Category: Software and applications

Please see uploads for instructions

The source for the text in your research paper is in the file SC_WD_2-3.docx, located in the Week 3 Module. The goal is to organize the notes in the text file in the Data Files, rewording as necessary, so you can create a research paper. Using the concepts and techniques presented in class and the organized notes from the Data File, you can create and format a research paper according to the MLA documentation style (be sure to write an appropriate conclusion). While creating the paper, be sure to do the following:

1. Modify the Normal style to the 12-point Times New Roman font.

2. Adjust line spacing to double.

3. Remove the space below (after) paragraphs.

4. Update the Normal style to reflect the adjusted line and paragraph spacing.

5. Insert an MLA-style header and page numbers in the header.

6. Type the name and course information at the left margin. Use your name and course information. Center and type the title.

7. Set a first-line indent to one-half inch for paragraphs in the body of the research paper.

8. Type the body of the research paper from the notes. Change the bibliography style to MLA. As you insert citations, enter their source information. Edit the citations to conform to the MLA documentation style.

9. At the end of the research paper text, press ENTER and then insert a page break so that the Works Cited page begins on a new page.

****This is a continuance of a project. I have it attached for reference. You’re simply building off what I’ve already established****

The purpose of this assignment is to help you practice the following skills that are essential to your success in this course:

• Engage with and analyze AI in a real-world scenario to understand the intricacies of integrating AI solutions, with a specific focus on data requirements and user interaction.

Preamble:

This milestone builds on your ongoing Course Project by shifting focus from designing an AI system to preparing it for responsible and effective deployment. Youll define the types of data your system requires, explore how end-users will interact with it, and evaluate the role of Natural Language Processing (NLP)if applicable. Youll also identify organizational, ethical, and governance challenges that may arise, and begin planning for successful change management and adoption.

Task:

In 23 pages, you will build a strategic implementation plan for your AI solution, focusing on three core components:

• Data Governance and Infrastructure
• User Adoption and Organizational Integration
• NLP Evaluation and Risk Assessment

You are writing as an AI leader presenting to a cross-functional leadership team. Your audience is not technicalyour goal is to highlight the business implications and strategic decisions behind your design choices. Add this as a new section in your ongoing course project document.

Instructions:

Your submission must include the following clearly labeled sections:

1. Data Strategy and Governance

• What types of data will your AI system require?
• Where will this data come frominternal sources, external partnerships, or third-party providers?
• What challenges might you face in data collection, cleaning, or access?
• What are the privacy, security, and compliance considerations (e.g., GDPR, HIPAA)?
• Will you need ongoing data pipelines, human oversight, or bias auditing systems?

1. User Interaction and Organizational Integration

• What will the user experience look like (interface, workflow, communication methods)?
• Who are the primary users, and what training or onboarding will they need?
• What organizational barriers could hinder adoption (e.g., cultural resistance, legacy systems)?
• What change management strategies will support adoption?
• How does this system fit into or reshape current workflows?

1. NLP-Specific Evaluation (If Applicable)

• If your solution uses NLP, what tasks will it perform (e.g., summarization, sentiment analysis)?
• What accuracy thresholds or confidence scores are acceptable for your use case?
• Will the model need to handle multilingual inputs or cultural sensitivity?
• How will you monitor and respond to biased or inappropriate outputs?
• If NLP is not used, briefly explain why its not a strategic fit for your system.

1. Reflection (Optional but Encouraged)

• What is the biggest leadership challenge you anticipate in implementing this system?
• How will you know if your AI solution is successful? What are your key performance indicators (KPIs) or adoption metrics?