AI for Business: #2 AI Use-cases (50 Practical Applications)

AI Disruption Across Industries

Introduction to AI Applications

• AI is transforming various industries similarly to how electricity did in the past, with applications ranging from self-driving cars to fraud detection and generative AI.

• This episode presents over 50 AI use cases across different domains, aimed at providing a non-technical introduction for those interested in applying AI in their work.

Overview of Learning Objectives

• The focus of this episode is on understanding AI use cases and applications, building on the previous introduction to machine learning concepts.

• A recommended resource for exploring critical use cases across sectors like supply chain, healthcare, and education is ai-cases.com.

Understanding Patterns in AI Use Cases

• Identifying common patterns underlying various AI use cases is more beneficial than memorizing specific examples; there are eight major patterns discussed.

• The eight major patterns include prediction, clustering, anomaly detection, computer vision, natural language understanding, voice analytics, action optimization, and content generation (generative AI).

Exploring Prediction as an AI Pattern

Importance of Prediction in Decision Making

• The first pattern explored is prediction; machine learning models can make informed decisions based on historical data.

• Machine learning can predict future outcomes when trained with historical input-output data.

Examples of Predictive Analytics

• Personalized recommendations are a versatile application where AI analyzes user behavior to suggest tailored options across platforms like Amazon and Netflix.

Industry-Specific Use Cases

1. Retail:

• Marketing teams can enhance ROI through hyper-targeted promotions by predicting customer engagement with offers.

• Operations teams utilize demand forecasting for automating replenishment per store based on predicted product demand.

2. Supply Chain:

• Machine learning aids in predicting delivery times and optimizing logistics networks based on forecasted demand.

3. Manufacturing:

• Predictive maintenance uses machine learning to foresee potential machinery failures by analyzing sensor readings.

4. Banking:

AI Applications in Various Sectors

Personalized Policy Pricing in Insurance

• Machine learning can forecast risk for applicants based on multiple factors, allowing banks to assign risk scores and make informed decisions.

• Personalized policy pricing is a significant use case, enhancing the accuracy of insurance offerings tailored to individual candidates.

Predictive Analytics in Healthcare

• AI applications include precision medicine, where treatments are recommended based on genetic history and medical records.

• Another application is predicting hospital readmissions, which helps improve resource management and patient care by estimating the likelihood of patients being readmitted.

Clustering Techniques

• Clustering identifies natural groupings within data, useful for customer micro-segmentation based on behaviors or characteristics.

• Insights from clustering can inform marketing strategies and product development while also aiding in fake news detection by categorizing articles based on content similarities.

Anomaly Detection Across Industries

• Anomaly detection focuses on identifying outliers that deviate from expected patterns; it's particularly effective when labeled data is scarce.

• In finance, it detects unusual spending patterns and potential fraud, enhancing security within financial systems.

• In healthcare, it reduces medication errors by flagging conflicting prescriptions and identifying rare conditions or symptoms.

Computer Vision Capabilities

• Computer vision enables AI systems to interpret visual information with high accuracy, facilitating automation across various tasks involving large datasets.

• Use cases include quality inspection in manufacturing to detect defects and diagnosis support in medical imaging for early disease detection.

Natural Language Understanding Applications

• Natural language understanding allows AI systems to analyze unstructured text from diverse sources like social media and customer support tickets.

Automation in Document Processing

Enhancements through Automation

• Automation replaces manual data entry, allowing for the processing of thousands of documents in minutes. This leads to increased productivity, reduced costs, and fewer errors.

• Similar automation techniques can be applied across various sectors including claims processing in insurance, customer onboarding in banking, and document verification in government.

AI Applications in Healthcare and Sentiment Analysis

Leveraging AI for Insights

• AI can analyze social media posts and news articles to gauge customer sentiment regarding products or services.

• In healthcare, AI processes forms and patient records while also predicting supply chain disruptions by mining diverse data sources.

Advancements in Chatbots and Customer Interaction

Smart Chatbot Integration

• AI-powered chatbots recognize speech and text, understand intent, and respond human-like. They can be integrated into websites, online stores, social media channels, and mobile apps.

• These chatbots reduce the burden on customer support teams while enhancing overall customer satisfaction.

Natural Language Processing Capabilities

Understanding Human Language

• Natural language processing (NLP) encompasses tasks such as machine translation, named entity recognition, text summarization, part-of-speech tagging, dependency parsing, topic modeling, and question answering.

Impact of Large Language Models

Advancements with GPT Models

• Recent advancements in large language models like GPT-4 have significantly improved AI systems' understanding capabilities when dealing with textual data.

• Businesses can utilize APIs from companies like OpenAI or open-source models like Falcon 4TB or Llama 2 to incorporate powerful NLP interfaces without building them from scratch.

Speech Understanding: A New Frontier

Voice-Based Interactions

• The sixth pattern focuses on spoken language comprehension by AI systems which allows for meaningful insights extraction from voice interactions.

• Voice-based chatbots interact with customers verbally while smart call centers leverage this technology for real-time analysis of call data.

Applications of Speech Understanding

Enhancing Customer Experience

• Voice-to-text transcription is utilized in healthcare for clinical documentation by transcribing physician-patient conversations.

• Virtual home assistants use speech understanding to perform tasks such as playing music or controlling smart devices; banks may use voice recognition for identity verification instead of traditional methods.

Action Optimization Through Analytics

Types of Analytics Explained

• Action optimization aims to identify optimal decisions across various situations. It includes three major types:

• Descriptive analytics: Analyzes past events (e.g., sales).

• Predictive analytics: Forecast future events (e.g., sales volumes).

• Prescriptive analytics: Determines optimal actions based on specific conditions.

Reinforcement Learning's Role in Business Decisions

Continuous Improvement Strategies

• Reinforcement learning differs from traditional methods by using trial-and-error approaches to maximize a reward function (e.g., click-through rates).

Real-Life Use Cases Across Industries

Practical Applications of Action Optimization

• Various industries are deploying action optimization strategies including:

• Logistics optimization for supply chains,

• Trajectory planning for autonomous vehicles,

• Production scheduling optimization,

• Portfolio management,

• Driver allocation for ride-sharing companies.

Recap of Key AI Patterns Discussed

Summary of Seven AI Patterns

Exploring AI Transformations in Industries

Major AI Use Cases

• Mid Journey, Stable Diffusion, and other technologies are significantly transforming various industries.

• The discussion will cover major AI use case patterns, including:

• Chatting with enterprise data

• AI agents

• Natural language-driven experiences

• The episode aims to unravel the potential of AI in these contexts.