Case Management, Practice Operations, and Client Services

Course Objectives

The objectives of this course are to;

1. Streamline administrative workflows, billing, and scheduling using AI.
2. Deploy AI-powered chatbots and virtual assistants for client support.
3. Integrate AI into knowledge management for better organizational learning and practice efficiency.

Learning Outcomes

By the end of this course, a learner will be able to;

1. Manage cases, schedules, and client communications efficiently using AI assistants.
2. Design and deploy AI chatbots to enhance client access to legal information.
3. Integrate AI into knowledge management workflows to improve firm efficiency and decision-making.
4. Evaluate ethical and operational considerations in deploying AI for client services.

Outline of Content

1. Introduction to AI in Law Practice Management
   • Overview of AI applications in case management and firm operations.
   • Benefits: time-saving, cost reduction, error minimization, client satisfaction improvement.
   • Challenges: data privacy, integration with existing systems, maintaining ethical standards.
2. Tools for Case and Practice Management
   • LitiGate: AI-driven case tracking, litigation analytics, and automated document filing.
   • Legl: Automation of billing, invoicing, payment tracking, and matter management.
   • Harvey AI: AI legal assistant for drafting, research, and workflow optimization.
   • Demonstration of tools, dashboards, alerts, and workflow automation features.
3. AI in Administrative Operations
   • Scheduling and calendar management: AI-assisted reminders and prioritization.
   • Billing and time tracking: automated logging, client reporting, and predictive invoicing.
   • Document management: auto-classification, tagging, and retrieval using NLP.
4. Client-Facing AI: Chatbots and Virtual Assistants
   • Design and deployment of AI chatbots for client FAQs, intake, and triage.
   • Improving client experience with personalized interactions.
   • Ethical considerations: transparency, confidentiality, and bias in AI responses.
5. Knowledge Management (KM) with AI
   • Organizing, storing, and retrieving firm knowledge efficiently.
   • Using AI to identify expertise within the firm.
   • Leveraging AI to support precedent search, case research, and internal training.
6. Lab / Practical Exercise

• • Hands-on project: Build a simple AI-powered legal FAQ chatbot.
  • Tasks:
    • Define a set of typical client queries and answers.
    • Implement a chatbot using no-code or low-code AI tools.
    • Test the chatbot with sample queries.
    • Discuss limitations, improvements, and ethical implications.

Reference List

1. Susskind, R. & Susskind, D. (2015). The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford University Press.
2. Susskind, R. (2019). Tomorrow’s Lawyers: An Introduction to Your Future (3rd Ed.). Oxford University Press.
3. Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press.
4. Mak, V., Tjong Tjin Tai, E., & Berlee, A. (2020). Research Handbook in Data Science and Law. Edward Elgar Publishing.
5. Paul Mukiibi and Daniel R. Ruhweza. Digital Transformation of Court Processes and Effectiveness of Delivery of Justice in Uganda. The Uganda Living Law Journal [ULLJ], January 2024, Volume 11 No. 1, pp. 23-53. ISSN 1729-4672.
6. Paul Mukiibi. Virtual Courts/E-Justice and the Right to a Fair Hearing during and After Covid-19 in Uganda. The Uganda Living Law Journal [ULLJ], 2021, Volume 10 No. 1, Pages 28-60. ISSN 1729-4672.
7. Paul Mukiibi. Legal Issues on Admissibility of Electronic Signatures under the Electronic Signatures Act in Uganda’s Civil Proceedings. African Journal of Legal Issues in Technology and Innovation, Volume 1, Number 1, 2023. Pages 67-93.
8.  

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