Five AI Capabilities for CLM You Need to Know
- Last Updated: Jan 02, 2026
- 15 min read
- Sirion
We’re at a place in time where digital transformation drives business strategies. Failing to embrace the full potential of clm technology can leave your enterprise vulnerable to inefficiencies, missed opportunities, and increasing competition.
One of the pivotal technologies emerging at the forefront is AI-powered Contract Lifecycle Management (CLM). And it’s not just hype or a passing phase.
By neglecting to integrate AI capabilities into your CLM and operational framework, your organization risks grappling with outdated contract management processes that are slow, error-prone, and lack predictive insights. This directly impacts your bottom line, client relationships, and competitive positioning.
That said, there is a clear, strong business case when investing in CLM to onboard an AI-enabled solution.
Below are five concrete ways that an AI-backed CLM delivers improved contract value and why you shouldn’t delay in implementing a solution.
1. AI Capabilities Streamline Initial Review of Third-Party Paper
How it Works: An AI engine can be trained to recognize language in a proposed contract for clauses on a specific topic, such as indemnification, automatic renewal, and warranty. With the proper classification, which users can confirm, the solution can then go on to analyze how closely each proposed clause aligns with your organization’s preferred position. The output is a short list of proposed clauses that aren’t acceptable or aren’t yet familiar to your organization. Your legal team can then review these “outliers.”
What Happens Without AI: For situations where it is best to use third-party paper, initial intake requires a legal professional to manually review the entire proposed contract and manually evaluate each clause based on your preferred language. This is generally time-consuming and a common bottleneck in the contract negotiation process.
Why it’s Important: It’s not practical for an enterprise – even a large one – to attempt to come up with standards for the wide variety of goods and services they buy occasionally.
For example, real estate agreements have clauses on leasehold improvements; SaaS agreements outline solution performance minimums, and statements of work cover late fees, the use of subcontractors, and how fees are calculated.
Consequently, it’s common for an organization to have a large volume of their contracts on third-party paper. Because third-party paper is someone else’s preferred set of terms and conditions, they carry significantly more risk. Moreover, long, complex agreements are challenging to get through, and it’s easy for even the best legal team to miss things.
The Bottom Line: AI capabilities automate the third-party contract intake process, greatly reducing the cost of labor but, perhaps more importantly, reliably identifying problematic clauses that put your organization at risk. It reduces the task of intake from full document review to a short list of issues.
2. AI Assesses the Risk of Your Existing Contracts
How it Works: AI can be trained to review your existing body of executed contracts for unfavorable and non-conforming clauses. You must first establish what your preferred terms are for various contract types, but then the AI can systematically review all of your agreements and score each for risk level. It can net things down concisely, creating a hot list of contracts that are most troubling to your organization.
What Happens Without AI: Unfortunately, most organizations find any sort of existing contract review to be far too costly and time-consuming to attempt. It’s hard enough for most to maintain adequate staff for new work. This means that the organization will review contracts as they come up for renewal, leaving themselves potentially highly exposed in the meantime.
Why it’s Important: Organizational culture plays a big role here. Some organizations maintain good control over their contracts, enforcing legal review of all agreements before they are signed and tightly controlling any deviations from preferred positions. Others, however, are not so careful.
For example, some organizations permit, explicitly or implicitly, operational teams to sign their own agreements. When this happens, individuals without proper legal training may inadvertently bind the organization to highly unfavorable terms and conditions that can result in large expenditures, unwanted liability, or automatic renewals with little warning.
Organizations usually realize there is a problem when a contract goes bad, and they realize there are probably more problem agreements in the pipeline.
The Bottom Line: AI makes it possible and practical to back through your existing body of contracts to identify problem agreements before they cost the organization.
3. AI Matches Information From Other Systems to Your Contracts
How it Works: In terms of the business relationship, executing a contract is just the beginning of work, invoicing, payment, and assessment. Organizations support operational work in other systems, e.g., creating purchase orders and paying invoices in an ERP suite and tracking progress against milestones in a project management solution.
AI pulls data from these other applications and processes it against contract terms and conditions, so your organization knows exactly where you stand against contract commitments.
What Happens Without AI: Most organizations attempt to manually match up operational data with contract terms and conditions for a handful of top relationships. Human beings are great at figuring out that a particular invoice with one address and one version of the company name matches up to a specific contract with a totally different address and slightly different company name.
Some organizations try to put everything in a single ERP system so that all records are natively connected. This approach rarely works unless you are a small organization with a simple business.
Occasionally, large companies implement a master data management (MDM) solution to match up data between systems. MDM for suppliers and partners is notoriously costly and too rigid to deliver a return on investment.
Why it’s Important: Despite the effort that goes into negotiating contracts, organizations rarely know how they and their partners perform against them. They don’t know if major milestone dates are met. They don’t know if a supplier is billing them in accordance with pricing schedules. They don’t know if they are getting important discounts and benefits.
The Bottom Line: AI-enabled CLM tracks the performance of your contract to make sure that you’re realizing the value promised in your contracts.
4. AI Provides Contract Analytics for New Requirements
How it Works: When your business requirements or regulatory environment changes, AI gives you the means to assess which existing contracts you need to renegotiate. It does this by searching, at scale, through existing contracts. AI is key because the relevant clauses will appear in different ways and in different sections of your various contracts. AI identifies matches and verifies the match with your users.
For example, if you have a new requirement to report on the number of contracts with payment terms shorter than 45 days, AI capabilities give the ability to quickly create suitable analytics on how many and which contracts are a problem.
Likewise, when your organization needs to incorporate language in certain contracts for compliance with a new regulation, AI can be trained to rapidly identify which contracts you must update.
What Happens Without AI: Without AI, ad hoc analytics are done entirely manually. As a result, organizations do this only when absolutely necessary. Facing a mass change to your existing body of contracts is a common trigger for getting funding for a CLM solution. For example, listen to this case study with BNY Mellon.
Why it’s Important: Some industry dynamics make mass contract changes to existing agreements common. These include industries that operate in highly regulated environments, as well as industries that have a lot of mergers and acquisitions.
The Bottom Line: AI provides an efficient means to tackle large-scale, ad hoc reporting on existing contracts.
5. Generative AI Suggests Text to Speed up Contract Drafting
How it Works: After you’ve used AI-enabled CLM for a while, your solution builds up knowledge of what works in negotiation and what doesn’t.
For example, if 60% of the time your prospective partner rejects your preferred indemnification clause, AI can recommend that you change your proposed language to the clause that most accept. Likewise, generative AI can propose new legal language that it learns in the third-party paper to meet your standards.
What Happens Without AI: Individual professionals build up negotiation knowledge over time. It’s part of what makes an individual valuable.
Why it’s Important: Relying strictly on individuals only works if that individual is always available and remembers everything. In larger organizations, politics, pride, and practicality get in the way of knowledge sharing. The result is often a significant loss of efficiency as well as money left on the table.
The Bottom Line: Great contracts deliver a competitive advantage. Taking advantage of lessons learned from your contract negotiations will make your organization more efficient and effective.
Why AI Contract Management Capabilities are Imperative for Your Enterprise
Organizations considering an investment in CLM should select a solution with advanced AI capabilities. Traditional systems without AI, at best, provide a central repository, basic reporting, and basic workflow for a few contract types.
However, using AI in enterprise software frees organizations from static, fixed data models. AI consumes text like humans do, tolerating challenges like clause order differences, matching related documents despite variations in names and spelling, and extracting key data such as milestone dates and discount levels.
If you are going to go through the trouble of implementing a CLM solution – don’t waste your time on simple contract repositories and outdated technology. Select a CLM that will deliver strategic value in terms of efficiency at scale, risk management, and competitive advantage.
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Debbie Wilson has delivered market-driven insights, strategic vision, and expert guidance to leading enterprises around the world for more than three decades. As Gartner’s lead analyst covering procurement and CLM technologies, and later as group leader for procurement, finance, and ERP, Debbie became widely recognized as one of the industry’s most respected thought leaders in procurement technology innovation, adoption best practices, vendor selection, and automation strategy. Her mission is to share her knowledge, passion, and experience to help enterprises identify and deploy the right CLM solutions to transform their contracting.
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