A Deep Dive Into AI-Enhanced Commercial and Contract Management
TL;DR: AI enhances—but doesn’t replace—CLM platforms and human expertise. It improves commercial and contract management and productivity by streamlining processes, offers or contract creation, smart storage, version control, redlining capabilities, and much more, but its full potential is unlocked only when paired with a well-structured CLM system and skilled contract professionals.
Introduction
I overheard a discussion where a manager suggested a new way to reduce costs: “Let’s bring in AI to manage offers and contracts so we can cut down on commercial and contract management staff .” On the surface, it sounds logical—AI is transforming processes across industries, and many tasks in commercial and contract management (and other processes) seem ripe for automation.
However, for AI to truly work in commercial and contract management, there are several foundational prerequisites: it needs structured and organized data.
In short, contract lifecycle management (CLM) platforms offer all that. Here is a list of CLM solutions on the Gartner website.
Without the backbone of a robust Contract Lifecycle Management (CLM) system, introducing AI is like trying to plug a Tesla self-drive autopilot into a horse-cart. A CLM system organizes offers and contracts in a centralized repository (‘single source of truth’), establishes workflows, and automates approvals. This is a basis needed for AI that can then amplify this organized structure, transforming well-structured offer and contract data into strategic insights. When used together, CLM and AI and skilled professionals who know how to work with both create a powerful combination that can significantly improve top- (revenues) and bottom-line (profit) results.
In this article, I explore why AI can’t fully replace CLM systems and people, but instead, enhances them. We’ll break down the key ways AI is transforming traditional CLM processes—such as structuring unstructured data, mapping contract relationships, managing version control, and handling redlines—and why organizations looking to streamline their contract processes should adopt an AI-enhanced CLM approach. And yes, I have my own OpenAI GPT helping me structure my input and thoughts for this article.
Note: this article is written from the perspective of a sell-side contract but can be easily translated also to buy-side and other contracts.
What Are CLM Systems?
Contract Lifecycle Management (CLM) systems are designed to simplify and automate the entire contract lifecycle, from creation through execution and renewal or termination. These systems centralize data, create offers and contracts, and organize the complex workflows associated with offers and contracts, ensuring consistency and compliance across an organization.
Benefits of CLM for Complex Contracts in High-Tech Industries
In high-tech, especially if they are highly regulated industries like aerospace and defense, CLM systems are indispensable. These sectors often deal with complex, high-stakes agreements—such as multi-year government contracts, vendor partnerships, and comprehensive regulatory requirements—that demand a high degree of precision and compliance. CLM systems standardize and automate critical processes, minimizing the risk of non-compliance and contractual errors. They ensure that essential terms, deliverables, and regulatory obligations are tracked and met, an absolute necessity in industries where contract missteps can result in severe penalties or reputational damage. Additionally, by centralizing contracts, CLM systems enable teams to quickly access and cross-reference specific terms, amendments, or compliance requirements, making them invaluable tools for managing vast portfolios across intricate projects.
In addition, industries like aerospace and defense also face unique security challenges when it comes to CLM and AI implementation. Due to stringent security restrictions, many organizations in these sectors are unable or at least reluctant to use cloud-based CLM or AI systems, which are the norm in many other industries. Instead, they must rely on on-premise solutions to ensure data protection and meet regulatory compliance standards. While this approach secures sensitive data, it limits access to the latest cloud-based AI advancements and may increase implementation complexity.
While CLM systems streamline these tasks, traditional platforms still face limitations, particularly in handling unstructured data and managing revisions. This is where on-premise AI-enhanced CLM systems with restricted access to the cloud for best-practice learning can help, providing capabilities that unlock new levels of efficiency and insight. This will be another article I intend to write.
Core Functions of a CLM System
CLM systems provide essential functions across the contract lifecycle including but not limited to:
- Offer/Contract Drafting: Simplifying the creation of offers/contracts using templates and standard clauses.
- Negotiation: Enabling collaborative edits and transparent workflows between parties.
- Approval Workflows: Routing contracts to the appropriate stakeholders for approval.
- Execution and Storage: Centralizing offers, contracts, and related documents (mail, etc.) with secure digital signatures in one repository.
- Compliance Management: Monitoring contract obligations, deadlines, and regulatory compliance (export control).
- Amendments, renewals: Tracking amendments, contract renewal dates, and prompting renegotiation when needed.
What Happens Without CLM Structure?
Attempting to add AI to a chaotic, unstructured contract environment is a recipe for frustration. Imagine trying to extract insights from contracts that exist in uncontrolled versions with no clear metadata, fragmented across various databases, and filled with inconsistent, even unreadable (non-OCR-ed) text. Many of these contracts might contain outdated clauses, poor wording, and critical mistakes that slipped through previous reviews. Without a CLM foundation to impose structure, AI is forced to work with a jumble of parent-child relationships that are mismanaged or unclear, creating more noise than value. Instead of delivering clarity, AI in this context would surface a cascade of errors and inconsistencies, potentially amplifying mistakes rather than resolving them. This scenario highlights why establishing a clean, organized CLM structure is a non-negotiable first step—AI is powerful, but it’s not a miracle fix for disorganized data.
How AI Enhances CLM Systems
So AI needs structured data which CLM systems can provide. Rather than replacing CLM systems, AI enhances their functionality by transforming offer and contract data into valuable insights and enabling smarter processes. Let’s examine key areas where AI strengthens CLM capabilities.
1. Structuring Unstructured Data: Solving the Storage Problem
Offers, contracts, and associated documents are often stored across multiple platforms—(cloud and on-premise) systems, shared drives, email attachments—and in various unstructured formats like PDFs, scanned images, or text files. This fragmentation can make it difficult for organizations to maintain a comprehensive view of their contract data.
AI’s Role in Structuring Data AI tools, particularly those with Optical Character Recognition (OCR), can extract and digitize text from unstructured documents, making it accessible and searchable. AI also tags documents with relevant metadata (e.g., party names, dates, terms), and indexes documents across different storage locations, creating a searchable, organized repository of contract data.
Practical Benefits
- Fast Retrieval: AI’s search capabilities allow users to quickly locate contracts by keyword or metadata, saving time and improving response rates.
- Improved Compliance: By organizing and tagging data, AI helps organizations monitor critical obligations and deadlines, reducing the risk of non-compliance.
- Requirement: you need your contract experts here to assist AI in how to analyse and structure the data.
2. Contract Mapping: Understanding Relationships Across Documents
Contracts are often accompanied by a variety of related documents—such as amendments, addendums, purchase orders, and invoices—which can be hard to track when scattered across departments or systems.
How AI Creates Contract Maps AI can analyze contract content and metadata to automatically link related documents. For instance, it might link a purchase order to a framework agreement or tie an amendment to its original contract. Some AI-powered CLM platforms also feature visual dashboards that display these relationships, giving users a clear view of the full contract ecosystem.
Applications
- Legal Audits: Contract maps ensure all related documents are accounted for, simplifying audits and compliance reviews.
- Negotiation Context: Mapping relationships between documents gives negotiators comprehensive context, enabling more informed decision-making.
- Requirement: you need your contract experts here to assist AI in how to map the contracts.
3. Version Control: Solving the Problem of Multiple Versions
Version control is a common pain point in contract management, especially when contracts undergo multiple revisions from various stakeholders. Ensuring that all parties work with the latest version can be a logistical challenge.
How AI Improves Version Control AI can automatically track contract versions, labeling each new version with a unique identifier and logging changes as they occur. Additionally, AI’s intelligent change detection compares different versions and highlights modifications, so nothing critical is missed. By consolidating these updates in a central “source of truth,” AI ensures everyone is working from the latest version.
Benefits
- Reduced Errors: Automated version control minimizes the risk of acting on outdated contract terms.
- Faster Review Cycles: AI-driven comparison tools speed up review processes, allowing teams to focus on substantive changes.
4. Handling Redlines: Streamlining Contract Negotiations
Redlining—the process of reviewing and marking up changes in contracts—is critical but time-intensive. Managing redlines effectively is essential, particularly during high-stakes negotiations.
AI’s Role in Redlining AI can detect and highlight redlined changes automatically, identifying additions, deletions, and modifications with precision. AI also flags clauses that may pose risks based on company standards or industry benchmarks and suggests alternative language from a library of approved clauses, ensuring consistency. Some AI tools even offer predictive insights, analyzing redlined changes to forecast how they might affect negotiations, helping contract managers prioritize specific clauses or anticipate objections.
Practical Applications
- Faster Negotiations: AI drastically reduces the time required to review and compare redlined versions, accelerating the negotiation process.
- Consistency in Language: By recommending standard clauses, AI helps ensure that contract language aligns with organizational standards, reducing risks during negotiations.
5. AI-Enhanced Templates in a CLM
For most organizations, drafting an offer or contract clause from scratch is (should be) now more of an exception than a rule. Instead, companies rely on templates with pre-approved clauses in their CLMs to maintain efficiency and reduce risk. By standardizing language, organizations ensure compliance with legal standards and internal policies, minimizing the need for frequent legal reviews. However, while templates streamline the process, they can also be limiting. This is where AI can add significant value.
AI’s Role in Templating: AI analyzes patterns in existing clauses and suggests context-specific modifications, allowing for a level of customization that template-based systems alone can’t provide. In this way, templates serve as a foundation, while AI adds the flexibility to adapt to unique contract requirements.
Conclusion: AI-Enhanced CLM Is the Future for Commercial and Contract Managers
In commercial and contract management AI does not add much value without a CLM and skilled users. AI is not here to replace CLM systems and contract professionals but to elevate them. By automating repetitive tasks, providing actionable insights, and streamlining contract workflows, AI-enhanced CLM systems allow organizations to achieve smoother operations, improved compliance, and greater efficiency. For companies in high-tech, complex industries like aerospace and defense, where precision and regulatory compliance are where precision and regulatory compliance are paramount, AI-powered CLM is particularly transformative.
The additional challenge of needing on-premise solutions due to security restrictions adds complexity for aerospace and defense organizations, but with the right approach, AI and CLM systems can still provide remarkable improvements in contract management efficiency and effectiveness. A future article will address the unique risks and strategies for managing AI and CLM in on-premise environments within such high-security industries.
As AI capabilities advance, companies that adopt AI-enhanced CLM systems will find themselves better equipped to handle the demands of modern contract management, gaining a competitive edge in efficiency, compliance, and strategic oversight.
Afterword: The Evolving Role of Humans in AI-Enhanced CLM
As CLM systems become more sophisticated and AI continues to automate repetitive tasks, a natural question arises: What role will humans play in AI-enhanced contract management? The combination of CLM and AI is transformative, greatly increasing effectiveness and efficiency in contract workflows. However, rather than making contract managers obsolete, these technologies are redefining and elevating their roles.
AI and CLM systems excel at structuring data, spotting patterns, and automating tasks, but they lack the nuanced judgment, strategic thinking, and relationship-building that only humans can provide. AI might suggest a standard clause or flag a risky term, however, it cannot understand the full business context, navigate complex stakeholder interests, or negotiate nuanced terms in real time. Humans are essential in these areas, bringing levels of insight, creativity, and interpersonal skills that technology alone can’t replicate.
In an AI-enhanced CLM environment, contract managers will increasingly become strategic advisors. Freed from the burden of manual tasks, they can focus on high-level responsibilities—like shaping contract strategies, understanding market trends, and working directly with stakeholders to align contracts with broader business goals. Their role will shift from simply managing processes to guiding decision-making, leveraging AI-generated insights to make informed, proactive choices. In this way, contract managers become not just custodians of compliance but key drivers of value and competitive advantage.
So, while CLM and AI are transformative, they don’t replace the need for human expertise—they amplify it. Contract managers who adapt to these tools will find themselves in a unique position to lead, providing strategic oversight and human insight that maximize the value of AI-enhanced systems. As technology and AI continue to evolve, the role of humans in contract management will remain indispensable, with even greater emphasis on skills like critical thinking, negotiation, and strategic vision.
For those who have reached this far in this article and therefore must be very interested, I highly recommend you to read “Human + Machine – Reimagining work in the age of AI” by Accenture’s Paul Daugherty and Jim Wilson (no affiliation), about how AI is now causing the transformation of all business processes within an organization—whether related to breakthrough innovation, everyday customer service, or personal productivity habits.
The future of contract (lifecycle) management is bright for those ready to embrace this change, and the combination of human expertise with advanced CLM and AI tools promises a more dynamic, effective, and fulfilling role than ever before.
This article is the result of a successful collaboration between me, the author, and my assistant, the GPT model which I created through OpenAI specifically for creating contract management content. Both my AI assistant and I contributed insights and structure to one another and collaborated on refining the article, demonstrating the potential for human-AI partnerships to deliver quality content and informed perspectives.