust product analytics from the outset to avoid post-launch data gaps.
Incorporating in-app feedback mechanisms like chatbots or in-app forms allows for real-time insights during specific user interactions. Additionally, conducting user interviews—potentially incentivized—provides valuable qualitative feedback on user perspectives.
For enterprise-focused initiatives, engaging closely with beta clients during initial launches fosters direct feedback loops, driving collaborative product enhancements.
The key to effective feedback utilization is the continuous cycle of implementation and evaluation.
Sustaining AI Initiatives Post-Launch
The work doesn’t end with a product launch—ongoing support and improvement are essential for AI initiatives to succeed.
Allocate extended support from engineering and data science teams beyond the initial build phase to derive maximum utility from the AI solution.
Transitioning from beta to general availability typically takes one to two quarters, followed by additional optimization phases to address feedback and meet quality targets.
Timely improvements are crucial to prevent user disillusionment and maintain product adoption. Continuous refinement based on feedback ensures a robust, user-friendly solution aligned with business objectives.
Strategies for Adapting AI Challenges
In the face of AI performance issues, implementing effective fallback strategies is crucial:
1. Heuristic Rules and Simple Settings
Utilize fallback heuristic rules or settings to ensure continuity when AI models underperform. Even major B2C products integrate these fallbacks alongside advanced features like recommendation engines.
2. User Options to Bypass AI
Offer users the choice to bypass AI layers, especially in consumer-facing products. This empowers users and alleviates frustration caused by AI performance fluctuations.
3. Adaptation to Model Changes
Embrace the dynamic nature of AI models. Continuously refine and adapt models based on evolving data and user behaviors to maintain optimal performance and user satisfaction.
Adapting AI Models to Evolving Data
Maintaining the effectiveness of AI models over time requires a keen understanding of how data changes can impact performance. Consider a scenario where a credit scoring model initially performed well but then began to deteriorate after six months. Upon investigation, it was discovered that the client had expanded their customer base to include students, significantly altering customer behavior patterns. This change rendered the original model assumptions and relationships obsolete, necessitating a reevaluation and refinement of the model to accommodate the new customer segment.
Such challenges are common, especially with factors like geographic expansion or seasonality affecting data dynamics. Ongoing performance monitoring is essential to detect and address these shifts
Artificial general intelligence (AGI) might become a reality within the next five years. While the feasibility of AGI is within reach, the actual deployment may be subject to regulatory considerations, akin to the restrictions on human cloning.
Furthermore, advancements in AI include self-training and unsupervised models that learn autonomously from data, representing the cutting edge of AI development.
Essential Skills for AI Product Managers
Traditional product management blends business context, system thinking, and user experience focus. However, managing AI products requires additional layers of understanding, particularly in the realm of AI capabilities, limitations, and lifecycle management.
AI product managers must possess a unique blend of core product management skills, coupled with a deep comprehension of AI technologies. This includes:
1. Business Context and System Thinking: Like traditional product managers, AI product managers need a robust understanding of business outcomes and user-centric design. They must codify business needs into tech products while ensuring intuitive user experiences.
2. AI Capabilities and Limitations: Understanding AI capabilities is critical. AI can perform various tasks, but translating these capabilities into practical use cases requires a nuanced understanding of what AI can and cannot achieve.
3. AI Product Lifecycle Management: Managing AI products involves navigating complex considerations, such as resource requirements, legal implications, optimization strategies, and ongoing performance monitoring. This lifecycle management is essential for ensuring sustained product success.
Additionally, familiarity with different AI capabilities, even without coding expertise, can be advantageous. Knowing how to leverage AI models for specific tasks, while being aware of their limitations and potential biases, is crucial.
AI, once confined to tech experts and scientists, has now permeated everyday conversations and industries. Everyone seems familiar with AI’s existence and potential applications. Despite this widespread awareness, the actual number of individuals building AI products remains relatively low, considering the immense potential for future adoption.
Key areas of AI adoption include marketing and sales, software engineering, customer operations, and R&D in healthcare, education, manufacturing, and supply chain.
The call to action is clear: integrate AI into existing products or create new AI-driven solutions to enhance speed, efficiency, and user experience.
Ultimately, the democratization of AI requires more builders—individuals willing to explore AI’s potential and utilize its transformative power. Let’s embark on this journey to unlock AI’s value and drive innovation across diverse domains.
About the Author:
Malavika Lakireddy – Vice President, Zeotap
Frequently Asked Questions
Products that utilize AI services to enhance user experiences and drive specific use cases. Netflix and Grammarly are common examples.
Discriminative AI revolves around generating specific outputs based on prior data input. This model excels in classifying information or predicting probabilities, leveraging historical data to make informed decisions.
Common applications include:
- Email classification (spam vs. non-spam)
- Gender identification in images
- Predicting customer behavior (e.g., purchasing, site engagement, churn probability, product affinity)
AI tech stack is the layered structure of technologies that power AI solutions. It includes the application layer, enablers, domain-specific models, foundation models, and application layers.
The capabilities of AI systems include classifying, predicting, verifying, translating, generating, and summarizing.
Prompt engineering is a strategic approach to refining AI behavior and optimizing output quality. Here are key practices that enhance prompt engineering: Persona definition, detailed instructions, output guardrails, and including examples.
Retrieval augmented generation (RAG) involves augmenting a base model with external data to improve output quality and accuracy. This approach mitigates hallucination (generating false information) and ensures richer outputs, especially when the base model lacks the necessary data.
Traditional product management blends business context, system thinking, and user experience focus. However, managing AI products requires additional layers of understanding, particularly in the realm of AI capabilities, limitations, and lifecycle management.
AI product managers must possess a unique blend of core product management skills, coupled with a deep comprehension of AI technologies.
To build an AI data product, follow these steps:
- Identify Use Cases: Research market needs and stakeholder insights to prioritize impactful AI applications aligned with business goals.
- Collect & Prepare Data: Gather relevant datasets ensuring quality and diversity for training AI models.
- Build & Train AI Models: Utilize machine learning algorithms to develop models tailored for specific tasks, such as regression, ML, deep learning, or generative AI depending on complexity.
- Validate & Test: Ensure the accuracy and reliability of AI models through rigorous testing and validation against benchmarks.
- Integrate & Deploy: Integrate trained models into existing systems and deploy AI-driven features.
- Monitor & Improve: Continuously monitor model performance, gather user feedback, and iteratively improve AI solutions to adapt to evolving needs and data patterns.
A good AI product is characterized by the following factors:
- Effective problem-solving aligned with market needs.
- Relies on high-quality, diverse datasets.
- Uses accurate, reliable AI models validated against benchmarks.
- Enhances user experience through intuitive interactions.
- Seamlessly integrates and scales within existing systems.
- Embraces continuous improvement based on performance metrics and user feedback.
- Adheres to ethical standards in AI development.
- Demonstrates measurable business impact.
The time required to develop an AI product can vary significantly based on complexity, scope, available resources, and specific requirements. However, a typical timeline might involve the following stages:
- Identify Use Cases: Weeks to months for market research and stakeholder input.
- Data Collection & Preparation: Several weeks to months to gather and prepare datasets.
- Model Development & Training: Weeks to months depending on algorithm complexity and data volume.
- Validation & Testing: Several weeks to ensure model accuracy.
- Integration & Deployment: Weeks to months for system integration and deployment.
- Monitoring & Iteration: Ongoing process spanning months to years for optimization based on feedback.
The AI product lifecycle involves 4 stages:
- Identify: Research market needs, gather insights, and prioritize use cases aligned with business goals.
- Build: Collect and prepare datasets, develop and train AI models, then test and validate for accuracy.
- Launch: Integrate AI into systems, provide user training and support, monitor performance, and gather feedback.
- Improve: Continuously fine-tune models based on metrics and user feedback to adapt to evolving needs and ensure long-term effectiveness.
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