quizz busa3430.pdf

week 1 1. Product Development and Management Tools: o Viable Generative Analysis: Focuses on creating generative models for product ideas and market analysis. o Stability AI: Known for its work in developing stable and reliable AI system...

week 1 1. Product Development and Management Tools: o Viable Generative Analysis: Focuses on creating generative models for product ideas and market analysis. o Stability AI: Known for its work in developing stable and reliable AI systems. o AI21 Labs: Provides advanced language models for generating insights and product development. o GPT-4: Advanced language model by OpenAI used for various applications, including generating text and insights. Considerations: o Appropriateness: Assess if these tools can be used to generate innovative product ideas or improve product development processes. o Usage Restrictions: Determine who should have access to these tools, e.g., product managers, developers. o Management: Implement guidelines for the ethical use of generative AI to avoid misuse. o Risks: Be aware of potential misuse for generating misleading product information or proprietary data leaks. 2. Blog and Social Media Content Writing Tools: o Jasper: Generates blog and social media content using AI. o Notion AI: Enhances content creation and organization within Notion. o Phrasee: Creates optimized marketing copy using AI. o HubSpot Content Assistant: Assists in creating and managing content marketing strategies. Considerations: o Appropriateness: Evaluate if these tools can create engaging and relevant content for your target audience. o Usage Restrictions: Limit access to content creators and marketing teams. o Management: Establish content guidelines and review processes to ensure quality and accuracy.  o Risks: Risk of generating biased or inaccurate content, potentially damaging brand reputation. 3. Project Management and Operations Tools: o Wrike: AI-powered project management and collaboration tool. o ClickUp: Comprehensive task and project management with AI features. o monday.com: AI-driven project management and team collaboration. o Notion: Integrates AI for managing projects and workflows. Considerations: o Appropriateness: Determine if these tools can improve project management efficiency and team collaboration. o Usage Restrictions: Access should be restricted to project managers and team leaders. o Management: Set protocols for data handling and project oversight. o Risks: Potential risks include data security issues and over-reliance on AI for critical decision-making. 4. Graphic Design and Video Marketing Tools: o Diagram: Assists in creating diagrams and visual content using AI. o Synthesia: AI-driven video creation and synthetic media generation. o Lightricks: Provides AI tools for photo and video editing. o Rephrase.ai: Generates personalized video content using AI. Considerations: o Appropriateness: Assess if these tools can enhance visual and video content creation. o Usage Restrictions: Access should be limited to design and marketing teams. o Management: Implement guidelines for ethical use of AI-generated visual content. o Risks: Risk of misuse in creating deceptive or misleading visuals, potentially affecting brand trust. 5. Customer Support and Customer Service Tools: o Gridspace: AI-driven customer support solutions for analyzing customer interactions.  o UltimateGPT: Advanced AI for providing customer support and service automation. o Zendesk Advanced AI: Enhances Zendesk’s support capabilities with AI. o Forethought SupportGPT: Uses AI to improve customer service responses. Considerations: o Appropriateness: Determine if these tools can effectively handle customer inquiries and improve support quality. o Usage Restrictions: Restrict access to customer support representatives and service managers. o Management: Create guidelines for AI interactions to ensure customer satisfaction and accuracy. o Risks: Risks include misinterpretation of customer queries and potential loss of personal touch in support interactions. Your Review Should the company use the tool? Evaluate each tool’s potential benefits and alignment with company needs. Who should be allowed to use the tool? Limit access based on departmental needs and expertise to prevent misuse. Under which circumstances should the tool be used? Define clear use cases and scenarios where each tool can be effectively utilized. How its use is to be managed? Implement governance structures, guidelines, and oversight mechanisms. Are there any risks for using this tool? Identify risks such as misuse, security issues, and potential negative impacts on quality or reputation. By carefully considering these details, you can ensure the responsible and effective use of AI tools within your company. 4o mini Week 2 Part 1: Brief Week 1 Revision Recap of key concepts covered in Week 1. Part 2: Topic 2: AI in Business/Organizations  1. Importance of Human “Pattern Thinking” Skills for AI o Recognizing patterns to enhance decision-making and innovation. o Critical thinking and creativity as fundamental skills for business professionals. 2. Understanding AI for Business at Different Levels o Socio-Technical Perspective: Using the Information Systems framework to view AI as an integration of technology, data, processes, and people. o PACE Layers Framework: Analyzing the impact of AI changes at various levels, from rapid innovation (application layer) to foundational changes (infrastructure layer). o Porter’s Value Chain Model: Identifying how AI affects different business functions, such as inbound logistics, operations, marketing, and customer service. o Human-in-the-loop Framework: Emphasizing the role of human decision-makers in AI implementation and oversight. 3. Industry Insights into the Current Use of AI in Business o Real-world applications of AI and its implications for different industries. Key Concepts and Frameworks Socio-Technical View: o Technology: Infrastructure and tools (hardware/software). o Data: Management, quality, and ethics. o Processes: Organizational workflows. o People: Human capital and roles. o Value Propositions: Value creation for stakeholders. o Contexts: Organizational strategy and industry influences. PACE Layers Framework: o Application Layer: Rapid innovation in specific AI applications (e.g., chatbots, predictive analytics). o Commerce Layer: Evolution of business models and markets due to AI. o Infrastructure Layer: Slow-moving foundational changes affecting AI development. o Governance Layer: Policies and regulations addressing AI ethics and bias. o Culture Layer: Societal attitudes towards AI and its ethical implications. o Nature Layer: Environmental impacts and sustainability considerations in AI development.  Porter’s Value Chain: o Inbound Logistics: AI in inventory and supply chain management. o Operations: Automation and process optimization. o Outbound Logistics: Enhancements in delivery and distribution. o Marketing and Sales: Personalization and targeting through AI. o Service: Improving customer support and engagement with AI tools. o Technology Development: Accelerating innovation through AI. o Human Resource Management: AI in recruitment and performance evaluation. Tasks and Reflections Activity Examples: Discuss AI applications such as AI-supported job application screening and generative AI in teaching and learning. Pause and Reflect: Consider why these frameworks serve as “thinking tools” and identify any additional layers that might be relevant. Week 3 1. Foundation Concepts: Data, Information, Intelligence, Knowledge Data: Raw facts and figures without context. Example: "112" Information: Data that is processed or organized to have meaning. Example: "112" as an emergency call number. Intelligence: The ability to use information to make decisions. Example: Recognizing that "112" is used for emergencies. Knowledge: Understanding and insight gained from experience or learning. Example: Knowing how to respond when "112" is dialed. 2. Big Data: The 3Vs Volume: The amount of data. For instance, 300 billion emails exchanged daily. Velocity: The speed at which data is created and processed. Example: 400 hours of video uploaded to YouTube every minute. Variety: The different types of data. This includes: o Structured Data: Organized in databases, like spreadsheets. o Semi-structured Data: Partially organized, like XML files. o Unstructured Data: Not organized, like text documents or multimedia. Additional Characteristic:  Veracity: The trustworthiness and accuracy of data. Ensures the data is reliable and valid. 3. Data Quality (DQ) Key Attributes: Accuracy: Does the data represent reality correctly? Integrity: Are data structures and relationships consistent? Consistency: Are definitions and data elements uniform? Completeness: Is all necessary data present? Validity: Do values fall within acceptable ranges? Timeliness: Is data available when needed? Accessibility: Is data easy to access, understand, and use? Types of Data Quality Issues: Syntactic DQ: Related to data format and structure (e.g., incorrect date formats). Semantic DQ: Deals with the meaning of the data (e.g., different interpretations of terms). Pragmatic DQ: Concerns how data is used in context and for specific purposes (e.g., relevance for decision-making). Examples of DQ Issues: Data Matching: Combining data from different sources can lead to inconsistencies. Ethical Issues: Data matching may have unintended consequences, such as privacy concerns. 4. Business Analytics (BA) Definitions and Evolution: Business Analytics (BA): Involves applications and processes for analyzing data to support business decisions. Definitions have evolved to include practices like data governance and big data analytics. Types of Business Analytics: Descriptive Analytics: Summarizes past data (e.g., sales reports). Predictive Analytics: Uses past data to forecast future outcomes (e.g., sales forecasts). Prescriptive Analytics: Recommends actions based on data analysis (e.g., marketing strategies). Traditional BA Architecture: Data Source Systems: Includes internal databases and external data sources. Enterprise Data Warehouse/Data Marts: Centralized data storage.  End-user Analytics Tools: Tools for data analysis and reporting. 5. AI and Business Analytics Relationship: AI in BA: AI is often seen as a new phase in analytics (Analytics 4.0) and can enhance business analytics processes. BA in AI: Business analytics provides the foundation for AI by supplying the data and insights needed for AI systems. Caution: AI and BA can be misleading if not properly understood. AI should be seen as complementary to BA rather than a replacement. 6. Industry Practices AI Assurance Framework (NSW Government): Ensures that AI systems are ethical and align with human rights. Human Rights Impact Assessment Tool (Australian Human Rights Commissioner): Evaluates the impact of AI on human rights to avoid discrimination and harm. Week 4: AI-enabled Business Models Overview Agenda 1. Reminder of Quiz (Week 5) 2. Preparation for Assignment 1 o Walk-through o Q&A 3. Topic 4: AI-enabled Business Models (Business Model Canvas) Week 4 Overview At the end of this week, you will: Understand: Different elements of business models. Learn to Apply: Business Model Canvas (BMC) framework to analyze AI applications across business model components. Build Foundations: To start working on Assignment 1. Benefits of AI  Increased Efficiency Increased Productivity Improved Decision-Making Better Use of Employee Skills Empowerment of People Creation of New Products and Services Innovation Improved Safety and Wellbeing New Employment Opportunities Fairness and Equity Business Model Canvas (BMC) Purpose: o Strategic management tool o Business design and analysis tool o Innovation and knowledge-sharing tool o Simplifies business models to a single page Benefits: Concise Overview: Replaces lengthy plans with a one-page snapshot. Collaboration: Aligns teams and improves understanding. Iterative Design: Allows for testing and refining models. Visual Thinking: Stimulates creativity and sense-making. Simple and Intuitive: Easy to learn and apply. Components of the Business Model Canvas 1. Customer Segments: Identify target customers (demographics, needs). 2. Value Propositions: Define unique value offerings. 3. Channels: Determine how to reach and deliver value to customers. 4. Customer Relationships: Strategies for acquiring, retaining, and engaging customers.  5. Revenue Streams: Specify how the business generates income (sales, subscriptions). 6. Key Resources: List critical resources (tangible, intangible, human). 7. Key Activities: Outline essential tasks (production, marketing). 8. Key Partners: Identify external partners (suppliers, alliances). 9. Cost Structure: Detail fixed and variable costs (production, marketing). Case Study: Spotify Business Model Canvas 1. Customer Segments: o Free Users: Ad-supported. o Premium Subscribers: Paying for ad-free. o Advertisers: Providing ad services. 2. Value Propositions: o Free Users/Subscribers: Access to music library, personalized recommendations, convenience. o Advertisers: Effective advertising platform. 3. Channels: o Mobile App o Web Player 4. Customer Relationships: o Self-Service o Community Engagement o Support 5. Revenue Streams: o Advertisements o Premium Subscriptions 6. Key Resources: o Music Catalogue o Technology Infrastructure o Brand Reputation  7. Key Activities: o Content Licensing o IT Services o Algorithm Development o User Experience Enhancement o Partnership Management 8. Key Partners: o Record Labels o Device Manufacturers o Telecom Companies 9. Cost Structure: o Content Licensing Costs o Technology Maintenance o Marketing and Promotion AI Applications in Spotify: AI DJ: Curates personalized music selections. Discover Weekly: Personalized playlists based on user history. Spotify Wrapped: Personalized annual summaries of listening patterns. Recommendations and Playlists: Personalized based on listening habits. Content Cataloging and Search: Enhanced search and cataloging. Business Metrics and Optimization: Improved operational efficiency. AI-Assisted Music Creation: AI tools for generating music. Limitations of BMC Strategy: Not explicitly considered. Environment: Not included. Snapshot: Represents a business model at a specific time. Static Use: Often used as a static document rather than a living one.