Upon completion of this intensive course, participants will be able to:

• Develop a comprehensive business case for implementing or upgrading a CLM system, justifying the ROI.
• Define the core functional requirements (e.g., repository, workflow, authoring) for CLM system selection.
• Understand the capabilities and limitations of Artificial Intelligence (AI) in contract review, risk scoring, and data extraction.
• Lead the CLM implementation process, including data migration, template standardization, and user training.
• Design and automate contract workflows and approval processes for speed and compliance.
• Leverage AI tools for post-award monitoring, including identifying contractual obligations and deadline tracking.
• Develop strategies for data governance, quality, and taxonomy to maximize CLM system utility.
• Analyze the ethical and security implications of using AI and cloud-based systems for sensitive contract data.

• Contract Managers and Directors leading digital transformation initiatives
• Chief Procurement and Commercial Officers (CPOs/CCOs)
• IT and Legal Operations Specialists
• Business Analysts and Process Improvement Managers
• Professionals involved in CLM implementation or vendor selection
• In-house Legal Counsel focusing on process efficiency
• Anyone responsible for contract repository and data management

• Workshop on building an ROI model and business case for a CLM system implementation.
• Demonstration and hands-on practice with simulated AI clause review and data extraction tools.
• Group activity mapping a complex contract approval workflow for automation.
• Case studies of successful and challenging CLM system implementations and lessons learned.
• Discussions on data governance, security, and the ethical use of contract data.

Unit 1: Building the Case for Contract Lifecycle Management (CLM)

• The core components of a CLM system: intake, authoring, negotiation, execution, repository, and compliance.
• Calculating the return on investment (ROI) and key financial metrics for a CLM implementation.
• Conducting a current state analysis of contract processes to identify pain points and inefficiencies.
• Structuring the business case to secure executive sponsorship and budget approval.

• Developing a detailed functional and technical requirements checklist for CLM vendor evaluation.
• Key considerations in vendor selection: integration capabilities, scalability, and user interface (UI).
• Strategies for successful data migration, taxonomy design, and template standardization.
• Managing the implementation timeline, user acceptance testing (UAT), and post-go-live support.

Unit 2: Leveraging AI and Machine Learning in Contracting

• Using AI for automated contract review, redlining suggestions, and playbook compliance checking.
• AI-driven risk scoring based on clause deviation from the organizational standard.
• The role of natural language processing (NLP) in understanding and summarizing complex legal text.
• Ethical and accuracy considerations when relying on AI for first-pass contract analysis.

• Utilizing AI for intelligent search and rapid data extraction from legacy contract documents.
• Automated identification and tracking of key obligations, milestones, and expiration dates.
• AI-powered reporting and dashboard generation for real-time risk and performance monitoring.
• Future trends: predictive analytics and using contract data to inform business strategy.

Unit 3: Workflow Automation and Process Design

• Designing and mapping optimized contract workflows for various agreement types (e.g., standard, non-standard).
• Implementing complex approval routing logic based on value, risk score, and required signatories (Delegation of Authority).
• Strategies for integrating the CLM system with ERP, CRM, and financial systems.
• The importance of version control, audit trails, and security protocols within the automated workflow.

• Developing standardized, modular contract templates for maximum automation and compliance.
• Creating and maintaining dynamic negotiation playbooks within the CLM system for consistent risk mitigation.
• Using document automation tools to draft complex schedules and appendices accurately.
• The role of the contract professional in governance of the automated content library.

Unit 4: Data Governance and Future Trends

• Establishing data standards, naming conventions, and metadata requirements for the contract repository.
• Developing a data governance committee to ensure data accuracy and completeness.
• The security implications of cloud-based CLM: data residency, encryption, and access controls.
• Strategies for cleaning and preparing legacy contract data for successful migration.

• Overview of distributed ledger technology (blockchain) and its potential for secure contract execution.
• The concept of "smart contracts" and their implications for future contract management roles.
• Exploring the integration of generative AI for initial contract drafting and summarization.
• Developing a continuous improvement roadmap for technology within the contract function.