Description Unit 5 Assignment: Case Study Analysis: AI-Enhanced Project Management System Design B ...
Description Unit 5 Assignment: Case Study Analysis: AI-Enhanced Project Management System Design Background For this summative assessment, you will design an AI-enhanced project management system for a fictional organization. This assessment will allow you to demonstrate your cumulative learning from Units 1–5, with a particular focus on AI-driven project management tools and analytics. The case scenario will describe an organization's project portfolio, current tools, and specific challenges they face in managing information systems projects. Your task is to design a comprehensive AI-enhanced project management system that addresses the following components: Requirements Analysis and Tool Selection Data Model Design AI-Assisted Decision Support Framework Dashboard and Reporting System Implementation and Ethical Considerations Purpose This assessment evaluates your ability to apply AI-driven project management concepts to solve real-world challenges. You will demonstrate your understanding of how artificial intelligence can enhance project management through improved data modeling, decision support, and visualization techniques. Associated Skills AI systems architecture and tool evaluation Data modeling and information flow design Strategic decision support and forecasting Interactive dashboard creation and reporting visualization Ethical reasoning in AI adoption Implementation planning and change management Case Scenario: TechNova Solutions TechNova Solutions is a mid-sized technology consulting firm specializing in custom software development, systems integration, and digital transformation services. The company has approximately 300 employees across five offices in North America and Europe, with a project portfolio of 25–35 concurrent projects ranging from small website developments to complex enterprise system implementations. Current Project Management Environment Multiple disconnected tools: Microsoft Project for scheduling, Excel spreadsheets for resource planning, JIRA for development tracking, and various financial systems Manual reporting processes requiring project managers to compile data from different sources Weekly status meetings and email updates as primary communication methods Resource conflicts and availability challenges across multiple projects Difficulty predicting project risks and making proactive interventions Limited visibility into cross-project dependencies and impacts Increasing complexity as the organization grows Organizational Goals Improve project delivery predictability and success rate Optimize resource utilization across the project portfolio Enhance decision-making with data-driven insights Reduce administrative burden on project managers Provide appropriate visibility to different stakeholder groups Scale project management capabilities as the organization grows Instructions Task Details As a consultant hired to design an AI-enhanced project management system for TechNova Solutions, you will create a comprehensive design document covering the following five components: 1. Requirements Analysis and Tool Selection (700-900 words) In this section a) Analyze TechNova's project management needs Assess the current environment's strengths and weaknesses Identify core requirements for the new system based on organizational goals Prioritize requirements based on business impact and implementation feasibility Consider organizational constraints and implementation challenges b) Evaluate and select appropriate project management tools with AI capabilities Research and compare at least three project management platforms that incorporate AI features Consider factors such as: Core project management functionality (scheduling, resource management, etc.) AI capabilities (predictive analytics, automated reporting, etc.) Integration capabilities with existing systems Scalability to support organizational growth User experience and adoption considerations Cost and implementation requirements c) Justify your selection based on organizational requirements Explain how your recommended solution aligns with TechNova's specific needs Discuss how the AI capabilities will address key challenges Address potential limitations and how they might be mitigated 2. Data Model Design (600-800 words) In this section: a) Develop a comprehensive data model for project information Design an integrated data model that supports both operational project management and analytical needs Identify key entities (projects, resources, tasks, etc.) and their relationships Define important attributes for each entity that will support AI analytics Describe how the data model will accommodate both structured and unstructured project data b) Create entity-relationship diagrams or data schemas Include visual representations of your data model (you may use diagrams, tables, or textual descriptions) Show how different data elements relate to each other Highlight how the model supports integration across different data sources c) Explain how the data model supports AI analytics capabilities Discuss how your data model enables specific AI applications (prediction, classification, optimization, etc.) Identify what historical data will be captured to train AI algorithms Explain how the model ensures data quality and consistency Address data normalization and dimensional modeling considerations for analytics 3. AI-Assisted Decision Support Framework (600-800 words) In this section: a) Design decision support workflows for key project management processes Identify three to four critical project management processes that would benefit from AI assistance Map out the workflow for each process, showing where and how AI would be incorporated Describe the inputs required, processing applied, and outputs generated Explain how the AI assistance improves decision quality or efficiency b) Specify AI algorithms and approaches for each decision area Recommend specific AI techniques for each identified decision support area Explain why these techniques are appropriate for the particular challenge Describe what these algorithms would do and how they would work Address data requirements and potential limitations c) Develop decision criteria and thresholds for automation vs. human intervention Define when AI should make autonomous decisions versus providing recommendations Establish confidence thresholds and escalation protocols Design appropriate human oversight mechanisms Balance efficiency gains from automation with risk management considerations 4. Dashboard and Reporting System (600-800 words) In this section: a) Design mockups for at least three role-specific dashboards Create conceptual designs for dashboards targeted at: Executive stakeholders (strategic view) Project managers (operational view) Team members (tactical view) Describe the layout, components, and information presented in each Explain how the designs address the specific needs of each user group b) Specify key performance indicators and visualization methods Identify the most important metrics for each stakeholder group Recommend appropriate visualization techniques for different types of information Explain how real-time, historical, and predictive data will be presented Design interactive elements to enable exploration and drill-down capabilities c) Explain AI-enhanced features for data interpretation and insight generation Describe how AI will enhance the dashboard beyond traditional reporting Include features such as: Anomaly detection and automated highlighting of issues Predictive indicators and forecasting Pattern recognition across projects Natural language generation for automated insights and summaries Personalization and adaptive displays based on user behavior 5. Implementation and Ethical Considerations (500-700 words) In this section: a) Develop an implementation plan with key milestones Outline a phased approach for implementing the AI-enhanced system Identify critical milestones and dependencies Address change management considerations for user adoption Discuss training and support requirements Consider data migration and integration challenges b) Address data privacy and security considerations Identify potential privacy concerns with AI-powered project management Recommend appropriate security measures Discuss compliance with relevant regulations (GDPR, CCPA, etc.) Design appropriate access controls and data governance c) Identify potential ethical issues and mitigation strategies Discuss ethical considerations specific to AI in project management, such as Algorithmic bias in project predictions or resource assignments Transparency and explainability of AI recommendations Potential workforce impacts and responsible AI use Balancing automation with human judgment Trust and accountability issues Propose specific strategies to address each identified ethical concern Submission Guidelines / Criteria for Success Your AI-Enhanced Project Management System Design should be formatted as follows Professional document with clear section headings and subheadings Executive summary (300–400 words) at the beginning, summarizing the key elements of your design Visuals where appropriate to illustrate concepts (data models, dashboard mockups, workflow diagrams) References to specific AI techniques, tools, or frameworks properly cited Professional terminology appropriate to both project management and AI domains Total length: 3,000–4,000 words (excluding title page, table of contents, and references)
