Key Takeaway: Explore the key frameworks and real-world lessons from the Association of Corporate Counsel (ACC) Legal Ops Conference session, How AI Delivers Value at Each Stage of CLM Maturity. These insights offer legal operations and contracts management professionals a practical roadmap for understanding their current stage, assessing their readiness, and taking the right next steps.

AI is reshaping contract lifecycle management (CLM). The return on AI investment depends on CLM maturity and readiness across five core dimensions. Deploy AI too early, and it amplifies chaos. Deploy it on the right foundation using a disciplined approach and proven best practices, and it becomes a force multiplier. This accelerates the move from reactive contracting to an intelligent, self-improving legal function.

Part One: The CLM Maturity Framework

CLM maturity is best understood as a progression, moving from purely reactive contracting toward a proactive, intelligence-driven function.  

Deploying AI at level one, when processes are ad hoc and contracts are scattered, does not accelerate maturity; it automates dysfunction. Organizations must build the proper foundation first. 

Once teams establish a clause library and playbooks, AI redlining and risk scoring reduce cycle time and improve consistency. From level three onward, the ROI on AI investment compounds with each additional layer of process sophistication.

Part Two: Prepare for Adoption

True AI readiness spans five dimensions, each of which directly impacts a successful AI deployment.

People Readiness

The human element is consistently the hardest to change. Key questions to assess people readiness include:

• Do users understand what generative AI does and does not do well?
• Can legal and business teams critically evaluate AI redlines, or will they accept outputs unquestioningly?
• Is there a genuine appetite for changing workflows, or entrenched resistance?
• Who owns the output?
• What training gaps exist between current skills and proficient use of the tool?

Process Readiness

AI cannot compensate for a broken or undocumented process. Before piloting any AI tool, organizations should ensure that they have: 

• A documented contracts lifecycle process
• Agreed contracts types and value thresholds
• Standardized negotiation playbooks
• Clear visibility into bottlenecks and failure points

Data Readiness

AI is only as good as the data it draws upon. A solid data foundation requires:

• A single, accessible contracts repository in place.
• Legacy contracts teams tag, categorize, and enrich with metadata.
• Documented and updated preferred positions and acceptable fallback terms.
• A screening process for sensitive terms, personal data, or third-party confidentiality obligations before ingestion.

Governance Readiness

AI introduces new legal and operational risks that organizations must actively manage, such as:

• An AI use policy that addresses the appropriate use of different AI types in legal workflows.
• A mandatory human review step before any AI output is shared externally.
• A completed AI risk assessment covering the provider's data handling, model training, and security posture.
• Awareness of industry-specific regulations (e.g., HIPAA, DFARS, GDPR) that govern permissible use of contracts data.

Technology Readiness

Even well-designed AI tools fail if the technology stack cannot support them. Gain clarity by asking: 

• Does an existing CLM system need to connect to the AI tool? How complex is that integration?
• Has IT security reviewed and approved the provider?
• Can the tool be provisioned through existing identity infrastructure (e.g., single sign-on (SSO) and access management)?

A “not ready” score in any single dimension is a blocker. A “partial” score typically requires active remediation before piloting. Reaching “mostly ready” or “ready” across all five dimensions is the threshold for a responsible AI pilot launch.

Part Three: A Practical Framework for Getting Started

Step One: Run the Readiness Assessment 

Conduct a structured readiness assessment across all five dimensions with key stakeholders. Use surveys across user groups, observation of current-state task completion, and interviews with leads from legal ops, IT, and business functions. The goal is an honest picture.

Step Two: Remediate Blockers

Address any ‘not ready’ scores before proceeding. Common remediation actions include establishing or updating negotiation playbooks, centralizing and cleansing contracts data, and drafting an AI governance and use policy. Skipping this step is the most common cause of AI pilot failures.

Step Three: Launch a Scoped Pilot

Start with a minimum viable product (MVP) scope and a willing team. Measure three things in the launch:

• Accuracy: How often is the AI output correct or useful?
• Cycle Time: Is the process measurably faster?
• User Satisfaction: Are users actually adopting the tool in their daily workflow?

Conclusion

AI in CLM is a capability that grows in value as organizational maturity grows alongside it. 

The organizations that succeed are the ones that invest equally in people, process, data, governance, and technology, and who treat AI adoption as a change management challenge as much as a technical one.

Learn more about Epiq Contracts Solutions.


Jessica Cook, Senior Director, Contracts Solutions
Jessica Cook is a lawyer with more than 15 years of consulting experience and over 12 years of delivery leadership in legal technology, AI, and legal operations professional services. Jessica oversees CLM implementation teams for enterprise digital transformations, ensuring adoption success and helping clients scale for growth.


Dundi Thompson, Project Manager, Legal Operations, Flowserve
Dundi Thompson is a senior project manager specializing in legal operations and enterprise transformation. With extensive experience leading complex, high-impact initiatives, she drives efficiency through technology, process improvement, and scalable solutions. At Flowserve, she has led major CLM and digital transformation projects, consistently delivering on time and under budget.