Why study Advisory Services?
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
How this course fits into the MPAcc program:
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).
Key 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.
- By proposing and defending the use of emerging technology in the capture and dissemination of ESG data.
- 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 robotics process automation solution to a business problem
- By researching and evaluating processes fit for automation.
- By practicing and implementing automation using RPA software.
- By using trial and error to find innovative RPA solutions to a business problem.
Prerequisites, required texts, materials, and software:
- Students are required to be enrolled in the MPAcc program. There are no other formal prerequisites for this course, however, students are encouraged to complete the Foster Microsoft Excel for Business online (or equivalent) course prior to starting, or during, the Autumn quarter.
- There is no required text for this course. If you wish to explore a topic in greater depth, please ask your instructor for recommendations.
- Materials outlining the required deliverables, templates, sample code/solutions, background readings and/or cases are accessed via Canvas.
- Students will be using specialized software in this course all of which will be made available on the Foster remote labs, an open-source or cloud-based setting, or from a subscription.
Required Deliverables:
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:
| Assessment | Assessment Type | Deliverables | Due Date | Grade Percentage |
|---|---|---|---|---|
| Professionalism | Individual | Polls; Verbal and Written Communication | All Quarter | 20% |
| Professionalism: Skylar RPA Case | Individual | Short Video Recording | April 28th | part of professionalism |
| RPA Individual Submission | Individual | Software; Short Video Recording | Class 13 | 30% |
| Intelligent Automation Team Challenge | Team | Software; Short Presentation | Classes 17 and 18 | 35% |
| Common Final Project | Team | Written Material; Presentation | Classes 19 and 20 | 15% |
Professionalism: An individual assessment of student professionalism throughout the quarter. Students are expected to maintain a professional approach to work and approach all classes as professional engagements. Part of this grade is determined via deliverables relating to pollEverywhere engagement, written responses and other deliverables to cases and verbal communication in class.
Professionalism: Skylar RPA Case: An individual assessment of student professionalism relating to the RPA introductory case worked on during classes 3 to 7. The responses to this case will be used in part to determine professionalism grades, and in part to provide practice and feedback for the individual RPA challenge. The required deliverable is a video relating to the Pure Reformed Oils Case (a.k.a. Skylar RPA Case).
RPA Individual Submission: An individual project that includes building an RPA software solution that is explained, motivated and demonstrated in a recorded video presentation. Each individual will be asked to develop a desktop-level automation robot of a common repetitive business task. The bot is to be built using either UIPath or the Microsoft Power Apps. The RPA solution is expected to work on a single desktop, and any issues related to the transfer of the bot need to be identified and discussed. The automation solution needs to be motivated in terms of how many human work hours it can save (efficiency) and the value associated with reductions in human errors (effectiveness). Details and further guidance will be provided on Canvas.
Intelligent Automation Team Challenge: A team project that focuses on an enterprise level automation robot aimed at solving a reporting problem. The bot can be demonstrated in a proof-of-concept state which includes being presented on a single desktop, not relying upon live data inputs, not being 100% completed at the enterprise-level, and producing rudimentary dashboards or other outputs. The solution can be leveraged and used in the Common Final Project, where a technology assisted ESG disclosure task is discussed. Teams should consider how the solution can be considered as "Intelligent Automation" by applying Generative Artificial Intelligence and/or using Agentic AI / teams of AI Agents. The presentation of the automation solution will be focused on the technical goals (including clearly articulating the process using a process diagram) and limitations of the bot. Teams are recommended to discuss their solution from their instructor as early as possible for feedback on feasibility and expectations. Details and further guidance will be provided on Canvas.
Common Final Project: A team project that synthesizes material from three MPAcc Spring Quarter Classes. Teams will write a report and present findings relating to the use of technology to solve an ESG-related problem. A software deliverable is not required for this project but can be heavily related to the team's solution to the Intelligent Automation Team Challenge if appropriate, or can be discussed at a purely hypothetical level. Details and further guidance is available at the Common Spring Project page.
Administrative Matters:
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Instructor: |
Asher Curtis, PhD. Herbert O. Whitten Endowed Associate Professor of Accounting. |
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Class Times: |
Tuesdays and Thursdays at 1:30PM to 3:20PM. |
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Location: |
PACCAR 392. |
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Office Hours: |
In PACCAR 414: Monday, Wednesday, Friday by appointment; Tuesdays and Thursdays before class or after class until 4pm; other times by appointment; Zoom by appointment. |
Religious Accommodations:
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.
MPAcc Policies and Further Questions:
Questions about the content of this course should first be directed to the instructor. Please see the MPAcc orientation materials for important administrative details regarding the program that apply to all courses in the MPAcc program and the UW https://registrar.washington.edu/staffandfaculty/syllabi-guidelines/ for important university policy and guidelines. If you have any additional questions, please contact the MPAcc Office (mpacc@uw.edu).
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.
Tentative Course Schedule