Artificial Intelligence Use Cases — PM Mental Model #3

6 min readMar 11, 2024

We have all heard the hype, Artificial Intelligence stands out as an incredibly transformative force poised to reshape industries economies, and societies worldwide. A report by PWC suggests that AI could contribute 45% of total economic gains amounting to 15.7 trillion to the global economy by 2030. As a product manager, you need to understand the various AI use cases that will drive these over the decade. In today’s blog, we have a mental model that will cover the main AI use cases that will drive the SaaS industry with examples of how they are already impacting company fortunes.

1. Automation

As the name suggests, automation leverages AI algorithms to streamline repetitive tasks, enhance efficiency, and reduce the need for manual intervention. Automation is a critical component in redefining customer onboarding processes seen as critical to a product-led growth strategy (see future blogs). AI-driven automation tools streamline setup procedures, and automate email comms ensuring a smooth onboarding experience for users.

A prime example is seen in HubSpot’s automation capabilities. HubSpot’s marketing and sales software employs AI-powered automation features to guide users through their onboarding journey. By analyzing user behavior and interactions, HubSpot’s automation tools deliver tailored messages and recommendations, enhancing user engagement and retention.

2. Personalization

Personalization tailors product experiences and content based on individual user preferences, behavior, and demographics, enhancing user engagement and satisfaction. AI algorithms use user data such as pat interactions, preferences, and demographic information to dynamically adjust product features content suggestions and pricing options.

Netflix employs advanced AI to personalize content recommendations for its subscribers. By analyzing viewing history, ratings, and interactions of not only the user in question but other users who have similar viewing habits Netflix’s recommendation system suggests movies and TV shows tailored to each users tastes, increasing engagement and retention. Nexflix have suggested that $1 Billion is saved and that 80% of the watched content on Netflix are influenced by the personalized recommendation system.

3. Categorization & Classification

Categorization and classification involve organizing and labeling data into distinct categories or classes, facilitating information retrieval and decision-making. These algorithms are employed to organize vast amounts of unstructured data such as customer queries, support tickets, large textual documents, news articles etc to categorize and tag data based on content and context, these algorithms enable efficient data retrieval, identifying the right people with the right skills to investigate queries and generally facilitate better decision-making without the need for humans to trawl through massive amounts of text much of which is not relevant.

Zendesk, for example, utilizes AI-powered categorization algorithms to streamline customer support. By analyzing support ticket content and context, Zendesk’s AI automatically tags tickets with relevant categories, prioritizes them based on urgency, and routes them to the appropriate support agents, improving response times and resolution and providing a superior customer experience.

4. Natural Language Processing (NLP)

NLP enables machines to understand and interpret human language automatically, facilitating tasks such as text analysis, sentiment analysis, language translation, and chatbot interactions.

A compelling example of NLP in sentiment analysis is showcased by Trustpilot, a leading customer review platform. Trustpilot employs advanced NLP algorithms to analyze the sentiment expressed in customer reviews. By automatically identifying key sentiment indicators such as positive, negative, or neutral language, Trustpilot provides businesses with actionable insights into customer satisfaction levels and sentiment trends allowing them to address problems, invest in popular areas of their product, or simply enhance their brand image.

5. Generative AI

Truly, the talk of the town just now when it comes to business innovation. Generative AI refers to a new generation of AI techniques that focus on the creation of new data rather than analyzing existing data. Generative AI models operate by learning the underlying structure and distribution of the input data (The internet for example) and then using that knowledge to generate new data, for example, text, music or image generation.

Many companies are now looking to Generative AI to provide them with a competitive advantage either by building their own or using existing models (known as Large language models LLM’s) like Open Ai’s Chat-GPT or Google BARD. Some companies are leveraging these LLM’s to automate content creation tasks such as writing blog posts, generating social media captions, crafting emails, or building sophisticated Chatbots. Google has integrated LLM models into many of its products and services for tasks such as language translation, auto-complete suggestions and improved search results.

6. Predictive Analytics

Predictive analytics utilizes historical data, statistical algorithms, and machine learning techniques to forecast future events, trends, or behavior enabling businesses to make informed decisions.

Companies often use predictive analytics to forecast customer churn, identify upselling opportunities, optimize pricing strategies, or fraud detection. By analyzing user behavior, usage patterns, and demographic information predictive analytics predicts future events, actions and preferences allowing companies to be proactive in reacting to the potential of these events causing issues.

Mixpanel offers predictive analytics tools for companies to forecast user engagement and retention. By analyzing user interactions and conversion metrics Mixpanels predictive analytics algorithms identify at risk users and can recommend personalization interventions to improve these rates, driving long term customer engagement and success.

7. Computer Vision

Computer vision enables machines to interpret and understand visual information from images or videos, mimicking human visual perception. It finds applications in object detection, image recognition, document processing, and video analysis.

Social Media sites like Instagram, Facebook, and TikTok use computer vision to automatically detect and moderate inappropriate content such as violence, nudity, and hate speech. Large cloud providers like Microsoft Azure provide API’s to allow any company to integrate the tech to their products. By leveraging AI-driven video analysis, businesses can ensure a safe and compliant environment for their users while mitigating risks associated with harmful content.

AI cannot be ignored, offering unprecedented opportunities for innovation, efficiency, user satisfaction, and engagement. By embracing AI-powered solutions product managers can drive product development, enhance user experiences, and stay ahead of the curve in today’s competitive market. AI is no longer expensive, many of the innovators in the area are now providing ease of use, fast cheap (ish) solutions to provide every business with the means to take advantage of the technology, unlock new possibilities, drive growth, and deliver value to their customers in transformative ways.