Syllabus

Course Overview

Course Objective

Advisory services increasingly focus on delivering measurable business value through innovative, technology-enabled solutions. As regulatory requirements grow more complex and data volumes expand, firms must identify ways to improve decision-making, reduce costs, and scale processes without compromising quality or integrity. This course examines how emerging technologies particularly automation, generative AI, and data analytics can be deployed to meet these challenges. These tools are reshaping professional practice, offering new ways to design, test, and deliver solutions that would have been unthinkable a decade ago. Students will adopt an innovation mindset, grounded in the realities of business constraints, and develop practical skills by designing, analyzing, and deploying intelligent automation tools. Whether building bots, evaluating AI-assisted workflows, or analyzing the feasibility of new technologies, students will gain experience with the kinds of problems and possibilities that define the next generation of advisory work. I look forward to exploring these ideas with you, and to seeing what kinds of solutions you'll create. Asher

Course introduction

Advisory services help organizations solve complex problems with smart, scalable solutions. In this course, we will explore how automation, generative AI, and emerging technologies are transforming the way professional service firms deliver value. You'll gain hands-on experience with advanced tools and learn how to use them not just to innovate, but to improve performance, reduce costs, and think strategically. I look forward to working through this cutting-edge material with you!

Curriculum

Advanced Cases in Accounting Advisory (ACCTG 528) is a required course in the MPAcc concentrating on the study of the innovation mindset by studying the skills and tools to advise clients on potential business solutions enabled by emerging technology. Students will engage their questioning mindset to critically examine the role of emerging technology for various business problems and enhance their adaptability and resilience capabilities by engaging with challenging advanced software packages to build business solutions. Students will work in teams and communicate their findings to a professional audience. This course builds heavily from the analytic mindset and data and analytic skills in Data Analytics for Professional Accountants (ACCTG 522) and works with the material focusing on business and control developed in Accounting, Audit and Assurance Regulation (ACCTG 520), Advanced Cases in Assurance Services (ACCTG 521) and Advanced Financial Statement Analysis (ACCTG 523).

Assessment

Assessment in this course is focused on providing you with feedback on how well you can undertake and communicate automation and scale-driven projects in accounting-centric advisory settings. You will be assessed on both written and verbal communication as well as the ability to effectively work in your teams and as an individual. A summary of the components of the deliverables used to determine your grade for ACCTG 528 are below, detail for each assessment follows:

Learning Objectives

• Students will be able to propose and defend opportunities for the use of emerging technologies in data analytics and automation By evaluating robotic process automation solutions. By evaluating Generative AI solutions.
• Students will be able to explain how technology can facilitate a combination of digital and physical solutions to complex advisory issues By evaluating Generative AI and other technologies to solve business problems. By evaluating the use of emerging technology to solve business problems.
• Students will be able to build and present a proof-of-concept agentic automation solution to a business problem By researching and evaluating processes fit for intelligent automation. By implementing automation using RPA software. By implementing agentic frameworks using LLMs within an automation solution.

Faculty Info

Grading Policies and Assignments

Course Calendar

Course Policies

Archived instructional materials

Key terms

• Automation: The use of technology to perform tasks with reduced human intervention, aiming to improve efficiency, accuracy, and consistency across business processes.
• Robotic Process Automation: A form of automation technology that uses software robots or 'bots' to mimic human actions for rule-based, repetitive tasks in digital systems such as entering data or processing transactions.
• Business Process Model and Notation (BPMN) Diagram: A standardized graphical notation used to model and visualize business processes, enabling clear communication and analysis of workflow structures.
• Desktop Automation: Automation tools installed on individual user desktops that execute tasks within a specific workstation environment, often tailored to personal workflows or department-level needs.
• Enterprise-Wide Automation: A strategic approach to automation that spans departments and functions across an organization, typically involving scalable platforms that integrate with enterprise systems like ERP or CRM.
• Human-in-the-loop: A design pattern where human judgment is embedded in automated workflows to oversee, approve, or intervene in processes, ensuring accountability, accuracy, and ethical decision-making.
• Digital Worker: An AI- or rule-based software agent capable of performing end-to-end tasks traditionally done by humans, often across multiple systems or applications.
• Process Mining: A data-driven technique used to analyze and visualize business processes based on event logs, helping organizations identify inefficiencies and automation opportunities.
• Low-Code / No-Code: Development platforms that allow users to build applications or automations with minimal or no programming, enabling business users to create solutions without relying solely on IT.
• Intelligent Automation: The integration of artificial intelligence (e.g., natural language processing, machine learning) with traditional automation to enable decision-making, adaptability, and improved process performance.
• Bot Lifecycle Management: The governance and oversight of RPA bots across their lifecycle, including development, testing, deployment, monitoring, and retirement, ensuring operational stability and compliance.
• Artificial Intelligence: The broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
• Generative AI: A subset of artificial intelligence that focuses on systems capable of generating new content such as text, images, audio, or code by learning patterns from training data and producing original outputs.
• Agentic AI: AI systems designed to operate with a degree of autonomy, persistence, and goal-orientation, often executing sequences of actions independently and adapting to changes in their environment or instructions.
• Fully Autonomous AI: An advanced form of AI capable of performing tasks and making decisions entirely without human intervention, including the ability to initiate, monitor, and complete actions in dynamic or uncertain environments.
• Order-to-Cash (O2C): A core business process that encompasses all activities from receiving a customer order to collecting payment. It typically includes order management, inventory tracking, invoicing, shipping, and payment processing.
• Provided By Client (PBC): Documentation or data extracted from a client's system that is provided to auditors or external stakeholders. PBC files should be accurate, complete, and traceable to system-of-record sources for audit purposes.
• Metadata Tagging: The practice of attaching structured information to data or process events to provide context, support traceability, and enable process mining or automated reporting. Metadata may include fields like process step, timestamp, status, or user.
• Process Mining: A technique that uses system-generated logs to reconstruct and analyze actual process flows within an organization, identifying bottlenecks, deviations, and optimization opportunities.
• Governance Dashboard: A reporting interface that provides visibility into automated processes, displaying key metrics such as process execution status, errors, user activity, and compliance with controls.
• Audit Trail: A chronological record of system activities, changes, and transactions that provides a traceable path for auditing purposes. Audit trails are essential for compliance, accountability, and forensic analysis.
• Exception Handling: The process of managing errors or unexpected events in automated workflows, ensuring that the system can recover gracefully or alert users to take corrective action.
• Business Intelligence (BI): Technologies and practices for collecting, analyzing, and presenting business data to support decision-making. BI tools often include dashboards, reporting systems, and data visualization capabilities.

FAQs

I am having trouble connecting to the remote labs.

If you cannot connect to the labs please change your UW id password on myUW and try logging into the remote labs. This step will solve most problems.

The software is asking for a license.

Log out of the remote labs, close the browser. Then log back into the remote labs and wait 2 minutes to allow the software license script to run. This step will solve most problems.

The remote labs are running slowly.

Log out of the remote labs, close the browser. Then log back into the remote labs. This step will solve most problems.

My U drive isn't showing up when I log onto the remote labs.

Log out of the remote labs, close the browser. Then log back into the remote labs. If this doesn't work please change your UW id password on myUW and try logging into the remote labs. This step will solve most problems.

How do I access all of the files by cloning the repository?

To clone the repository navigate to your U-drive and click the filepath, type cmd and a command window will open. To clone the repository type: git clone https://github.com/ashercurtis/datasets.io and press enter. This will start the download process from GitHub to the U-drive on the remote labs.

How do I update files in my datasets.io folder?.

To update the files used in the labs, navigate to the datasets.io folder, click on the filepath and type cmd> command window will open. To check for and download any updates, type git pull and press enter.

My git pull request is giving me errors.

Make sure you are running the command in the correct folder which is U:\datasets.io. If a specific file is logged in the error message, rename that file e.g., dataset1.csv to dataset1-mine.csv, and retry the git pull command. If this still isn't working check in with me, we can re-clone the repository. To reclone, rename the folder you are having trouble with and in the U: use cmd and type: git clone https://github.com/ashercurtis/datasets.io and press enter.

I am having difficulty accessing the remote labs from off-campus.

To access the remote labs from off-campus you typically need to install the Husky OnNet Virtual Private Network (VPN) see here for the installation instructions: UW IT Husky OnNet.

I am having difficulty with the Husky OnNet Virtual Private Network (VPN)

UW IT maintains the OnNet VPN, their troubleshooting page can be found here: UW IT Husky OnNet Troubleshooting.