528 Assessment

Intelligent Automation Team Challenge

Team Deliverable (40%)

Assessment Overview

A team project that focuses on designing and demonstrating an enterprise-oriented automation solution aimed at improving a reporting workflow, operational process, compliance activity, or business decision-support task. The automation may be demonstrated in a proof-of-concept state, including operation on a single desktop, the use of simulated or static data inputs, partial enterprise integration, and simplified dashboards or outputs. Teams should consider how the solution can be classified as "Intelligent Automation" through the application of Generative Artificial Intelligence, retrieval-augmented workflows, and/or Agentic AI systems involving coordinated AI agents. The presentation of the automation solution should focus on the technical goals, workflow design, process architecture (including clearly articulating the process using a process diagram), and practical limitations of the proposed solution. Teams are strongly encouraged to discuss project ideas with the instructor as early as possible to receive feedback regarding feasibility, scope, and technical expectations. Additional details and guidance will be provided on Canvas.

Required Deliverables

Deliverable Due Date Canvas Submission Portal
Intelligent Automation Team Challenge (Team, 40%, Presented in Class 20, materials due Tuesday June 2nd at 11:59PM) 6/2/2026 11:59PM Upload to Canvas (one submission per team)

Deliverable Details and Hints

Further details are provided below for each required deliverable.

Required deliverable: A software submission demonstrating an enterprise-oriented automation that applies at least one form of intelligent automation, including Generative AI, retrieval-augmented workflows, or Agentic AI principles.

  • The automation should simulate or address a realistic enterprise-level workflow or reporting problem. This can include financial reporting, compliance monitoring, operational analytics, reconciliations, document processing, management dashboards, forecasting, or workflow orchestration tasks.
  • The automation must be submitted as a working prototype capable of running on a single desktop environment. The project is not expected to be fully integrated into live enterprise systems but should clearly demonstrate the technical logic and structure of the proposed solution.
  • Teams must document which elements involve intelligent automation, especially where GenAI, retrieval systems, reasoning workflows, or agentic coordination mechanisms are incorporated.

  • Think about enterprise use cases such as reconciliations, report generation, anomaly detection, workflow routing, risk alerts, or document processing that could be enhanced using GenAI or agentic logic.
  • Agentic automation might involve multiple specialized agents working together, such as a planning agent, retrieval agent, validation agent, and reporting agent operating within a broader workflow.
  • If your automation uses an LLM, clearly explain how it is being used within the workflow (e.g., summarization, classification, extraction, orchestration, reasoning, or formatting). Include prompt examples or API logic where appropriate.

Required deliverable: A short presentation explaining the problem, the automation architecture, the intelligent automation features included, and the limitations or future extensions of the solution.

  • The presentation should include a clear process diagram showing the current manual workflow and how the automation addresses or modifies each stage of the process.
  • Present the automation objectives, major inputs and outputs, and how intelligent automation elements (e.g., GenAI, retrieval systems, agentic coordination, contextual adaptation) are integrated into the workflow.
  • Discuss limitations of the solution (e.g., data availability, LLM reliability, scalability, enterprise integration constraints) and identify which portions of the solution are fully operational, simulated, or conceptual.

  • Use screenshots, process walkthroughs, or short demo clips to demonstrate the automation in action rather than relying exclusively on presentation slides.
  • Keep the presentation focused on workflow improvement, process design, and the role of intelligent automation technologies. Be transparent regarding which elements are fully implemented versus conceptual extensions.
  • Use the process diagram to anchor the presentation. A strong workflow diagram often makes the technical architecture and scalability discussion substantially clearer.

Generative AI Policy

This policy outlines expectations for the responsible and ethical use of generative AI technologies, including large language models (LLMs) such as ChatGPT, in this course. These tools can significantly enhance learning, productivity, and creativity–but must be used transparently and professionally to support a respectful and effective learning environment.

Permitted Use:

Generative AI may be used to assist with idea generation, research, document drafting, programming, editing, and other academic work, provided the output is critically reviewed, refined, and understood by the student or team. Use of AI is encouraged when it enhances the learning process.

Student Responsibility:

Students are responsible for the accuracy, relevance, and integrity of any work submitted, including content influenced or generated by AI tools. Errors introduced by generative AI–factual, analytical, or interpretive–will be treated as student errors and may result in reduced grades.

Disclosure & Ethics:

Students may be asked to disclose when and how they used generative AI tools in individual or team assignments. In cases where the use of AI significantly contributes to the submission (e.g., coding assistance, text drafting), students should include a brief statement describing the use.

Unacceptable Use:

Submitting AI–generated content without understanding it, using AI to bypass individual learning (e.g., for comprehension–based quizzes or in–class polls), or allowing AI to make up sources or misrepresent work is a violation of course expectations and academic integrity.

This policy may be updated as the role of AI in education continues to evolve.